# Sensors and Measurement Systems for Comprehensive On-water Performance Analysis of Kayakers: A Review

Souebou Bouro, Antoine Courtay, Guillaume Nicolas, Nicolas Bideau, Mickaël Le Gentil, Olivier Berder

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Souebou Bouro, Antoine Courtay, Guillaume Nicolas, Nicolas Bideau, Mickaël Le Gentil, et al. Sensors and Measurement Systems for Comprehensive On-water Performance Analysis of Kayakers: A Review. IEEE Sensors Journal, 2025, pp.1-1. 10.1109/jsen.2025.3591238. hal-05241448

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# Sensors and Measurement Systems for Comprehensive On-Water Performance Analysis of Kayakers: A Review

Souébou Bouro<sup>[D]</sup>, Member, IEEE, Antoine Courtay<sup>[D]</sup>, Guillaume Nicolas, Nicolas Bideau, Mickaël Le Gentil<sup>[D]</sup>, and Olivier Berder<sup>[D]</sup>, Member, IEEE

**Abstract**—In the field of sports, including kayaking, developing technical skills and optimizing performance are essential. Given the constraints on equipment design (paddles, footrests, seats, and boats), the use of portable on-water measurement systems is crucial for elite kayakers and coaches to study equipment usage and athletes movements in real-world conditions. Among the technologies employed, force sensors and inertial measurement units (IMUs) are commonly used for on the water dynamic and kinematic analysis, respectively. However, finding a portable system for kayaking that allows comprehensive evaluation while being suitable for on-water analysis has proven extremely challenging. Existing systems primarily focus on paddle data collection and analysis, with limited attention to data transmission and the energy efficiency of wireless sensor nodes. Moreover, the body of literature in this area remains relatively fragmented and limited, with contributions dispersed across disciplines including biomechanics, embedded systems, and sports science. This study reviews essential systems for real-time, comprehensive analysis of professional kayakers' performance. It examines both commercial systems and academic research, focusing on dynamic and kinematic measurements, with an emphasis on inertial and force sensors, particularly strain gauges, which play a central role in this emerging trend in kayaking. These technologies enable detailed analysis, paving the way for future developments to enhance kayakers' training programs significantly.

**Index Terms**— Data fusion, energy efficiency, kayak, performance optimization, portable systems, sensor, wireless network.

<figure><img src="image_2.png" /></figure>

## I. INTRODUCTION

CANOEING and kayaking are water-based sports activities that involve propelling, steering, and balancing vessels using paddles. This practice, also known as “paddle sports,” has been gaining popularity, particularly with its inclusion in the Olympic Games.

The improvement of technical skills and the pursuit of optimal performance have become essential in the training of kayakers. However, studies focusing on real-time, on-water

performance analysis [1], [2], [3], while insightful, remain relatively scarce and underexplored compared to research conducted under controlled laboratory conditions using kayak ergometers [4], [5], [6]. The specific challenges presented by the aquatic environment such as water resistance, instability, and signal interference, make it difficult to replicate authentic paddling conditions in a laboratory setting [7]. Moreover, visual observation by coaches alone is insufficient to assess technical execution at a high level, where paddle stroke sequences evolve too rapidly to be captured and interpreted reliably without technological assistance.

In response to these challenges, many researchers have explored the use of wireless measurement systems based on human motion sensors [8], [9], force sensors [6], [10], [11] and inertial measurement units (IMUs) to support coaches and athletes in the evaluation and refinement of paddling technique. These technologies, when deployed effectively in real-time on-water settings, offer valuable insights into equipment usage, body coordination, and force application.

Nonetheless, despite their growing relevance, existing systems still present notable limitations. Most focus predominantly on paddle instrumentation, overlooking critical interactions with other contact points such as the seat

Received 28 May 2025; accepted 9 July 2025. Date of publication 6 August 2025; date of current version 15 September 2025. This work was supported in part by the Agence Nationale de la Recherche (ANR) within the Framework of the PIA EUR DIGISPORT Project under Grant ANR-18-EURE-0022 and in part by the Région Bretagne. The associate editor coordinating the review of this article and approving it for publication was Dr. Yang Yang. (Corresponding author: Souébou Bouro.)

Souébou Bouro, Antoine Courtay, Mickaël Le Gentil, and Olivier Berder are with CNRS, IRISA, University of Rennes, 35200 Rennes, France (e-mail: souebou.bouro@irisa.fr; antoine.courtay@irisa.fr; mickael.le-gentil@irisa.fr; olivier.berder@irisa.fr).

Guillaume Nicolas and Nicolas Bideau are with the Mouvement Sport Santé (M2S) Laboratory, University of Rennes 2, 35000 Rennes, France (e-mail: guillaume.nicolas@univ-rennes2.fr; nicolas.bideau@univ-rennes2.fr).

Digital Object Identifier 10.1109/JSEN.2025.3591238
and footrest. Others fail to adequately address the challenges of wireless communication and energy consumption in distributed sensor networks. Moreover, a frequent lack of integration among system components, including sensors, data fusion algorithms, and feedback mechanisms, limits the ability of these tools to provide a truly comprehensive analysis of kayaking performance.

Although promising advances have been made in the development of instrumented paddles, wearable sensors, and wireless monitoring systems, the field of kayaking performance assessment remains fragmented. Existing studies often focus on isolated components of the athlete equipment interaction such as paddle forces or trunk kinematics and are frequently restricted to controlled laboratory environments. As a result, few systems offer fully integrated, field-ready solutions capable of capturing the biomechanical complexity of kayaking under real-world aquatic conditions. In addition, inconsistencies in sensor placement, sampling strategies, and data processing methods hamper cross-study comparisons and reduce the generalizability of results.

Crucially, several technical challenges remain unresolved. These include the synchronization of mobile and fixed sensor nodes, signal degradation due to water-induced noise, and the need for energy-efficient wireless data transmission. Addressing these issues is essential for enabling reliable, high-resolution performance monitoring in open-water environments.

These gaps highlight the relevance and timeliness of a comprehensive review. Indeed, existing contributions across biomechanics, embedded systems, and sports science are often fragmented, lacking a unifying framework to integrate these perspectives into a cohesive methodological approach.

This review seeks to address these gaps by synthesizing recent technological advances and critically evaluating current methodologies. It offers an updated overview of the tools, techniques, and limitations associated with kayaking-specific measurement systems while highlighting opportunities for interdisciplinary collaboration and future system development. The insights derived from this analysis may also inform performance assessment in other aquatic sports facing comparable constraints.

This article is organized as follows. Section II highlights kayak practices and the importance of developing technical skills and optimizing performance. Section III discusses the requirements and specifications of on-water kayak measurement systems. Section IV presents recent advances in the modeling and analysis of kayak propulsion, thereby identifying the main biomechanical parameters (dynamic and kinematic) used in performance evaluation. A review of technologies for kayak performance analysis, including both commercial systems and academic studies, is provided in Section V. Section VI explores the main limitations of current systems and proposes avenues for improvement. It covers key challenges related to hardware design, sampling frequency, signal preprocessing, feature extraction, data transmission, energy constraints, and time synchronization in multisensor setups. Finally, Section VII proposes an integrated approach for comprehensive kayak performance analysis, emphasizing the

<table><caption>TABLE I
KAYAKING DISCIPLINES</caption><thead><tr><td>Discipline</td><td>Aquatic Environment</td><td>Nature of Practice</td></tr></thead><tbody><tr><td>Touring</td><td>Flatwater</td><td>Touristic and recreational</td></tr><tr><td>Sea kayaking</td><td>Sea, estuaries</td><td>Touristic and recreational</td></tr><tr><td>Freestyle or rodeo</td><td>Flatwater, white-water</td><td>Touristic and recreational</td></tr><tr><td>River kayaking</td><td>Flatwater, white-water</td><td>Touristic and recreational</td></tr><tr><td>Wave-ski and kayak surfing</td><td>Sea, estuaries</td><td>Touristic and recreational</td></tr><tr><td>Sprint racing</td><td>Flatwater, white-water</td><td>Competition in Olympics</td></tr><tr><td>Slalom</td><td>Whitewater</td><td>Competition in Olympics</td></tr><tr><td>Whitewater kayaking</td><td>Whitewater</td><td>Competition</td></tr><tr><td>Kayak polo</td><td>Flatwater</td><td>Competition</td></tr><tr><td>Marathon</td><td>Flatwater, white-water</td><td>Competition</td></tr></tbody></table>

role of data fusion and real-time feedback to support coaches and athletes in practical contexts.

## II. KAYAKING PRACTICES

Kayaking includes a variety of disciplines, as detailed in Table I, each suited to specific aquatic environments (e.g., flatwater, whitewater, and sea). While certain forms of kayaking are primarily recreational or touristic in nature, others are high-performance competitive sports requiring advanced technical skills and physical conditioning. These disciplines encompass sprint racing, slalom, whitewater kayaking, kayak polo, and marathon. The inclusion of several of these disciplines in the Olympic Games underscores their significance and the high level of athletic competition they entail.

Extracting quantitative information to distinguish between professional kayak racing techniques requires a thorough understanding of the fundamentals of this activity. The paddling technique is often divided into distinct subphases, although classifications may vary slightly across studies for example, five phases (entry, catch, pull, exit, and recovery) [12] or four phases (entry, pull, exit, and recovery) [8]. Typically, these phases include catch phase (from the moment, the blade is in a horizontal position until it is fully submerged in the water), pull phase (from full blade immersion at the beginning of the stroke to the initiation of blade withdrawal), exit phase (from a submerged blade position to a horizontal in-air position), and recovery phase (the period during which the blade moves in the air in preparation for the next stroke). These phases are usually grouped into two main phases: the aerial phase (recovery and entry) and the underwater phase (catch, pull, and exit).

Regarding boat propulsion, kayakers alternate paddle strokes on both sides, engaging the muscles of the trunk, upper limbs, and lower limbs. While seated with feet positioned on footrests, hands evenly spaced on the paddle shaft, and legs slightly bent and apart, athletes aim to optimize force transmission and maintain effective control of the boat under varying conditions. During a right-side paddle stroke, for instance, the right shoulder flexes and the elbow extends, accompanied by a slight forward lean of the torso. Concurrently, the right
knee extends and the pelvis rotates horizontally, contributing to enhanced trunk rotation [9], [12]. This complex sequence of movements must be executed in a harmonized and coordinated manner to ensure efficient propulsion.

Technical skills include coordinating body movements, properly using the paddle, maintaining posture and balance, and navigating various conditions such as currents, waves, and obstacles. A sound technique allows kayakers to move smoothly and maximize the power and efficiency during each paddle stroke. Performance optimization focuses on enhancing the efficiency and speed of kayakers by finding the most effective way to apply force to the paddle, leveraging equipment components to their full potential, and eliminating unnecessary movements that result in energy loss. Through optimized technique and movement economy, kayakers are able to achieve greater propulsion and sustain higher speeds.

The development of technical skills and performance optimization are closely interrelated. Proficiency in technical skills enhances a kayakers' capacity to apply force efficiently and adapt to varying conditions, thereby facilitating performance gains. Conversely, efforts to optimize performance often reveal specific areas where technical refinement is needed. This dynamic, iterative process of feedback and continuous improvement enables kayakers to progressively approach their full athletic potential.

### III. NECESSITY AND SPECIFICATIONS OF ON-WATER KAYAK MEASUREMENT SYSTEMS

Portable measurement systems are essential for analyzing on-water kayak performance due to the unique challenges of the kayaking environment and the dynamic nature of the sport. These systems enable the recording and analysis of real-life performances in realistic conditions, in contrast to laboratory analyses on kayak ergometers. During on-water training, coaches typically track kayakers from a motorized boat or from the shore using a bike or car. This approach often restricts their ability to quantify movements and offer detailed feedback on stroke techniques. Portable measurement systems offer greater flexibility and mobility compared to fixed laboratory setups. They can now be integrated into the kayakers' equipment, such as through the use of force sensors or IMUs, allowing for precise and continuous performance analysis overextended periods during training or competitions.

Improving kayaker performance can be achieved either by increasing propulsive power or reducing drag on the kayak. This involves optimizing efficiency, power output, and the power-to-weight ratio of the kayaker while minimizing the kayaks' rotations [13]. Portable measurement systems play a critical role in achieving these improvements. To be truly effective and facilitate athlete progress, these systems must meet several specific requirements, identified since the 1980s [14]. These requirements fall into three categories: usability, safety, and technical specifications.

In terms of usability, the sensors integrated into the kayak, particularly on the paddle, should be lightweight. Their positioning must be carefully chosen to avoid disrupting the kayakers' technique. The system should be self-contained, eliminating the need for the kayaker to carry additional items

such as heavy batteries or radio transmitters. Installation should be simple, quick, and easy to use, without requiring specific technical skills.

Regarding safety, all collected data should be managed in compliance with privacy standards and secured against any form of unauthorized access or misuse.

Finally, concerning technical specifications, the sensors used in monitoring systems should be waterproof and resistant to humidity and temperature variations. Given that Olympic sprint races span distances of 200, 500, and 1000 m [15], it is crucial for the communication technology to have a sufficiently long range to cover the entire 1000-m distance while being energy-efficient and providing sufficient battery life to cover a complete training session. Additionally, the network should remain operational even in case of sensor failure. The system may be multichannel to allow simultaneous tracking of up to four kayakers (K4) paddling together. Proper choice of sampling frequency can enable accurate motion reconstruction, and storage space should be sufficient for signals and software components. To ensure accurate and coherent multisensor data acquisition, especially when using wirelessly connected nodes (e.g., paddle, footrest, seat, and body segments), proper time synchronization across all sensor units is essential. This synchronization must be maintained continuously to allow meaningful comparison and fusion of data, particularly for real-time analysis and feedback during on-water training sessions.

By adhering to these design requirements, carefully selecting parameters, and applying rigorous data collection, processing, and analysis methodologies, substantial advancements can be achieved. This approach ensures that the systems used are effective, reliable, and capable of providing actionable insights that can enhance training programs and optimize competitive performance in kayaking.

### IV. PERFORMANCE EVALUATION PARAMETERS

#### A. Biomechanical Coupling Between the Kayaker and the Boat

The performance evaluation parameters require a biomechanical analysis of the kayaker/boat couple. To achieve forward acceleration for the boat, the kayaker needs to produce sufficient propulsive force $F_{Propulsive}$ to overcome the drag forces ($F_{Drag}$) acting on the boat [16], [17]. Thus, the dynamics of the kayak are governed by Newton's second law, whose expression along the direction of motion is given by

$$ (M_{KB} + M_{Add}) \frac{dv}{dt} = F_{Propulsive} - F_{Drag} \quad (1) $$

where $M_{KB}$ is the mass of both the kayaker and the boat; $M_{Add}$ is the added mass, which correspond to the mass of water entrained by the boat; $v$ is the boat velocity; $F_{Propulsive}$ is the propulsive force exerted by the shaft on the water in the direction of motion; and $F_{Drag}$ is the total drag.

During the kayak stroke, to create propulsive force, the athlete transfers force to the water through the paddle shaft. Specifically, the paddle transfers blade forces from the paddler to the water and subsequently to the kayak through the footbar and seat [14]. Therefore, instrumentation of the kayakers'
<figure><img src="image_3.png" /><figcaption>Fig. 1. Contact forces exerted by the kayaker on the boat, paddle, and footrest.</figcaption></figure>

<table><caption>TABLE II
MAIN DYNAMIC PARAMETERS OF PERFORMANCE IN KAYAK</caption><thead><tr><td>Contact point</td><td>Dynamic parameters</td></tr></thead><tbody><tr><td>Paddle</td><td><ul><li>Forces applied to the paddle by the right and left hand (maximum, minimum, average, pulse)</li></ul></td></tr><tr><td>Footrest</td><td><ul><li>Pressure forces on the toe clip by the right and left foot (maximum, minimum, average)</li><li>Tensile forces on the strap by the right and left foot (maximum, minimum, average)</li></ul></td></tr><tr><td>Seat</td><td><ul><li>Pressure forces on the right and left part</li></ul></td></tr></tbody></table>

contact points between the paddler and the various components (paddle, seat, and footrest) is crucial for understanding paddling mechanics and optimizing performance. Fig. 1 illustrates the different contact forces between the kayaker and the equipment (paddle, seat, and footrest) that would require an embedded measurement during motion. The athlete applies force to the paddle using the right hand ($F_{pr}$) and the left hand ($F_{pl}$). Forces exerted on the seat are also identified on the left side ($F_{sl}$) and on the right side ($F_{sr}$). Additionally, compression forces are applied to the footrest by the right leg ($F_{fr}$) and the left leg ($F_{fl}$). Finally, tensile forces are exerted on the strap by the right foot ($F_{str}$) and the left foot ($F_{stl}$), contributing to the propulsion and stabilization phases of the stroke.

Dynamic parameters of interest according to each contact point are detailed in Table II.

Among the biomechanical parameters of interest, kinematic parameters are mechanically related to the consequences of the forces involved in the paddling movement and the technique employed by the kayaker. Such parameters relate either to the paddle, footrest, boat, but also to the kayaker. The main kinematic parameters involved in the performance of kayaking are reported in Table III.

To assess the aforementioned parameters, kinetic and kinematic analyses of kayaking have been conducted under both controlled laboratory settings and natural in-field environments, each offering distinct advantages and limitations. In laboratory conditions, ergometers, force sensors, and motion capture systems enable precise measurement of joint angles,

<table><caption>TABLE III
MAIN KINEMATIC PARAMETERS OF PERFORMANCE IN KAYAK</caption><thead><tr><td>Measurement Location</td><td>Kinematic parameters</td></tr></thead><tbody><tr><td>Paddle</td><td><ul><li>Blade angle of attack</li><li>Distance relative to the boat</li><li>Direction in the water</li><li>Amplitude in the aerial phase</li><li>Blade immersion level in the water</li><li>Exit position from the water</li></ul></td></tr><tr><td>Footrest</td><td><ul><li>Tilt angle</li></ul></td></tr><tr><td>Boat</td><td><ul><li>Acceleration</li><li>Velocity</li><li>3D boat rotations: yaw, pitch, and roll</li><li>Boat Position</li></ul></td></tr><tr><td>Kayaker</td><td><ul><li>Head position</li><li>Bending angles of the torso</li><li>Trunk rotation (hip rotation)</li><li>Elbow or arm flexion</li><li>Amplitude of arm movements</li><li>Leg flexion</li><li>Knee position</li></ul></td></tr></tbody></table>

limb trajectories, and force application patterns with high temporal and spatial resolution.

These setups provide a repeatable environment, ideal for isolating specific movement phases and quantifying asymmetries or inefficiencies in the stroke cycle. However, they often lack hydrodynamic realism, as the absence of water flow, balance dynamics, and boat motion can alter natural paddling technique.

In contrast, in-field assessments conducted on-water using IMUs, waterproof force sensors, and Global Positioning System (GPS) systems allow for the analysis of athlete performance in ecological conditions, capturing boat velocity, stroke variability, and whole body coordination under realistic dynamic loads.

While in-field data are more ecologically valid, they tend to offer lower measurement precision and are more susceptible to noise and environmental variability.

These approaches have mainly focused on paddle trajectory or general spatiotemporal parameters. However, 3-D body-segment movement analysis has received less attention, which may be essential to understanding kayaking performance. Indeed, previous studies have shown that it is necessary to consider not only arm and leg movements but also trunk and leg movements during kayaking [18]. Furthermore, the intracycle analysis of parameters by their time histories or phase durations would be of considerable interest with regard to pacing or, for example, the onset of fatigue.

The biomechanical coupling between the kayaker and the boat, together with the associated instrumentation, has contributed to accurate modeling and analysis of kayak propulsion.

## B. Recent Advances in the Modeling and Analysis of Kayak Propulsion

Recent advances in sprint kayaking research have combined the biomechanical analysis of the kayaker-boat system with
theoretical models and experimental data to better understand propulsion and support real-time performance monitoring. Thus, Delgado and Ruiz [19] developed a 1-D model describing kayak motion as the balance between propulsion and hydrodynamic drag. Using a Fourier series for propulsion, the authors derived expressions for limiting velocity and acceleration. Such a mathematical model enables quick estimation of key race dynamics like power and distance but lacks experimental measurements of the force and kinematics. To deepen the understanding of kayaking propulsion and its applicability on-field, computationally and experimentally investigations are required [20], [21]. Thus, a recent study [22] developed a model of the dynamics of the kayak through simple physics parameters. This approach enabled to evaluate the important parameters to describe a kayak race from experimental data: the drag of the boat, an active drag coefficient, the mean propulsive force, and a propulsive length. While 1-D models offer simplicity, 2-D and 3-D models provide a more detailed representation of kayaking dynamics by incorporating lateral and rotational motions. These models are particularly useful for analyzing complex interactions between the kayak, paddler, and water. Therefore, Leroyer et al. [23] employed computational fluid dynamics (CFD) to simulate the 3-D flow around a kayak, capturing the effects of paddling motions on hydrodynamic forces. Their model considers the six degrees of freedom of the kayak and provides detailed insights into the influence of paddling techniques on kayak stability and propulsion. Similarly, Begon and Sardain [24] developed a 3-D kinematic model combined with force measurements to calibrate a kayak ergometer, enabling precise measurements of paddling mechanics and facilitating the assessment of training interventions.

Furthermore, experimental studies play a crucial role in validating theoretical models, providing empirical data for refining simulations, and, taken independently, also offer significant value in performance analysis. For example, Gomes et al. [20] conducted field studies measuring stroke frequency, paddle force, and kayak velocity in competitive paddlers. Their findings revealed a linear relationship between stroke frequency and kayak speed, highlighting the importance of optimizing stroke rate for performance. Goreham et al. [25] and Hay and Kaya [26] found that during international races, as the race distance decreased, stroke rate and boat speed both increased, while the stroke length decreased. This highlights the need for real-time monitoring systems that track cadence and stroke adjustments. As regards paddle trajectory, it has been shown that using wing paddle produces a slight curved trajectory, moving beyond the centerline of the boat [27], which enables to prolong the optimal duration of the propulsion generation position [28].

To assess the effectiveness of propulsion, Romagnoli et al. [29] introduced a force-based power balance approach. By directly measuring the propulsive force and estimating power losses due to hydrodynamic drag, they introduced a framework capable of quantifying athlete efficiency beyond simple velocity measurements. This approach reveals the potential of instrumented paddles to provide direct indicators of technical and energetic efficiency,

reinforcing the need for high-fidelity sensor integration into kayak performance systems.

Beyond force and cadence, technical execution of the stroke cycle remains central to propulsion quality. McDonnell et al. [30] developed an observational model identifying key phases (see Section II) and detailing how specific technical flaws can impair the effective transfer of force. Their qualitative framework, although not based on direct force measurements, highlights critical biomechanical principles that must be considered when interpreting sensor data, as technical execution directly influences propulsion effectiveness.

Altogether, these contributions illustrate the multifaceted nature of kayak propulsion, involving not only force generation but also cadence control, technical execution, and individualized adaptation to athlete profiles. In line with the objectives of this review, the integration of sensor technologies capable of capturing force, motion, and stroke characteristics is essential for enabling comprehensive, real-time, and individualized analysis of kayak performance on the water.

The synergy between theoretical modeling and experimental analysis enhances our understanding of kayaking dynamics. Both modeling and experimental approaches have to be grounded in real-world performance and continue to evolve with advancements in measurement technologies.

### C. Considerations for Multipaddler Crews (K2 and K4)

The current state of knowledge about biomechanics of kayaking must also be considered in relation to the type of crew configuration. Indeed, while most sensor-based analyses have been conducted on individual paddlers (K1), the dynamics of multiathlete boats such as K2 and K4 introduce additional complexities where stroke synchronization becomes critical for propulsion efficiency and team performance. Thus, several studies (see [31], [32]) have investigated interpaddler coordination in team boats, typically evaluating parameters such as follows:

1) stroke timing offsets between paddlers (e.g., timing offsets at paddle entry, peak force, and exit);

2) interpaddler phase differences or stroke frequency correlation;

3) synchronization of applied forces on the paddle;

4) energy transfer efficiency across the team;

5) position-based kinetic asymmetries (front versus rear);

6) response to seat inversion (preferred versus reversed).

These studies highlight that these new performance indicators complement those listed in Tables II and III, which pertain to K1.

In light of previous studies, it appears that varying levels of description exist, ranging from global system parameter estimation to more localized analyses.

There is a growing interest in obtaining kinematic and dynamic parameters, considering body-segment parameters and human-equipment contact points, along with tracking their temporal evolution. Obviously, these factors influence the selection of technology, including sensor choice, accuracy, range, energy consumption and efficiency, ergonomics, and user acceptance.
# V. REVIEW OF TECHNOLOGIES FOR KAYAK PERFORMANCE ANALYSIS

Nowadays, there are a wide variety of systems that operate on completely different physical principles, offering different performance characteristics for kinematic and dynamic measurements.

## A. Sensors for Dynamic Analysis

### 1) Sensor Technologies for Dynamic Analysis:

In kayaking, dynamic analysis primarily targets three key contact points: the paddle, the footrest, and the seat, as discussed in Section IV. While various force detection technologies have been applied in sports science, not all are suited for capturing the specific mechanical and environmental constraints of the kayak-athlete system, including rapid force application, motion-induced artifacts, and exposure to humidity.

2) *Sensor Requirements for Kayaking:* To define appropriate sensor requirements, both the magnitude and dynamics of applied forces must be considered. Elite kayakers can generate pulling forces up to 525 N at the start of a 1000-m K1 race [33], with average peaks around 274 N across stroke rates ranging from 60 to 124 strokes per minute (spm) [2]. Additionally, footrest forces can reach up to 444 N [34]. To avoid signal saturation and accommodate individual variability, a force measurement range of at least 0–600 N is required.

From a temporal perspective, sprint kayaking can involve stroke rates as high as 180 spm (3 strokes/s), yielding a total stroke cycle of approximately 333 ms. The effective pulling phase typically lasts under 150 ms, with rise times of 30–75 ms. Accordingly, a minimum sampling frequency of 100 Hz is recommended (see Section VI-B), and the sensor must exhibit a response time under 5 ms to track fast transients accurately and minimize distortion.

Force sensors must also provide stability, linearity, and precision under changing environmental conditions. These qualities ensure consistent performance and repeatability during training and outdoor deployment.

3) *Comparison of Force Sensing Technologies:* Several force sensing technologies have been explored in sports applications, including resistive, capacitive, piezoelectric, piezoresistive, optical, and gyroscopic devices [35], [36].

1) *Capacitive sensors* offer excellent sensitivity (up to 2.05 MPa⁻¹ [37] or 8000 N⁻¹ [38]) and high-resolution measurements, but their response times (typically between 27 and 160 ms [37]) limit their use in high-frequency paddle force tracking.

2) *Piezoelectric sensors*, conversely, are highly responsive (0.8–3.2 ms [38]) and well-suited for dynamic detection. However, their susceptibility to vibration and signal drift, especially in unstable environments like kayaking, compromises measurement reliability [39].

3) *Piezoresistive sensors*, which operate via resistance variation under mechanical deformation, offer promising flexibility, ease of integration, and good sensitivity (e.g., 31.6 kPa⁻¹ [37]) with response times around 24 ms.

Yet, challenges remain under dynamic loading, notably mechanical hysteresis, signal drift, and performance degradation [40]. While effective for low-frequency measurements (e.g., footrest or seat), they are less suitable for capturing high-strain paddle forces. A recent study by Bouro et al. [41] demonstrated the application of a Velostat-based piezoresistive sensor matrix on the footrest of a kayak ergometer.

4) *Force Sensing Resistors* (FSRs) are another piezoresistive option, offering simplicity and affordability. However, their limited accuracy (often above 10%) restricts their use in elite performance monitoring [39]. Despite ongoing improvements in flexible sensor design [40], their reliability under dynamic loads remains insufficient for precise paddle force estimation.

5) Ultimately, strain gauges and, to a lesser extent, piezoresistive sensors remain the most viable options for precision force measurement in kayaking. The strain gauge effect results from geometric deformation and it is simpler to implement and more stable than the piezoresistive effect, which, although more sensitive, is often anisotropic and less predictable [42]. Metal foil strain gauges are especially valued for their durability, ease of mounting, and reliability [43], while semiconductor gauges are less used due to their nonlinear responses [44]. Additionally, resistance strain gauges are particularly well-suited for dynamic sports applications due to their inherent linearity, extremely low mass, wide frequency response (more than 100 000 Hz), and their versatility across diverse use cases [45].

In summary, while capacitive, piezoelectric, and piezoresistive technologies continue to evolve and find applications in sport and wearable systems, strain gauges currently offer the best tradeoff between sensitivity, dynamic responsiveness, stability, and integration ease for paddle force analysis. In parallel, Velostat-based systems represent an accessible and lightweight solution for low-frequency contact zones such as the footrest and seat, where pressure mapping is essential to understand athlete coordination and comfort. Capacitive and piezoresistive sensors may nonetheless be appropriate for less dynamic zones such as the footrest and seat, where force profiles evolve more slowly. These sensor types are increasingly integrated into both commercial and academic measurement systems for athlete monitoring.

### 4) *Commercial Systems for Dynamic Force Monitoring:*

For dynamic measurement in various sports activities, commercial constructions typically integrate load cells or strain gauges available in the market [46], [47], [48]. Load cells are sensors that incorporate multiple strain gauges within a test body.

When it comes to using force sensors for dynamic performance analysis on the water in kayaking, here are some examples of available commercial systems.

1) *Paddlemate* [49]: A system specifically developed for paddle sports and rowing, designed to measure pulling force, stroke frequency, stroke symmetry, and distance speed. It consists of two components: the BASE and the
POD, and is accompanied by a mobile application for data visualization.

2) *The Power Meter* [50]: This system differs from paddle-mounted sensors as it is a paddle itself equipped with force and motion sensors, developed by One GIANT LEAP. It provides real-time measurements of generated power, power budget, and feedback on stroke rate and is compatible with ANT+ and Bluetooth devices [51].

3) *Quiske Rowing Sensor (Quiske)*: While primarily designed for rowing, this system can also be used for kayaking. It uses force sensors placed on the paddle to measure the force exerted by the kayaker and analyze paddle technique [52].

These systems provide insights into paddle forces but lack comprehensive details on the force sensing techniques used. There are currently no systems available for measuring footrest and seat forces. Despite advancements, kayaking has not fully benefited from these improvements, leading to a lack of wearable on-water kayak force measurement systems. This gap poses a significant challenge for dynamic kayak performance analysis.

### 5) Academic Solutions for Dynamic Analysis:

Force measurement in kayaking typically uses custom systems with load cells or strain gauges available in the market. One recent example is the portable FPaddle system, developed by Gomes et al. [1], [2] and equipped with two pairs of strain gauges. Their study showed a decrease in force with more paddle strokes, an increase in maximum force amplitude, and a decrease in stroke time with higher stroke frequency for both genders. However, this quantification is not entirely accurate as it does not take into account variations in the efforts exerted by the kayaker on the footrest.

The study by Klitgaard et al. [4] highlights the importance of footrest efforts in analyzing kayakers' performance. This study analyzed the relationship between leg push efforts on the footrest and boat speed measured using a GPS watch. This study demonstrated that a decrease in leg traction and push forces on the footrest leads to a significant decrease in boat speed. However, this analysis did not consider the efforts exerted by the athlete on the paddle, a crucial parameter for boat speed and performance [1], [2]. An earlier study by Sturm et al. [5] compared the amplitude of the force exerted on the paddle and on the footrests with the power output achieved on an ergometer. The results showed that increasing power output was correlated with an increase in the maximum force exerted on both the paddle and the footrests, emphasizing the importance of considering both factors in kayak performance analysis.

A more recent study by Bonito et al. [6] on a kayak ergometer, instrumented the footrest, strap, and paddle with load cells to compare force amplitudes (compression and traction) between the most and least performing kayakers. The results showed differences in force amplitude among similarly performing kayakers, with higher and better-synchronized values observed in the most performing kayakers. However, this study did not take into account the efforts exerted on the seat, another important parameter to consider in a comprehensive

analysis of kayak performance, as highlighted by the study by Nilsson and Rosdahl [11] on the seat and footrests.

Some studies have explored kayakers' dynamics by mounting sensors on the paddle blade [53], [54]. For example, using fiber Bragg grating (FBG) sensors, Niu et al. [54] analyzed the force exerted on the blade and its distribution across different regions. However, the installation of FBG sensors on the paddle blade can be complex, potentially affecting the blade hydrodynamic properties and the athlete performance. The system also depends heavily on a stable light source and a precise FBG interrogator system, both of which can be affected by external factors such as vibrations and temperature fluctuations. Furthermore, the high costs associated with FBG technology and the need for specialized equipment and expertise for data interpretation can limit its widespread adoption in practical sports settings. Nevertheless, the possibility of placing pressure sensors on the paddle shaft, where the athletes hands rest, could be explored, as studies have shown that how kayakers grip the paddle can influence their performance.

The aforementioned studies have highlighted the involvement and importance of efforts exerted on the paddle, footrests, strap, and seat. However, no study has yet examined the complete dynamics, even in a laboratory context, and there is currently no available water analysis system capable of conducting such analysis.

## B. Sensors for Kinematic Analysis

### 1) Sensor Technologies for Kinematic Analysis:

Traditional kinematic analysis systems for kayakers have utilized optoelectronic motion sensors (OMSs), such as cameras equipped with reflective markers placed on the athletes body landmarks (Vicon MXT, Optotrack, and iGPS-Nikon) [55], [56], [57], [58], [59]. While these sensors have been effective in tracking kayakers' movements in stable environments like laboratories [60], their use during water training poses challenges. The accuracy of OMS strongly relies on camera stability and the relative positions between cameras and markers on the athlete [61]. The fast and complex movements of kayakers, with arms moving in various directions, can lead to data dropouts due to the need for line of sight [62] with OMS, which are sensitive to camera movements. External conditions such as sunlight and water reflections can also disrupt measurements. Furthermore, these systems are often costly.

A key challenge also lies in the ability to assess lower limb movements and their contribution to propulsion in situ. However, due to the positioning of the lower limbs within the kayak, these movements are not accessible via OMS. In this context, embedded systems such as IMUs, combined with a biomechanical model of the lower limbs, may represent a promising approach.

Other motion capture systems have been explored for sports in general [61], including electromagnetic systems (EMSs), image processing systems (IMSs) such as Kinect, ultrasound localization systems, and IMU. Among them, IMS and IMU have proven to be the most suitable and accurate for outdoor long-distance sports. However, for real-time analysis on water, IMS is unsuitable due to the complexity and speed of
kayakers' movements, as well as the challenge of performing real-time image recognition in an unstable environment.

Among the currently available technologies for capturing and identifying human movements, wearable sensors such as IMUs and radars are the most suitable and commonly used [63]. Radars offer high precision but are limited to fixed scenarios and do not allow for segmental analysis. On the other hand, while the accuracy of wearable sensors, including IMUs, depends on the processing methods and algorithms used, they are generally more practical, user-friendly, and less restricted by specific usage scenarios.

Technically, an IMU sensor unit combines multiple components to measure and analyze movements. Typically, a nine-degree-of-freedom (DOF) IMU is used, including a three-axis accelerometer (ACC) to measure linear acceleration, a three-axis gyroscope (GYRO) to measure angular velocity, and a three-axis magnetometer (MAG) to measure the magnetic field and orientation relative to the Earth magnetic field. This combination of sensors enables the construction of robust and accurate systems for motion detection and analysis.

However, it is worth noting that IMU sensors are not without drawbacks. They can be sensitive to electromagnetic disturbances, which can lead to interference and measurement errors, especially in electromagnetically noisy environments. Additionally, regular calibration of IMUs is necessary to ensure accurate measurements, but this process can be time-consuming. As for radars, while they offer high precision, they are also susceptible to electromagnetic interference, limiting their use in scenarios where such factors are more predominant. Furthermore, setting up and maintaining radar systems can be costly and resource-intensive.

Despite these drawbacks, given the recent advancements in IMU technology and their advantages in terms of user-friendliness and versatility, they still represent the most suitable solution for real-time kinematic analysis of kayakers on water.

## 2) Commercial Systems for Kinematic Monitoring:

There are three main categories of motion capture systems using IMUs. The first category consists of systems designed for specific sports, such as Catapult One [64], Zepp [65], Kinexon [66], and ShotTracker [67], which are tailored to specific sports like basketball, golf, softball, and baseball. The second category (Table IV) encompasses nonspecific sport motion capture systems [68], [69], [70], [71]. These systems are used in various applications and involve design tradeoffs in terms of range and cost. MVN AWINDA [69] and Movella DOT [70] have ranges of approximately 50 and 20 m, respectively. The VICON Blue Trident [71] has a range of 20 m. It is essential to note that in this context, range, refers to the maximum distance these systems can reliably capture and transmit motion data between the sensors and the data hub or receiver. Several of these synchronized sensors would be required to analyze complex movements effectively.

Finally, the third category relates to IMU sensors used in custom applications. These sensors are designed to be programmable and enable the development of customized motion capture systems. Sensor configuration is an important factor in reducing accumulated errors, and IMU system in

package (SiP IMU) fusion sensors offer high accuracy and low drift [78].

These commercial systems that utilize IMUs offer accurate measurements of the kayakers' orientation, acceleration, and angular velocity during kayaking practice [3], [79]. However, most measurement systems are designed to be used in specific conditions. Environmental conditions on the water, such as waves, currents, or wind, can affect the accuracy of the measurements. Current systems may struggle to compensate for these factors and provide reliable data in variable conditions. It is important to develop systems that can reliably operate under varying environmental conditions. In the specific context of kayaking, it is challenging to find portable systems for tracking kayaker kinematics on the water. There are a few existing systems such as Motionize [80] and Vaaka Cadence [81], but they are primarily intended for measuring paddle-specific parameters.

For a comprehensive kinematic analysis of the kayaker on the water, existing systems are not suitable and have limitations in terms of accuracy, sampling rate, and data transmission range. Currently, there is no specific system available on the market to meet these needs.

## 3) Academic Solutions for Kinematic Analysis:

Indeed, 3-D body-segment motion analysis is essential for understanding and improving kayaking performance. Several studies have been conducted to analyze these movements, but most of them have been done on ergometers or using cameras, and there is a lack of research using wearable sensors on the water.

A study by Limonta et al. [9] used cameras and retroreflective markers to perform 3-D kinematic analysis of the limb movements of elite, intermediate, and novice kayakers on a kayak ergo-meter. The results showed specific trajectory patterns and higher paddle amplitude in the sagittal plane among elite kayakers.

Another study by Kendal and Sanders [27] used two cameras to analyze the joint and paddle trajectories of kayakers on a 20-m course. Analysis revealed clear distinctions between the most and least skilled kayakers, especially regarding the consistency of joint and paddle trajectories during the recovery phase.

However, wearable systems have not been widely employed in conducting on-water studies. Most current wearable studies are limited to identifying phases of kayaking practice using IMU sensors placed on the upper body of the kayaker [8]. There is, therefore, untapped potential for using IMUs in analyzing joint movements of a kayakers' limbs during on-water activities.

Recent developments in sparse IMU-based wearable networks from other application domains offer promising opportunities for kayaking. For instance, Bao et al. [82] introduced a sparse sensor network composed of only five soft wireless IMUs, strategically placed on the head, wrists, and ankles, to assess infant movement. Despite the limited number of nodes, their system enables full-body motion monitoring with high accuracy using embedded machine learning. Similarly, Armani et al. [83] proposed Ultra Inertial Poser, a system that combines six wearable IMUs with ultrawideband (UWB)
<table><caption>TABLE IV
MOTION ANALYSIS SYSTEMS MARKETED FOR A WIDE VARIETY OF APPLICATIONS</caption><thead><tr><td>Systems</td><td>Company</td><td>Dimension (mm)</td><td>Weight (g)</td><td>Sensors - Materials</td><td>Frequencies</td><td>Communication</td><td>Range (m)</td><td>Battery Life (hours)</td></tr></thead><tbody><tr><td>Maxi Phyling [72]</td><td>Phyling</td><td>100x70x45</td><td>250</td><td>GPS, IMU</td><td>GPS 10 Hz, ACC and GYRO up to 500 Hz</td><td>Bluetooth and internal storage</td><td>Not available</td><td>10</td></tr><tr><td>MVN Link [68]</td><td>Xsens</td><td>58.6x38x20.9</td><td>Not available</td><td>17 wired sensors</td><td>Up to 240 Hz</td><td>WiFi</td><td>150</td><td>10</td></tr><tr><td>MVN Awinda [69]</td><td>Xsens</td><td>47x30x13</td><td>16</td><td>17 wireless sensors</td><td>Up to 60 Hz</td><td>Awinda protocol</td><td>50</td><td>6</td></tr><tr><td>Movella Dot [70]</td><td>Xsens</td><td>36.3x30.35x10.8</td><td>11.2</td><td>IMU 10 DoF, 5 wireless sensors</td><td>Up to 60 Hz</td><td>Bluetooth 5+</td><td>20</td><td>8</td></tr><tr><td>Physilog 6S [73]</td><td>Gait Up</td><td>42.2x31.6x15</td><td>15</td><td>Not available</td><td>ACC and GYRO 512 Hz, MAG 256 Hz</td><td>BLE</td><td>Not available</td><td>20</td></tr><tr><td>iSens [74]</td><td>STT Systems</td><td>64x38x18</td><td>46</td><td>1 to 18 wireless sensors nodes</td><td>25 Hz to 400 Hz</td><td>WiFi</td><td>Not available</td><td>Not available</td></tr><tr><td>Perception Neuron 3 [75]</td><td>Noitom</td><td>27.9x16.2x11.6</td><td>4.1</td><td>17 sensors in the body kit</td><td>Output rate 60 Hz, data calculation rate 600 Hz</td><td>Not available</td><td>4</td><td>5</td></tr><tr><td>OPAL System [76]</td><td>APDM</td><td>43.7x39.7x13.7</td><td>&lt;25</td><td>Up to 24 Opals on a single array</td><td>ACC, GYRO and MAG 20 to 128 Hz</td><td>ISM band 2.40-2.48 GHz, internal storage (8 Gb)</td><td>30 (LoS)</td><td>16</td></tr><tr><td>Ultium Motion [77]</td><td>NORA-XON</td><td>44.5x33x12.2</td><td>19</td><td>16 wireless sensors</td><td>Up to 400 Hz</td><td>Proprietary 2.4 GHz hopping protocol</td><td>40</td><td>8</td></tr><tr><td>Blue Trident [71]</td><td>Vicon</td><td>42x27x11</td><td>9.5</td><td>4 nodes, up to 20 wireless sensors</td><td>ACC High-g 1600 Hz, GYRO 1125 Hz, MAG 100 Hz</td><td>Bluetooth 5, internal storage 16 Gb</td><td>20</td><td>12</td></tr></tbody></table>

ranging. By fusing intersensor distance measurements with inertial data through a graph-based learning framework, their approach delivers accurate 3-D pose estimation without relying on visual markers or fixed infrastructural elements.

These systems show that sparse sensor networks can capture complex body kinematics while remaining low-cost and portable qualities well suited for on-water kayaking analysis. However, their application in this context is still limited: most validations were conducted indoors, and their robustness to aquatic conditions remains untested. Moreover, they require careful calibration, precise sensor placement. Their use in real-time feedback systems also needs further investigation to ensure low latency and reliability outdoors.

While IMUs already enable access to key biomechanical indicators such as the paddle blades' angle of attack, which affects both propulsion and boat orientation [84], current wearable systems do not yet support such fine-grained analysis in realistic kayaking conditions. These observations underline the need for more integrated approaches that combine kinematic data with dynamic force measurements.

### C. Toward a Complete Analysis: Dynamics and Kinematics

In competition, victory margins are determined by mere fractions of a second, making the optimization of all performance-related factors essential. For instance, the narrowest margin recorded between two kayakers for a podium finish at the Olympic Games occurred during the Paris 2024 Olympics in the men's K4 500-meter event.

In this race, Germany clinched the gold medal with a time of 1:19.80, narrowly defeating Australia by just 0.04 s. Achieving high performance in kayaking requires precise coordination between the upper and lower body of the athlete, as well as efficient use of equipment. Under such conditions, performance analysis necessitates the use of specialized kayak systems.

Commercial systems and custom systems described in the literature now employ IMUs and strain gauges to analyze the dynamic parameters applied to the footrests and/or paddle, often in conjunction with boat kinematics.

Most advancements in the literature regarding real-time evaluation of kayaker performance focus on simultaneous measurement of forces acting on the paddle and footrests, along with boat kinematics [3], [15], [85], [86]. A study by Bifaretti et al. [3], utilizing the e-kayak system, demonstrated that paddle strokes were accompanied by leg support on the footrests to enhance propulsion and rotational balance of the boat. The study also highlighted that increased forces with a low paddle stroke frequency resulted in reduced boat speed. These findings emphasize the importance of simultaneously measuring efforts on the paddle, footrests, and boat kinematics.

Bonaiuto et al. [85] conducted analyses on a K1 crew using the multiprotocol wireless e-kayak system, which was introduced in 2018 [87] and further developed in 2022 [15]. This system was used to analyze paddle stroke synchronization for a K2 crew by calculating the time the paddle spent in the water from the force applied to the handle and paddle. Simultaneous study of the paddle, footrests, and boat kinematics
<table><caption>TABLE V
SUMMARY OF SOME STUDIES ON THE MONITORING OF KAYAKERS IN THE LITERATURE</caption><thead><tr><td>Authors</td><td>Sensors location</td><td>On water ?</td><td>Description</td><td>Communication</td><td>Frequency</td></tr></thead><tbody><tr><td>M. Begon et al. (2009) [12]</td><td>Seat, footrest, paddle</td><td>No</td><td>Provide kinematics and contact forces with seven uni-axial force sensors and two goniometers, kinematics and contact forces</td><td>Not available</td><td>Kinematic data: 50 Hz, analog data: 100 Hz</td></tr><tr><td>E. Limonta et al. (2010) [9]</td><td>Kayakers body</td><td>No</td><td>17 retro-reflective hemispherical markers placed on various anatomical landmarks. Analyzed parameters include paddling amplitude, joint angles (elbows, knees, pelvis, trunk), and bilateral asymmetries</td><td>Recorded on ELITE BTS</td><td>100 Hz</td></tr><tr><td>D. Sturm et al. (2010) [5]</td><td>Paddle and footrests</td><td>Yes</td><td>Compared the amplitude of the force exerted on the paddle with the power output achieved on an ergo-meter</td><td>Bluetooth</td><td>Paddle: 150 Hz, footrest: 100 Hz</td></tr><tr><td>B. Gomes et al. (2011) [1]</td><td>Paddle</td><td>Yes (swimming pool)</td><td>The FPaddle system used four strain gauges attached to each side of the paddle shaft. Parameters analyzed include peak force, time to peak force, time of contact of the paddle blade with the water, and force profiles.</td><td>V-link (wireless)</td><td>512 Hz</td></tr><tr><td>R. Helmer et al. (2011) [53]</td><td>Paddle blade, boat and shirt</td><td>Yes</td><td>Flexiforce sensors on paddle blades to measure hydrodynamic pressure. Accelerometer mounted on the kayak to measure boat acceleration. E-textile shirt with strain sensors to monitor arm technique</td><td>Bluetooth</td><td>Paddle: 250 Hz</td></tr><tr><td>G. Vadai et al. (2011) [79]</td><td>kayak boat</td><td>Yes</td><td>The system included a three-axis ACC and GYRO. Parameters analyzed were stroke rate, symmetry, and pull time. The device provided data on acceleration and angular velocity to analyze motion</td><td>Data recorded on a micro SD card and analyzed offline</td><td>ACC and GYRO: 1000 Hz</td></tr><tr><td>G. Vadai et al. (2013) [60]</td><td>Kayak boat</td><td>Yes</td><td>The system uses a IMU 6 DOF. Parameters analyzed include stroke time, stroke impulse, and fluctuations in movement signals. Standard deviation and Poincaré plots were used as new indicators of paddling skill</td><td>Off-line analysis, data are saved to a 4 GB micro-SD card</td><td>1000 Hz</td></tr><tr><td>E. Nilsson et al. (2014) [11]</td><td>Seat and footrest</td><td>Yes</td><td>Develop portable force-measuring devices to record the pushing and pulling forces applied by each foot to the footrest and the horizontal force at the seat</td><td>Off-line analysis</td><td>Not available</td></tr><tr><td>B. B. Gomes et al. (2015) [2]</td><td>Paddle and boat</td><td>Yes</td><td>FPaddle system and an ACC placed inside the kayak, the shape of the paddle force-time curve at different stroke rates</td><td>Data recording, Off-line analysis</td><td>256 Hz</td></tr><tr><td>S. Bifaretti et al. (2016) [3]</td><td>Paddle, footrest and kayak boat</td><td>Yes</td><td>e-Kayak system includes a GPS, a 6DoF IMU, and two force channels for the paddle and foot brace. Parameters analyzed include speed, stroke frequency, force symmetry, roll and pitch of the boat, and synchronization between paddle and foot brace forces</td><td>Bluetooth and local recording on the master node</td><td>GPS: 10 Hz, IMU, strain gauge and MCU: 50 Hz</td></tr><tr><td>J. Nilsson et al. (2016) [86]</td><td>Paddle and footrest</td><td>Yes</td><td>Study on the contribution of leg forces when kayaking on water</td><td>Not available</td><td>Not available</td></tr><tr><td>V. Bonaiuto et al. (2018) [87]</td><td>Paddle, footrest and boat</td><td>Yes</td><td>Introducing a new Wireless Sensor Network (WSN) system for elite sports applications</td><td>Band ISM 2.4 GHz, WiFi and on an SD memory card</td><td>GPS 20 Hz, IMU and strain gauge: 50 Hz</td></tr><tr><td>L. Niu et al. (2019) [54]</td><td>Paddle (handle and blade)</td><td>Yes</td><td>Analysis of the force and the distribution of the force on different regions of the blade using a system of 19 fiber Bragg grating (FBG) sensors</td><td>Reflective spectrum</td><td>Light exposure time: 400 microseconds</td></tr><tr><td>S. Harrison et al. (2019) [88]</td><td>Simulation</td><td>No</td><td>The model includes biomechanical representations of the athlete and the kayak, and fluid dynamics simulations of the water. Parameters analyzed include kayak speed, fluid response to paddle interactions, force and impulse on the paddle and hands, stroke rate, stroke depth, and loading on the athletes body</td><td>Not applicable</td><td>Not applicable</td></tr><tr><td>V. Bonaiuto et al. (2020) [85]</td><td>Paddle, footrest and boat</td><td>Yes</td><td>Investigate the capabilities of the e-Kayak Portable Wireless System (DAQ) in performance analysis</td><td>Band ISM 2.4 GHz, WiFi</td><td>GPS: 20 Hz, IMU and strain gauge: 50 Hz</td></tr><tr><td>K. Klitgaard et al. (2021) [4]</td><td>Footrest and boat</td><td>Yes</td><td>Description of the characteristics of the pushing force of the legs and their relation to the speed of the boat</td><td>Off-line analysis</td><td>Footrest: 1000 Hz</td></tr><tr><td>Klitgaard, Kent K et al. (2022) [10]</td><td>Footrest</td><td>Yes</td><td>Kinematic profile comparison of the kayak on the water and on the ergometer during maximum paddling</td><td>Off-line analysis</td><td>Not available</td></tr><tr><td>Bonito et al. (2022) [6]</td><td>Footrest, strap and paddle</td><td>No</td><td>Study the magnitude and shape of the forces with 8 strain gauge load cells</td><td>Local processing</td><td>100 Hz</td></tr></tbody></table>

would also be highly useful for crew training. Although the e-kayak system is the most advanced portable system used for kayaker performance analysis found in the literature, it is not

yet considered complete. It does not allow for the analysis of kinematic data related to the kayakers' posture, paddle orientations, and seat forces.
Wearable sensors have revolutionized real-time motion tracking in sports [78]. The increasing miniaturization of these sensors (IMUs and strain gauges) enables their integration on any part of the kayakers' body and equipment, with minimal influence on environmental conditions. This represents the current main trend in athlete tracking systems. Table V presents some studies conducted on the analysis of kayaker performance.

Despite these remarkable advancements, there is currently no system that allows for a comprehensive measurement of kayakers' performance on the water. The limitations related to analysis parameters are closely tied to the design challenges imposed by the kayak environment. In addition to the gaps in research on kayakers' performance measures, another major gap concerns the design of these systems. Most proposed solutions focus solely on parameter measurement without delving into studies on the components and electronic development required for accurate motion reconstruction. In addition to IMUs for kinematics and strain gauges for dynamics, other wearable sensors such as pressure sensors at the interface between the athlete and the seat could provide performance context. For instance, GPSs can provide position information [89].

It should be noted that the specific features and availability of these systems may vary depending on the manufacturer and model. Table VI presents a summary of commercial systems available for dynamic and kinematic performance analysis on the water in kayaking, including the system name, manufacturers, sensors used, their location, and the measured parameters.

Vaaka Cadence measures only the paddle cadence, while Paddlemate also measures cadence and paddle forces, and Motionize focuses on paddle orientation and cadence instead (Table VI). However, none of these paddle systems measure both paddle kinematics (orientation angle, velocity, acceleration, and so on), dynamics (forces and force curve), and derived parameters (e.g., stroke rate)

# VI. LIMITATIONS, GAPS, AND APPROACHES FOR IMPROVEMENT

To achieve comprehensive and accurate kayak performance analysis, it is crucial to address several key limitations and gaps in current measurement systems. First, current systems often focus on specific aspects such as paddle force or movement but lack a holistic evaluation of body coordination, footrest use, and boat stability. Additionally, data transmission delays or lack of real-time acquisition restrict live information during training or competition. Precision and reliability issues, including signal noise, interference, and individual variations, affect measurement accuracy and reliability. Integrating and analyzing data from different systems also presents challenges, requiring accurate and consistent data fusion methods. The complexity of large amounts of raw data necessitates advanced, user-friendly analysis tools to extract relevant information. Moreover, some systems are bulky or need external power, limiting practical use on the water, highlighting the need for compact, lightweight, and autonomous solutions. The high cost of some systems restricts accessibility for

many athletes and coaches, necessitating more affordable solutions. Furthermore, the absence of standardization hinders result comparison and data integration from different systems, leading to inconsistencies. Collaboration is needed to develop recognized measurement standards.

Addressing these limitations requires overcoming several hardware design challenges, optimizing sampling frequencies, and improving transmission technology and energy efficiency.

## A. Hardware Design Constraints

Kayaking requires quick movements and a high frequency of motion [91], making it necessary to use portable and water-resistant measurement devices. However, accurately reconstructing the kayakers' movement from signals measured with wearable sensors becomes a very challenging task due to the complexity and speed of the involved gestures. The use of inertial signals on kayaking does not achieve sufficient accuracy, especially at high speeds [57], [79].

Meanwhile, systems based on IMUs and deformation sensors have been developed, validated, and used in various sports (Tables IV and VI). It is essential to consider and adapt these systems in a way that allows for efficient and real-time analysis of the kayakers' biomechanics.

IMUs have the advantage of being energy-efficient, compact, lightweight, and relatively affordable. However, in the absence of a fixed reference point, errors (drift) accumulate over time, which must be taken into account for accurate analysis.

The measured forces strongly depend on the sensor placement. For instance, some studies [3], [5], [85] have placed strain gauges at different positions, such as 120 mm from the inner gripping areas of each hand and at a 30° angle [5], or at 53 cm from the end of the paddle blade [85], to measure forces on the blade. To ensure relevant comparison of measurement results between these different systems, it is essential to specify and standardize the choice of sensor placement.

Assuming a known uniaxial state of stress on the paddle and alignment with the principal axes, long and linear strain gauges may be sufficient for measuring forces on the paddle. Given that the paddle shaft typically has a diameter of around 29 mm, it is possible to mount strain gauges that are easier to install and wire than miniature gauges. Smaller gauges (less than 3 mm) may suffer from reduced fatigue resistance and thermal stability. The use of strain gauges also requires appropriate instrumentation and Wheatstone bridge configurations [45].

Compared to conventional motion analysis methods using optoelectronic sensors, real-time monitoring of kayakers on the water using inertial sensors and force sensors is an emerging solution. Several studies have delved into the issue of error factors related to strain gauges and other types of wearable sensors such as pressure sensors [89], which need to be considered during the design [43], [92], [93], [94], [95], [96], [97].

These studies have specifically addressed errors due to slight misalignment of the strain gauge between the gauge axis after bonding and the intended angle of deformation measurement, transverse sensitivity errors due to the orthotropic properties of the material, as well as deformation measurements near
<table><caption>TABLE VI
EXAMPLES OF COMMERCIAL SYSTEMS FOR ON-WATER KAYAK PERFORMANCE ANALYSIS</caption><thead><tr><td>System</td><td>Manufacturer</td><td>Communication</td><td>Remarks</td></tr></thead><tbody><tr><td>Vaaka Cadence Sensor [81]</td><td>Vaaka Cadence</td><td>Bluetooth and ANT+</td><td>Simple cadence-only solution; no force or kinematic data; not sufficient for advanced performance analysis.</td></tr><tr><td>Kayak Power Meter [50]</td><td>One GIANT LEAP</td><td>Bluetooth and ANT+</td><td>Provides accurate paddle force, paddle power data; no kinematic data; expensive and limited compatibility.</td></tr><tr><td>Allegro Kayak Cadence [90]</td><td>Exponential Performance</td><td>Wireless</td><td>Offers basic stroke rate tracking; does not provide force, acceleration, or coordination information.</td></tr><tr><td>Paddlemate [49]</td><td>Paddlemate</td><td>Bluetooth</td><td>Modular solution with multiple pods; provides force, cadence, and symmetry data via mobile app; no kinematic data; limited temporal resolution for high-performance analysis.</td></tr><tr><td>Motionize [80]</td><td>Motionize</td><td>Bluetooth</td><td>Visual feedback system for stroke symmetry; no direct force measurement; company no longer active, limiting long-term use and support.</td></tr><tr><td>Mini Phyling [72]</td><td>Phying</td><td>Bluetooth</td><td>Measures paddle force; no paddle kinematic data; no public API or open SDK for research use.</td></tr><tr><td>Maxi Phyling [72]</td><td>Phying</td><td>Bluetooth</td><td>Mounted on the boat to track GPS and IMU data; useful for overall kinematic analysis when synchronized with the Mini Phyling; not real time; requires dedicated analysis interface.</td></tr></tbody></table>

a free edge [93]. They have also examined errors related to the human factor and gauge factor, as well as reinforcement effects [98]. The selection of gauge size, resistance, and model has been studied [92], as well as the calibration process of the strain gauge itself [93]. Furthermore, the selection of the gauge, the physical environment, the bonding techniques, the instrumentation circuit, and the calibration of the entire strain gauge setup must be carried out by the user of the strain gauge.

Moreover, when using sensors and measuring devices on a kayak, it is crucial to take additional precautions: 1) ensure that the protective casings have a high IP rating, such as IP67 or IP68, and use waterproof seals, cable glands, and connectors; 2) select robust and shock-resistant casings to withstand frequent vibrations and impacts; 3) securely fasten the sensors and devices inside the casings to prevent excessive movement or falling; and 4) finally, make sure that the casings allow easy access to sensors, control buttons, and connection ports for adjustments or battery changes. Note that each manufacturer may provide specific recommendations for the installation and use of their products in marine environments.

Additionally, there are design drawbacks to consider. One such drawback is the relatively short data communication range, which requires users to have their smartphones with them on the water. Another drawback is that the independent use of these sensors may not allow for comprehensive analysis; they may need to be synchronized with other systems to measure footrest forces, boat kinematics, or athlete limb movements, for example. Due to these parameter and design issues, raw data are rarely available. In some cases, to access all the features and data tracking, an active subscription to the application is required.

## B. Challenges Related to Sampling Frequency

Physical quantities such as force exerted on the paddle or footrest are measured using sensors like strain gauges and processed through an instrumentation circuit with an amplifier, filter, and analog-to-digital converter (ADC) before processing on microcontroller unit (MCU) and transmission (Fig. 2). These signals are often weak (10–1000 µV [45]) and require amplification, necessitating careful handling of sampling and transmission frequencies.

The sampling frequency, tied to the ADC, dictates how often the analog signal is digitized. It is crucial for motion

reconstruction; higher frequencies offer better signal approximation but require more storage and power, whereas lower frequencies risk losing important details. The goal is to find the minimum necessary frequency to digitize the signal accurately while optimizing data transmission due to larger file sizes.

For kayaking, a competitive stroke rate is around 84 spm [99], reaching up to 180 spm in sprints [87], equating to 0.33 s/stroke, a value lower than the 0.5-s duration mentioned in the study by Strum et al. [5], which focused solely on the pull phase. In practice, most studies analyzing the performance of kayakers often use a 100-Hz sampling frequency [5], [100], providing about 33 samples per stroke for 180 spm. However, the paddle is submerged for only 0.1–0.2 s [88], meaning that only 10–20 samples are collected for this crucial phase. With these details about the kayakers’ motion, while 100 Hz may be adequate for general performance analysis, it may lack precision for detailed assessments. The e-kayak system uses a lower frequency of 50 Hz [3], [15], [85], [87], highlighting the need for improvement. This highlights another drawback of custom systems for real-time analysis on the water, in addition to the limitations related to simultaneously analyzed parameters.

Another issue affecting these wearable systems is the possibility of having different sampling rates in different nodes. For instance, Strum et al. [5], [100] used 150 Hz for paddle sensors and 100 Hz for the footrest. This approach is interesting for separately analyzing the forces exerted on the paddle and footrest, but it would not be suitable for comprehensive analysis. It is necessary to set a common sampling frequency among the different nodes potentially through data interpolation or resampling to be able to compare and effectively analyze the kayakers’ technique.

The sampling frequency can be limited by available memory space and hardware capabilities. In the development, data collection devices can include internal storage to record data locally, which is crucial in case of connectivity loss and generally more relevant for analysis than transmitted data. Studies on ergometers typically use higher recording frequencies than 100 Hz. For example, 1000 Hz was used to analyze leg drive force and boat velocity [4] and to assess paddlers’ technical skills [60], [79]. Other studies used 400 Hz [8], 256 Hz with FPaddle [2], and 512 Hz [1]. Currently,
<figure><img src="image_4.png" /><figcaption>Fig. 2. Signal processing pipeline for real-time physical quantity analysis: from sensor to display for the trainer or athlete.</figcaption></figure>

no real-time on-water kayaking performance analysis system exceeds 100 Hz.

Thus, addressing the challenges of sampling frequency involves optimizing the tradeoffs between detail, storage, power consumption, and transmission efficiency to enhance the accuracy and usability of kayak performance measurement systems.

### C. Preprocessing and Signal Conditioning

The signals collected from strain gauges, IMUs, and other onboard sensors are often affected by noise, offset, and drift, particularly in aquatic and high-dynamic environments such as kayaking. IMU data can suffer from cumulative drift and sensor noise, especially when integrating gyroscopic data for orientation tracking. Preprocessing is therefore critical to enhance signal quality and extract meaningful biomechanical parameters. To reduce noise and stabilize the signal, various filtering methods can be applied.

1) **Moving Average Filter:** The moving average filter (MAF) is one of the simplest filters used in embedded applications. It reduces high-frequency noise by averaging a fixed number of previous samples

$$y[n] = \frac{1}{N} \sum_{k=0}^{N-1} x[n-k]. \quad (2)$$

Despite its efficiency and simplicity, the MAF introduces latency and significantly smooths transitions, which can distort key dynamic events in paddle force signals. Moreover, its equal weighting across the window ignores the temporal dynamics of the system [101].

2) **Weighted Moving Average:** The weighted moving average (WMA) improves upon the MAF by assigning greater importance to more recent samples. This enhances responsiveness to sudden changes, making it better suited for systems requiring quick adaptation. However, its performance depends on empirically defined weights, which can introduce bias if not carefully tuned [102].

3) **Exponential Moving Average:** The exponential moving average (EMA) uses a recursive formula

$$y[n] = \alpha x[n] + (1 - \alpha) y[n-1], \quad \alpha \in (0, 1). \quad (3)$$

It is widely adopted in low-power real-time systems due to its minimal memory and computation needs. A properly selected $\alpha$ ensures a good tradeoff between noise suppression and signal responsiveness. Recent studies have shown that EMA can achieve denoising performance comparable to Kalman filters (KFs) under certain conditions, with significantly lower complexity [103], [104].

4) **Savitzky-Golay Filter:** Unlike previous filters, the Savitzky-Golay filter fits a polynomial over a sliding window, preserving higher order signal features. It excels in offline analysis for maintaining peak shape and slope, making it useful for precise phase detection in stroke cycles [105], [106], [107]. However, its computational cost limits its use in resource-constrained embedded systems.

5) **Kalman Filter:** The KF provides optimal recursive estimation under Gaussian noise assumptions. It is particularly effective for sensor fusion (e.g., IMU and force sensors) and adaptive correction. Nevertheless, its implementation requires accurate system models and is more demanding in terms of processing power [108], [109].

6) **Least-Mean-Square Filter:** The least-mean-square (LMS) filter is an adaptive technique that adjusts its coefficients based on the minimization of the mean squared error. It is suitable for real-time applications where noise statistics vary over time, but it requires a reference signal and careful tuning of the learning rate to ensure convergence and stability [110], [111], [112].

7) **Wavelet-Based Methods:** Wavelet denoising enables multiscale analysis and is effective in removing nonstationary noise while preserving important time-frequency features. It is particularly suited for offline analysis due to its high computational load and memory requirements [113], [114], [115].

8) **Empirical Mode Decomposition:** Empirical mode decomposition (EMD) decomposes a signal into intrinsic mode functions and is often used in conjunction with thresholding methods. However, recent evaluations suggest that EMD may struggle to remove random noise efficiently and can introduce artifacts when used alone [116], [117].

In practice, the choice of filter depends on the system constraints and the specific application. For embedded real-time kayak monitoring, EMA remains a preferred solution
due to its simplicity and low resource usage. Kalman and LMS filters provide superior adaptability but are best suited for high-performance or offline systems. Methods such as Savitzky-Golay, wavelet, and EMD are more appropriate for postprocessing, especially when preserving signal morphology is essential.

It should also be noted that recent improvements in IMU hardware have reduced the need for aggressive denoising. While high-performance IMUs mitigate drift and noise, they are typically more expensive. Therefore, selecting an appropriate filtering method requires balancing sensor quality, computational capacity, and energy efficiency.

## D. Feature Extraction and Stroke Event Detection

After preprocessing, feature extraction is a key stage in converting raw biomechanical signals into interpretable indicators of kayaking performance. Depending on the sensor type, force sensors, IMUs, or combined system relevant features can be derived in both the time and frequency domains. These features are then used for stroke segmentation, phase recognition, asymmetry analysis, fatigue detection, and performance feedback.

1) Force-Derived Features: From paddle and footrest force signals, common features include the following:

1) peak force, mean force, and impulse or stroke power (area under the force-time curve);

2) time to peak force and force application duration, and paddle wet time;

3) mean-to-peak force ratio, indicating curve shape (triangular versus rectangular);

4) force symmetry between left and right sides.

These features are typically extracted using threshold-based or peak detection methods [1], [6], which are computationally efficient and suitable for embedded systems. However, they are sensitive to signal variability, baseline drift, and individual technique differences.

2) Phase and Cycle Analysis: Stroke segmentation is commonly based on thresholding or peak detection, which are computationally efficient but susceptible to noise and sensitive to the athletes technique. Bonito et al. [6] used synchronized force profiles and a signal-based rule engine to segment each stroke based on sign changes and amplitude thresholds, then computed metrics per cycle.

3) Cadence and Rhythmic Analysis: Cadence (stroke rate) is a fundamental performance indicator. While most studies rely on simple peak counting or threshold crossings to estimate cadence, an alternative approach is the use of the autocorrelation function (ACF), which identifies dominant periodicity in signals such as paddle force or boat acceleration

$$R_{xx}(\tau) = \sum_{t=0}^{N-\tau-1} x(t) \cdot x(t + \tau). \quad (4)$$

The first nonzero peak of $R_{xx}(\tau)$ corresponds to the stroke period. ACF is threshold-free, robust to signal shape variability, and works well in quasi-periodic signals [60] and is suitable for embedded implementation without manual tuning. However, it may require a minimum signal duration and

struggles in irregular paddling (e.g., fatigue or turbulence). As of now, ACF has not been directly applied in the cited studies but represents a promising direction for stroke rate estimation in embedded kayak systems.

Alternative methods include zero-crossing detection (fast but noise-sensitive), short-time Fourier transforms (useful for time-frequency evolution), and machine learning models trained to detect paddling events.

4) Kinematic and IMU-Based Features: For IMU signals, extracted features include the following:

1) peak angular velocity and acceleration;

2) range of motion (ROM) of joints (e.g., torso, shoulder, and elbow);

3) stroke symmetry across limbs;

4) entropy or signal variance as proxies for movement stability.

In [118], joint kinematics were computed using quaternion-based IMU fusion. A sliding window strategy was applied for stroke segmentation, and time-frequency features (e.g., entropy and spectral energy) were used for phase classification. Feature selection via neighborhood component analysis (NCA) highlighted autocorrelation as the most discriminative metric, while athlete skill level was successfully classified using principal component analysis (PCA) and XGBoost.

5) Synchronization and Coordination Metrics: Higher performing athletes displayed synchronized peaks between paddle, footrest, and foot strap forces [6]. This led to the extraction of interlimb timing differences as new coordination features. Such synchronization analysis is valuable for technique evaluation and crew coordination in K2/K4 boats.

6) Time-Domain-Frequency-Domain and Model-Based Features: Feature extraction can rely on different approaches depending on the system goals and complexity.

1) Time-Domain Features: Directly extracted from the raw or filtered signals (e.g., peak force and stroke duration). These are computationally efficient and suitable for real-time systems.

2) Frequency-Domain Features: Extracted using transforms (fast Fourier transform (FFT) and wavelet) to capture rhythm, regularity, or fatigue-related frequency shifts. These are often used in offline analysis.

3) Model-Based Features: Derived from biomechanical or physical models of motion (e.g., inverse dynamics, estimation of power, and energy expenditure). These require more computation and often additional sensor input (IMUs and GPS).

The extraction and analysis of features represent the backbone of algorithmic interpretation in kayaking systems. While simple statistical features remain prevalent due to their low computational cost, the use of sensor fusion, frequency-domain analysis, synchronization metrics, and machine learning is expanding. The optimal approach depends on the intended application whether offline analysis for technique refinement or real-time feedback for training support and must balance performance, robustness, and deployability.
## E. Transmission Technology and Energy Constraints

Real-time analysis of kayaking performance hinges on reliable, low-latency communication across distributed sensor nodes typically placed on the paddle, boat, footrest, seat, and body segments. Wireless transmission enables full freedom of movement for the athlete while making critical data accessible to coaches and support systems. However, designing such communication systems requires navigating a complex tradeoff between five interdependent factors: communication range, data throughput, energy consumption, latency, and scalability.

Each of these factors presents specific constraints. Communication range and reliability can be affected by water surface reflections, waves, and electromagnetic interference, leading to signal degradation or packet loss. Latency, defined as the delay between data collection and reception, can hinder real-time feedback. High-frequency or multinode transmission may saturate available throughput, especially with raw IMU or force data. Energy consumption is a key limiting factor due to the use of battery-powered sensor nodes, requiring efficient duty-cycling and lightweight protocols. Finally, scalability becomes critical in multisensor setups and multiathlete scenarios, where synchronized and parallel data streams must be supported.

To address these challenges, two families of wireless technologies are typically considered: short-range Technologies, ideal for intraboat communication, and low-power wide-area network (LPWAN) technologies, better suited for long-range transmission to shore-based systems or cloud platforms.

1) **Short-Range Technologies:** Commercial and academic kayaking systems often rely on Bluetooth Low Energy (BLE) or Wi-Fi for intraboat data transmission [3], [53], [60], [100]. BLE offers low energy consumption, moderate data rates (up to 2 Mb/s), and supports star topologies with up to 20 nodes. However, its limited range (10–100 m) and susceptibility to packet loss over water surfaces limit its utility for on-boat or shore-based monitoring. For systems requiring real-time transmission and remote connectivity, Wi-Fi is preferred [3]. Wi-Fi extends the throughput capacity (up to several megabits per second), allowing real-time streaming of high-resolution data. Nevertheless, its high energy demand and reliance on infrastructure (e.g., onboard routers or shore-based relays) restrict its deployment in mobile kayaking scenarios [119].

2) **LPWAN Technologies:** In terms of energy efficiency and addressing coverage challenges over bodies of water, LPWAN technologies such as LoRaWAN, NB-IoT, IEEE 802.15.4g, and IEEE 802.11ah (Wi-Fi HaLow) offer compelling alternatives.

LoRaWAN is favored for its energy efficiency, support for hybrid deployments (private/public), and adaptive spreading factors that scale range versus bitrate [120], [121]. However, its typical bitrate (0.3–50 kb/s) is only suitable for transmitting low-frequency, preprocessed features (e.g., stroke count and summary metrics), not high-rate raw IMU or force signals. For instance, LoRa has been used in kayaking for remote command transmission [8], but a more robust protocol is necessary to aggregate data from all sensor nodes in a comprehensive instrumentation setup.

NB-IoT, operating over licensed cellular infrastructure, ensures high quality of service and low latency but suffers from high deployment and device costs and may be unavailable in rural zones without long-term evolution (LTE) coverage [120], [122].

When evaluating criteria such as data rate, range, and power consumption, the IEEE 802.15.4 standards (including 802.15.4-2015, 802.15.4e, and 802.15.4g) offer significant advantages. Specifically, 802.15.4g is preferred for applications requiring a balance of data rate, extended range, and energy efficiency [123], [124]. It provides an extensive range of up to 1 km [125], low power consumption (typically a few milliamperes), and data rates up to 880 kb/s [124].

Finally, IEEE 802.11ah (Wi-Fi HaLow) has recently emerged as a promising alternative. Operating in sub-gigahertz bands, it combines long-range capability (up to 1 km), moderate data rates (hundreds of kilobits per second to several megabits per second), and native IP compatibility [121]. Although its energy profile is higher than LoRa or 802.15.4g, its throughput and infrastructure compatibility make it attractive for near-real-time applications in distributed multisensor networks on water.

**Table VII summarizes tradeoffs across protocols in terms of data rate, range, latency, power, and cost. While LPWANs like LoRaWAN and Sigfox excel in battery life and cost, they lack the throughput and latency control for real-time coaching. Conversely, BLE and Wi-Fi offer low latency but at the expense of power and limited coverage. NB-IoT ensures QoS but may not be universally accessible and requires operator contracts.**

3) **Design Implications for Kayaking Systems:** BLE and Zigbee favor star or tree topologies with local gateways, while LoRaWAN and NB-IoT rely on public operator-based infrastructures with centralized cloud processing. IEEE 802.15.4g supports flexible deployments, enabling both private mesh and star topologies, scalable to large multinode systems. As discussed in [121] and [124], star topologies reduce multihop energy overhead, simplify synchronization, and ease integration with real-time systems.

Hybrid architectures are thus recommended and best match the constraints of kayak instrumentation. Considering the maximum dimensions of K1, K2, and K4 kayaks (approximately 5.2, 6.5, and 11 m, respectively), it is feasible to utilize BLE for intraboat communication among nodes (paddle, seat, and footrest) due to its low latency and energy efficiency over short distances. This setup allows for high-resolution signal analysis with local storage for post-session review.

For uplink communication to a base station or coach-side gateway, IEEE 802.15.4g offers an excellent tradeoff between energy efficiency, range (up to 1 km), and moderate data rates, making it ideal for transmitting key performance metrics or synchronized stroke events in multiathlete boats (K2/K4). Alternatively, IEEE 802.11ah (Wi-Fi HaLow) provides native IP support and higher data rates, offering a promising solution for scenarios requiring greater throughput or more direct integration with real-time cloud-based analytics.

Even with robust communication and power management strategies, maintaining accurate temporal alignment across distributed sensor nodes remains a critical challenge.
<table><caption>TABLE VII
SOME COMMON WIRELESS COMMUNICATION TECHNOLOGIES OPTIONS</caption><thead><tr><th>Feature</th><th>Standard</th><th>Frequency Band</th><th>Max Range</th><th>Max Rate</th><th>Data</th><th>Power Consumption</th><th>Latency</th><th>Deployment Cost</th><th>Topology</th></tr></thead><tbody><tr><td>Sigfox [122], [126]</td><td>Proprietary</td><td>Sub 1 GHz</td><td>50 km</td><td>600 bps</td><td></td><td>Very low</td><td>High</td><td>Low</td><td>Star</td></tr><tr><td>NB-IoT [121], [122]</td><td>3GPP</td><td>Sub 1 GHz</td><td>10 km</td><td>200 Kbps</td><td></td><td>Low</td><td>Moderate</td><td>High</td><td>Star</td></tr><tr><td>LoRaWAN [126]</td><td>LoRa Alliance</td><td>Sub 1 GHz</td><td>15 km</td><td>50 Kbps</td><td></td><td>Very low</td><td>High</td><td>Low</td><td>Star</td></tr><tr><td>Telensa [126]</td><td>Proprietary</td><td>Sub 1 GHz</td><td>4 km</td><td>62.5 Kbps</td><td></td><td>Low</td><td>Moderate</td><td>Low</td><td>Star</td></tr><tr><td>Weightless-P [126]</td><td>Weightless SIG</td><td>Sub 1 GHz</td><td>10 km</td><td>100 Kbps</td><td></td><td>Low</td><td>Low</td><td>Low</td><td>Star</td></tr><tr><td>D7AP [126]</td><td>DASH7 Alliance</td><td>Sub 1 GHz</td><td>5 km</td><td>166.667 Kbps</td><td></td><td>Low</td><td>Low</td><td>Low</td><td>Star or tree</td></tr><tr><td>NB-Fi [126]</td><td>WAVIoT</td><td>Sub 1 GHz</td><td>50 km</td><td>10 kbps</td><td></td><td>Low</td><td>Low</td><td>Low</td><td>Star</td></tr><tr><td>Nwave [126]</td><td>Proprietary</td><td>Sub 1 GHz</td><td>30 km</td><td>100 bps</td><td></td><td>Very low</td><td>High</td><td>Low</td><td>Star</td></tr><tr><td>RPMA [127]</td><td>Ingenu</td><td>2.4 - Sub 1 GHz</td><td>15 km</td><td>624 Kbps</td><td></td><td>Low</td><td>Low</td><td>High</td><td>Star</td></tr><tr><td>WiFi HaLow [121]</td><td>IEEE 802.11ah</td><td>Sub 1 GHz</td><td>1 km</td><td>347 Mbps</td><td></td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Star</td></tr><tr><td>Wi-SUN [124], [125]</td><td>IEEE 802.15.4g</td><td>Sub 1 GHz</td><td>1 Km</td><td>6.25 Kbps to 800 Kbps</td><td></td><td>Low</td><td>Low</td><td>Low</td><td>Star</td></tr><tr><td>UWB [128]</td><td>IEEE 802.15.4a/z</td><td>3.1-10.6 GHz</td><td>10 m</td><td>110 Mbps</td><td></td><td>High</td><td>Low</td><td>High</td><td>Star, Peer-to-peer</td></tr><tr><td>WiFi [128]</td><td>IEEE 802.11a/b/g</td><td>2.4/5 GHz</td><td>100 m</td><td>54 Mbps</td><td></td><td>High</td><td>Low</td><td>High</td><td>Star</td></tr><tr><td>Zigbee [128]</td><td>IEEE 802.15.4-2015</td><td>2.4 GHz</td><td>100 m</td><td>250 Kbps</td><td></td><td>Low</td><td>Low</td><td>Low</td><td>Star, Mesh</td></tr><tr><td>BLE [126]</td><td>Bluetooth SIG</td><td>2.4 GHz</td><td>100 m</td><td>2 Mbps</td><td></td><td>Low</td><td>Low</td><td>Low</td><td>Star, Peer-to-peer</td></tr></tbody></table>

## F. Time Synchronization Strategies

Effective synchronization mechanisms and timestamp management are essential to ensure consistency and interpretability of multisource data in both real-time and post-session analysis.

A typical K1 configuration may include 6–10 wireless nodes (e.g., paddle left/right, boat, seat, footrest, and body segments), with even more nodes in K2 or K4 settings potentially 20 or more per boat. Topologies may range from star networks (in K1) to tree or hybrid configurations (in K2/K4), where nodes may be grouped by athlete or region of the boat.

As described in [129] and [130], synchronization protocol needs to include tradeoffs in terms of precision, complexity, energy efficiency, and scalability. Based on the insights from these surveys and protocol benchmarks, three approaches emerge as particularly suited for embedded kayaking systems.

1) Reference broadcast synchronization (RBS) is a receiver–receiver method achieving excellent accuracy [131], with good energy efficiency and support for small local star topologies (e.g., paddle–seat coordination). However, its scalability is limited and algorithmic complexity is relatively high.

2) Timing-sync protocol for sensor networks (TPSN) is a sender–receiver protocol well-suited for structured or tree topologies, with low complexity and energy requirements. It is effective in static configurations (e.g., boat-fixed nodes) but less robust under dynamic reconfiguration.

3) Flooding time synchronization protocol (FTSP) is designed for large and dynamic multihop networks. FTSP offers high fault tolerance and scalability, making it well-suited for K2/K4 configurations or mobile-static sensor integration. Its main drawback is increased convergence time and communication overhead.

These three protocols reflect complementary tradeoffs: RBS for localized synchronization with high precision, TPSN for

structured intraboat hierarchies, and FTSP for distributed and robust coordination. A hybrid implementation may combine RBS or TPSN within BLE-based local clusters and use an FTSP-inspired strategy for intercluster alignment via a central coordinator.

Ultimately, the synchronization strategy must meet the constraints of real-time aquatic sports systems: submillisecond precision, resilience to node motion and dropouts, energy efficiency, and low computational overhead. The wireless sensor network (WSN) literature offers a rich foundation from which carefully tailored solutions can be derived for embedded sports performance systems.

## VII. PROPOSAL OF AN INTEGRATED APPROACH FOR COMPREHENSIVE ANALYSIS

To capture different aspects of kayak performance, it is recommended to use multiple types of sensors such as force sensors, IMUs, pressure sensors, and speed sensors. The data collected by the different sensors need to be integrated to obtain a coherent picture of performance [132], [133]. This can be achieved using data fusion techniques [132], [134], [135].

### A. Data Fusion Techniques and Real-Time Applicability

Achieving accurate and responsive performance analysis in kayaking requires sensor fusion strategies that balance computational efficiency, signal complexity, and the constraints of embedded systems. Among the many taxonomies proposed in the literature [132], [136], we focus here on the classification by algorithmic paradigm, system architecture, and processing level.

1) Algorithmic Paradigms: We distinguish three main algorithmic paradigms.

1) Probabilistic methods rely on Bayesian principles to estimate the posterior distribution of system states given
noisy sensor inputs [134]. Classical examples include the KF and its nonlinear extensions, such as the extended KF (EKF) [137] and the unscented KF (UKF) [138]. The UKF is often more robust than the EKF in navigation and space applications [139], and recent improvements in sigma-point selection have helped mitigate its computational overhead [140]. While these methods are effective under Gaussian assumptions and known models, more flexible nonparametric techniques such as particle filters (PFs) [141], sequential Monte Carlo (SMC), and grid-based approaches can handle complex, nonlinear systems but are often too computationally intensive for real-time embedded use.

2) *Adaptive and heuristic methods* offer lightweight alternatives when explicit probabilistic modeling is difficult or unavailable. Techniques such as gradient descent [142], [143], LMS, or recursive least squares (RLS) [144] adjust fusion weights online to account for nonstationary dynamics, noise, or sensor drift conditions typical like in on-water kayaking. These methods are particularly attractive for embedded microcontrollers due to their low memory and processing requirements. Notably, certain extensions of KFs can also be made adaptive, by adjusting noise covariance matrices in response to data statistics, often using fuzzy logic or performance indicators [134].

3) *Learning-based approaches* leverage data-driven models to learn fusion strategies directly from labeled sensor data. These include classical algorithms such as decision trees [145], [146], support vector machines (SVMs) [133], K-nearest neighbors (KNNs) [147], and multilayer perceptrons (MLPs) [147], as well as more complex architectures like convolutional neural networks (CNNs), recurrent models [long short-term memory (LSTM)], or artificial neural networks (ANN) [146]. Such models can capture nonlinear relationships and temporal dependencies but typically require large annotated datasets and considerable computational resources. Their deployment in embedded real-time systems is feasible primarily when lightweight or quantized implementations are used, such as TinyML models [148]. Some learning-based methods can also adapt online, blurring the line with adaptive approaches.

2) **Architectural Organization:** Fusion techniques can also be classified based on where the fusion takes place within the system architecture [134], [149]. Centralized fusion gathers all raw sensor data to a central unit (e.g., tablet), enabling high modeling precision at the cost of bandwidth and computation. In contrast, decentralized fusion allows each node to operate independently, minimizing communication but lacking global coherence. Distributed architectures introduce collaboration between nodes, enhancing robustness but requiring complex synchronization. Federated fusion assigns local estimators to each node, with a central node aggregating partial results well suited for multiathlete setups like K2/K4 [150]. Hierarchical fusion combines local (e.g., paddle level) and global stages, balancing modularity and efficiency, and is particularly relevant for real-time embedded kayaking systems.

3) *Processing Level:* Another perspective focuses on the level of abstraction at which data are fused [132], [136], [151]. Observation-level fusion operates directly on raw or pre-processed signals and allows precise, low-latency estimation, though it requires tight synchronization. Feature-level fusion combines extracted variables (e.g., peak force and cadence), reducing bandwidth and enhancing robustness. This may be appropriate in kayak nodes that extract metrics locally before transmission. Finally, decision-level fusion merges the outputs of independent classifiers, providing robustness and modularity in distributed systems, particularly when coordinating information across multiple subsystems.

Fig. 3 illustrates a hierarchical fusion architecture tailored to kayak instrumentation. Local fusion at each subsystem (paddle, boat, and kayaker) reduces data volume and supports real-time feedback, while a central node performs high-level analysis for stroke classification and crew coordination. Depending on the hardware and network configuration, observation-level fusion could be implemented locally (e.g., combining IMU and force data directly on the paddle), feature-level fusion may be performed at intermediate gateways to process metrics like cadence or impulse, and decision-level fusion could be used centrally to interpret multiathlete interactions, particularly in K2/K4 configurations. The choice of fusion algorithm also plays a key role; while learning-based or hybrid methods offer flexibility and modeling power, they are best suited for postprocessing or gateway-level computation. In contrast, heuristic or simplified probabilistic filters (e.g., EKF) remain the most practical options for in situ, real-time onboard implementation. Overall, this layered approach offers a scalable and adaptable framework for on-water performance analysis, combining performance assessment, signal quality, and system efficiency.

## B. Kayak Performance Assessment

1) **Comprehensive Performance Analysis:** By integrating various data such as paddle force, footrest force, paddle orientations, boat kinematics, kayaker body movements, heart rate, and other physiological parameters, a detailed and multidimensional view of performance could be obtained. For example, the following conditions hold.

1) *Efficiency of Each Paddle Stroke:* By analyzing the force applied on the paddle and footrests and correlating these data with boat kinematics, the efficiency of each paddle stroke could be evaluated. This could help determine if the force is effectively used to propel the boat or wasted due to inefficient movements.

2) *Energy Expenditure:* Combining heart rate, oxygen consumption, and body movements could allow calculating energy expenditure during the activity. This could help in planning training and recovery regimes.

3) *Balance and Stability:* Boat kinematics and body movements data could be used to evaluate the kayakers' balance and stability.

2) **Performance Optimization:** The following conditions hold.

1) *Optimal Equipment Use:* By analyzing paddle orientation and applied forces, equipment use could be
<figure><img src="image_5.png" /><figcaption>Fig. 3. Hierarchical sensor fusion architecture example for kayaking: local fusion occurs on each subsystem (e.g., paddle, boat, and kayaker), with global fusion performed centrally for stroke, equipment, and crew-level analysis. Benefits include improved performance assessment, signal quality, and system energy efficiency.</figcaption></figure>

optimized to maximize performance and minimize
unnecessary effort.

2) *Optimal Crew Configurations in K2 and K4:* For K2 and K4 teams, fusing data from all crew members could identify the most efficient configurations. By evaluating forces, paddling cadences, and synchronized movements, coordination and collective performance could be optimized.

3) *Real-Time Feedback:* Data fusion systems could provide real-time feedback to athletes and coaches. By combining and processing data from various sources, the amount of data to be transmitted in real time could be reduced. For example, instead of transmitting every raw data point, synthesized indicators and specific alerts could be sent, allowing for immediate adjustments during training or competition.

4) *Injury Prevention:* Data fusion from biomechanical and physiological sensors could help detect early signs of fatigue or excessive stress on certain body parts. By monitoring indicators such as heart rate, blood pressure, and paddling movements in real-time, injury risks could be identified, and training adapted to prevent damage.

### C. Signal Quality and Sensor Management

1) *Improving Data Accuracy:* No sensor or measurement system is perfect and errors or inaccuracies can occur. By fusing data from multiple sensors in a comprehensive analysis system, these errors could be correlated and compensated. For example, combining information from motion sensors, GPS, accelerometers, and heart rate monitors could provide a more accurate and complete view of the kayakers' performance, reducing potential errors from missing or inaccurate data from a single source.

Moreover, the kayaking environment is uncontrollable. Environmental variables such as wind, currents, and waves can affect performance and data accuracy. Data fusion could allow for these variations to be considered and provide a more robust and reliable analysis despite changing conditions.

2) *Managing a Large Number of Sensors:* For a comprehensive analysis of kayak performance, it is essential to manage a large number of sensors. Each sensor provides specific and valuable information, but their management requires rigorous synchronization and integration to ensure the coherence and relevance of the collected data. Using advanced data fusion techniques, multiple data sources could be effectively managed, and significant insights could be extracted by correlating information from different sensors.

### D. Energy Efficiency of Wireless Sensor Nodes

1) *Reducing Data Amount:* By fusing data from multiple sensors, the amount of data to be transmitted could be reduced. Instead of sending each individual measurement, a single composite data point representing relevant information extracted from different sensors could be transmitted. This could reduce the energy consumption required for data transmission.

2) *Inactivity Detection:* In addition to reducing the transmission load, data fusion can also support more efficient sampling strategies. By combining information from multiple modalities such as force sensors, accelerometers, and gyroscopes, it becomes possible to robustly detect extended periods of inactivity such as during resting phases between intervals or training sets. Instead of maintaining a constant high-frequency sampling rate throughout the session, sensor nodes can switch to a low power or low frequency mode when no significant activity is detected, e.g., when both acceleration and force signals remain below threshold for a defined duration. Sampling resumes automatically as soon as movement or effort is identified. This event-driven sampling approach, guided by fused sensor inputs, is particularly relevant in kayaking, where strokes and pauses alternate regularly. It ensures energy-efficient operation while preserving the temporal resolution needed for accurate performance monitoring.

## VIII. CONCLUSION

The comprehensive analysis of kayak performance, considering both dynamic and kinematic aspects, holds tremendous
potential for enhancing kayak training programs. Wearable sensors offer the opportunity to overcome many limitations of laboratory systems, provided they are small, lightweight, accurate, energy-efficient, and have adequate data transmission range, appropriate sampling rate, as well as the required resolution and measurement range.

This review integrates insights from biomechanics, embedded systems, and sports sciences to offer a detailed overview of current monitoring technologies. It outlines key biomechanical parameters, compares academic and commercial systems, and discusses recent advances in sensor technologies, sampling strategies, signal preprocessing, feature extraction, synchronization, and data fusion. This article also addresses critical implementation challenges, including communication constraints, power management, and system integration for real-time use.

Despite these advancements, significant obstacles remain. Issues such as sensor miniaturization, optimal placement and calibration, inconsistent processing pipelines, and limited interoperability still hinder widespread adoption. Moreover, open-water environments introduce specific constraints such as water-induced noise, synchronization across distributed nodes, and the need for energy-efficient transmission that must be addressed for reliable on-water analysis.

As technology continues to evolve, the future of kayak performance analysis appears promising, with opportunities to enhance both sensor design and measurement capabilities. Beyond dynamic and kinematic parameters, future research should prioritize the fusion of multimodal data combining biomechanical, physiological (e.g., heart rate and oxygen uptake), and environmental factors (e.g., wind and water flow) to enable more holistic performance insights. The integration of artificial intelligence and machine learning can further support the detection of hidden patterns, the identification of complex intervariable relationships, and the delivery of personalized training recommendations. In parallel, optimizing embedded processing pipelines and implementing adaptive sampling strategies will help reduce energy consumption and support more autonomous, long-lasting sensor deployments.

Establishing standardized frameworks for sensor placement, signal interpretation, and feedback delivery will be essential to facilitate cross-study comparison and interdisciplinary collaboration for the identification of novel perspectives, the resolution of complex challenges, and the stimulation of innovation. Moreover, these developments enhance the timeliness and quality of feedback loops between athletes, coaches, and technical staff, enabling more responsive decision-making and personalized training interventions. Ultimately, the continued development of accurate, lightweight, and context-aware systems will not only benefit kayaking but also set a precedent for wearable technologies in aquatic sports.

Given that the time differences between top finalists in paddling sports often amount to mere milliseconds, we contend that such a methodological approach may be decisive in securing podium finishes or medals for nations actively engaging in this line of research. As wearable devices become more advanced and tailored to in situ data collection, they are

transforming how kayakers prepare, perform, and progress, from daily training to elite racing.

## ACKNOWLEDGMENT

This work is the result of a collaboration between the Mouvement Sport Santé (M2S) Laboratory, the IRISA Laboratory, French Canoe-Kayak Federation (FFCK), and the coaches. The authors would like to express their gratitude to all those who contributed to this survey. Special thanks to our colleagues and collaborators, in particular Rémy Masson from Campus Sport Bretagne; and Hermann Le Marec, Guillaume Berge, and Yann Le Carrer from the FFCK for their valuable advice and comments throughout the research process.

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<figure><img src="image_6.png" /></figure>

Souébou Bouro (Member, IEEE) received the M.Sc. degree in electronics and telecommunication systems (ESTEL) from Université Côte d'Azur, Nice, France, in 2021. He is currently pursuing the Ph.D. degree with the IRISA Laboratory, Université de Rennes, Rennes, France.

From 2021 to 2022, he was a Research Engineer with the Electronics, Antennas and Telecommunications Laboratory (LEAT), Université Côte d'Azur, where he worked on LoRa-based localization systems integrating

machine learning techniques. His research interests include the design of embedded measurement systems for performance analysis in elite kayaking, focusing on wearable sensing, multisensor data fusion, and real-time signal processing, with an emphasis on energy efficiency and long-range wireless data transmission.

<figure><img src="image_7.png" /></figure>

Antoine Courtay received the Ph.D. degree in electronics from the Université de Bretagne Sud, Lorient, France, in 2008.

He is currently an Associate Professor (Maître de conférences) in Electronics at the Université de Rennes, Rennes, France. He has contributed to several projects involving real-time signal processing, low-power system design, and sensor-based applications. His research and teaching activities span the fields of algorithm design, computer architecture, and embedded electronic systems.
<figure><img src="image_8.png" /></figure>

**Guillaume Nicolas** received the Ph.D. degree in biomechanics (STAPS) from the University of Rennes 2, France, in 2006.

He is currently an Associate Professor (Maître de conférences) in Biomechanics of Movement at the University of Rennes 2. His research interests include human movement biomechanics, musculoskeletal system modeling, embedded analysis systems, and simulation of motor performance, particularly in sports applications.

<figure><img src="image_9.png" /></figure>

**Mickaël Le Gentil** received the Engineering degree in electronics and industrial computing (EII) from ENSSAT, Lannion, France, in 2012.

He joined the CAIRN Team, IRISA Laboratory, Université de Rennes, Rennes, France, where he developed expertise in embedded programming for wireless sensor networks and project management. He later worked at IRT b<>com, Lannion, on private 5G core networks. In 2023, he returned to the IRISA Laboratory as the Head of the Development Unit, supporting the GRANIT and TARAN teams in the development of energy-efficient embedded systems. He is currently a Research Engineer in Computer Science and Electronics at the Université de Rennes.

<figure><img src="image_10.png" /></figure>

**Nicolas Bideau** received the Ph.D. degree in mechanics from the University of Rennes 1, Rennes, France, in 2009.

He is currently an Associate Professor (Maître de conférences) in Biomechanics (STAPS) at the Université Rennes 2, Rennes. His research interests include mechanics, biomechanics, numerical modeling, motion analysis, and simulation of human movement.

<figure><img src="image_11.png" /></figure>

**Olivier Berder** (Member, IEEE) received the M.Sc. and Ph.D. degrees in electrical engineering from the University of Bretagne Occidentale, Brest, France, in 1999 and 2002, respectively.

After a postdoctoral position at the Speech and Sound Technologies and Processes Laboratory, Orange Labs, Lannion, France, he joined the University of Rennes, Rennes, France, in March 2005, where he is currently a Professor at IUT Lannion and the IRISA Laboratory. He is the Leader of GRANIT Team. He has co-authored more than 120 peer-reviewed papers in international journals or conferences and has participated in numerous collaborative projects funded by either European Union or French National Research Agency. His research interests focus on power management for mobile communications and wireless sensor networks.