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# Real-Time Jump Activity Recognition and Jump Height Estimation using IMU

Maiva Schela Magnifouet Zefack, Jérôme Rocheteau

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Maiva Schela Magnifouet Zefack, Jérôme Rocheteau. Real-Time Jump Activity Recognition and Jump Height Estimation using IMU. 2025. hal-05109656

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# Real-Time Jump Activity Recognition and Jump Height Estimation using IMU

Maiva Schela MAGNIFOUET ZEFACK

Icam Nantes, 44470 Carquefou, France

Email: maiva.magnifouet@2025.icam.fr

Jérôme ROCHETEAU

LS2N, UMR 6004, F-44000 Nantes, France

Icam Ouest, 44470 Carquefou, France

Email: jerome.rocheteau@icam.fr

## Abstract—

Accurate recognition of jump activities and estimation of jump height are essential tasks in biomechanical analysis and sports performance research. This study introduces a real-time approach based on a low-cost Inertial Measurement Unit (IMU), to estimate jump height. Two state-of-the-art methods – Time of Flight (ToF) and Double Integration (DI) – are implemented and compared to evaluate their accuracy and applicability in real-world scenarios. Complementary signal processing techniques are also applied to reduce noise and improve reliability. The system is developed in collaboration with Les Neptunes de Nantes, a professional women's volleyball club in France, and aims at serving as a practical tool for field-based analysis. The results support the feasibility of using a wearable IMU to provide accurate real-time jumping activity recognition and jump height estimation.

## 1. Introduction

Volleyball is a sport where vertical jumps are essential for both offensive and defensive plays. Measuring jump height is a key metric for evaluating athlete performance, monitoring progress, and preventing injuries. Traditional solutions such as force plates and motion capture systems are highly accurate, but often expensive, bulky, and restricted to laboratory settings. Recent advances in wearable sensors, particularly inertial measurement units (IMUs), offer a promising alternative for jump analysis in real-world training conditions. However, challenges such as sensor noise, drift, and high cost still limit their widespread use in sports teams. Many commercial IMUs are relatively expensive and inaccessible for daily use by clubs and athletes. This study aims at developing and validating a real-time system for estimating jump height and recognition of jumps using a low-cost, reliable and custom-built IMU. The sensor includes a triaxial accelerometer and a triaxial gyroscope, which are used to implement two state-of-the-art estimation methods: Time of Flight (ToF) and Double Integration (DI). The system also integrates signal filtering techniques to improve accuracy and reduce the impact of noise. Beyond jump height estimation, our system is designed to recognize jumps performed by volleyball players and to count the number of jumps over time making it a complete solution for field-based performance tracking. The

study is conducted in collaboration with Les Neptunes de Nantes, a professional women's volleyball team in France, with the aim of providing them with a practical, affordable, and reliable tool tailored to their training needs. Then, the core research question consists in investigating whether affordable IMU-based devices and algorithms can provide accurate and reliable real-time jump height estimations when jump activities have been detected. We hypothesize that low-cost IMU can show a hight level of performance as well as for jumping activity recognition as for jump repetition counting. We investigate whether low-cost IMU can also offer performance comparable to laboratory-grade systems with regard to jump height estimation. The reminder of this paper is organized as follows. The section 2 presents scientific works related to both human activity recognition and jump height estimation. The section 3 presents dataset construction, model training and evaluation that makes us possible to perform jump activity recognition. The section 4 presents implementations of the 2 aforementioned state-of-the-art algorithms for jump height estimation. The section 5 presents the experiments in order to assess the 2 previous algorithms and discuss the analysis results. This source code related to this paper is publicly available¹.

## 2. Related Work

**Recognition of Jump Activity.** The automatic recognition of the activity of jumping and of the different kinds of jumps corresponds to the more generic field of Human Activity Recognition (HAR). HAR consists of automatically detecting which activity human beings are doing among a given set of activities. HAR is classically divided into camera-based HAR [1] and sensor-based HAR [2]. Sensor-based HAR corresponds to HAR whose input data are provided by sensors, smartphone-embedded ones or wearable ones, etc. Such sensors are mainly Inertial Measurement Units (IMUs) i.e. 3-axis accelerometers, 3-axis gyroscopes and 3-axis magnetometers. Sensor-based HAR is known to be more precise and fine-grained than camera-based HAR and it requires less computational resources which makes real-time processing possible. HAR mainly consists of time-series classification i.e. predicting the most probable activity (class) from timestamped data or multivariate time-series. In

1. See the repository at https://git.icam.fr/jerome.rocheteau/digisport.
fact, almost all works in HAR use Machine Learning (ML) or Deep Learning (DL) models. It has been shown in [2, Section 8] that classical ML classifiers such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Hidden Markov Model (HMM) perform well on small datasets or less-complex datasets whereas DL models such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) such as Long-Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) perform well on large datasets or more-complex datasets. Most of the recent works make use of DL models composed of both CNN layers and RNN layers sequentially [3, Section 3], [4, Section 2] or hierarchically [5, Section 3], with or without attention layer and with or without bidirectional layer [6, Section 3.3], [7, Section 4]. RNNs are proven to be useful for disambiguate relatively closed activities (such as open or close the door). RNNs also enhance activity detection confidence and RNNs can be trained over relatively small datasets (6h recording). Nevertheless, CNNs are also useful for sensor-based HAR. Sensor input data are merely times-series represented by number vectors that can be seen as 1D flatten matrices. In this case, 1D convolution layers have proven their capability to extract temporal features almost as well as RNNs [8, Section 3.2], [9, Table 5, Figure 5]. Another advantage of DL models over ML models consists of the automated feature extraction: whereas ML models pre-process input data in order to extract their features, DL models directly process input data, embed feature extraction and learn corresponding parameters. Finally, state-of-the-art HAR models currently achieve 0.95 of average accuracy; the best HAR models achieve more than 0.99 accuracy [2, Table 8], [10, Table 2]. It has been noticed that classification performances (accuracy, precision, recall, sensitivity and specificity) of the DL models are better than those of the ML ones, that classification performances of hybrid DL models composed of both CNNs and RNNs are better than those of DL models composed of either CNNs or RNNs, and that classification performances of bi-directional RNNs, either LSTM or GRU, are better than those of uni-directional RNNs.

The main drawback of HAR corresponds to the lack of available public datasets for HAR. In [1, Section 4], only 6 public SAR datasets are listed. In [2, Section 7, Table 6], only 15 public HAR datasets are listed. According to [2, Section 9], most of the studies (0.78) are based on experiments that are not reproducible as their datasets are not public. The reason why most of the studies are based on custom and private datasets because public and available datasets do not fit with the respective goals of these studies.

**Estimation of Jump Height.** The analysis of vertical jumps is essential for evaluating lower limb power and monitoring training progress in athletes. It is also relevant in rehabilitation contexts to track functional recovery [11, Section 1]. Various technologies have been used to determine and calculate jump height, ranging from 3D motion capture systems – considered the gold standard for accurately tracking movement and computing center of mass displacement,

but expensive and limited to laboratory environments – to force platforms, which directly measure ground reaction forces [12], [13, Section 1]. Although valid and portable, force plates can also be limited by their cost and the need for specific installation [14, Section 1]. Jump mats (or contact mats) like the VALD jump mat and optoelectronic systems like Optojump offer portable alternatives by estimating jump height based on flight time [15, Section 1]. 2D video is a low-cost option, though less precise. However, IMUs have become increasingly attractive due to their portability and low cost. IMUs measure acceleration and angular velocity in three dimensions, allowing jump height estimation through various methods [15], [16, Section 1]. A common approach uses flight time, derived from accelerometer data, to calculate height using ballistic equations [11, Section 1]. This method primarily relies on the accurate detection of take-off and landing moments [13, Section 2]. However, flight time under-estimates jump height compared to methods that measure the total displacement of the center of mass from the initial standing position, as it does not account for the upward movement of the center of mass before take-off due to ankle plantar flexion [16, Section 4.1]. Moreover, body posture during the flight phase can be a source of error for the FT method, as it assumes that the center of mass is at the same position at take-off and landing—which is rarely the case in practice. Another method involves double integration of the vertical acceleration signal to obtain velocity and displacement [15, Section 4]. It is considered one of the most reliable and accurate methods when applied to ground reaction force data from a force platform. However, this method can be sensitive to sensor drift and the accuracy of sensor alignment [16, Section 4.3].

Studies have validated IMU-based jump height measurements by comparing them with force platforms and motion capture systems, generally showing good correlation [14, Section 4.3]. Accuracy can vary depending on sensor placement (e.g. lower back, ankle, or foot), the algorithm used, and the type of jump [15, Section 1]. Commercial IMU devices such as Myotest and VERT have also been studied for their validity [17, Section 1]. Real-time jump height estimation using IMUs is an active area of research. On-board data processing enables autonomous analysis. Accurate identification of take-off and landing moments is crucial to estimate flight time and height. Comparative studies between IMUs and reference systems have shown promising results, although some biases and agreement limitations may exist. For example, some studies have compared Myotest with Optojump and force platforms for different types of jumps, such as squat jumps (SJ) and counter-movement jumps (CMJ), noting flight time overestimation by the accelerometer in some cases [13, Figure 5]. The importance of accelerometer placement and sampling frequency on measurement reliability has also been emphasized. In conclusion, IMUs offer a practical tool for jump analysis, but it is essential to consider the various calculation methods and validate the measurements against reference systems to ensure their accuracy specifically for real-time detection and estimation.
### 3. Jump Activity Recognition Models

This section explains both hardware and software components required to perform sensor-based HAR. It also explains how to perform jumping activity recognition.

**Devices, Gateway, Platform.** Sensor-based HAR mainly relies on IMU i.e. at least the product of accelerometer (3-axis acceleration), gyroscope (3-axis angular velocity) and, sometimes, magnetometer (3-axis magnetic field). As mentioned above, it makes it possible to recognize the most probable activity among a given set of activities from the movement features provided by such measurements by the means of Machine Learning. However, it requires that athletes be equipped by wearable IMU sensors. IMU devices that have been used are based on Adafruit Feather nRF52840 Sense boards equipped with a 180mAh battery and enclosed into a 54×34×10mm dimension case. Such IMU devices are programmed to broadcast the most measurements possible thanks to the Bluetooth Low Energy protocol. These measurements are received by a gateway that merely forwards them to the platform via Ethernet thanks to the MQTT protocol. Measurements are formatted by the gateways into messages compliant with the InfluxDB line protocol. In fact, the platform consists in 3 services: a MQTT broker, a Telegraf agent and a InfluxDB timeseries-oriented database. The MQTT broker forwards messages from the gateways to the Telegraf agent. The latter forwards the MQTT messages into the InfluxDB database. Data sampling rate averages 60Hz from IMU devices to the InfluxDB database. The latter makes it possible to process timestamped data and to provide aggregated data sequences for the sliding window technique easily upon which timeseries classification relies on. In fact, activity prediction is performed every 500ms from a last 2 seconds data that are aggregated every 20ms – i.e. at 50Hz – and the most probable predicted activity is published backward to the platform the same way as raw IMU data are.

**Data Acquisition, Datasets.** Such an architecture based on a timeseries-oriented database also makes it possible to compile IMU-based HAR datasets easily. It only requires another MQTT client that publish labeling messages accordingly. The aggregation feature of the timeseries database then enables to construct datasets – i.e. both the IMU data and the activity label – for a given sampling rate and with synchronized timestamps. Such MQTT clients can correspond either to manual activity labeling or to automatic activity labeling. For instance, in order to build IMU-based HAR datasets that fit with the two activities of jumping and standing, a first manual labeling program has been used that allows an annotator to label the accurate activity at the very same time a subject is performing the jumping protocol. Moreover, a second automatic labeling program that controls a SICK TiM561 Lidar sensor has been used in order to detect the presence or the absence of the subject on the jumping plates such that absence durations correspond to the time-of-flight of jumps.

The jumping protocol merely involves subjects to perform counter-movement jumps (CMJ) when the annotator tells them to jump. Each subject performs 5 series of 5 jumps with a 2-minute break between each series. A jumping session involves 3 subjects during 30 minutes. Subjects do not have requirements about the height of their jumps. Data were collected across 2 jumping sessions leading to 128,567 recordings that correspond to 42 minutes. Data are unbalanced with regard to labels as only 2 minutes and 20 seconds correspond to the label “jumping” whereas those of the label “standing” are associated to 33 minutes and 5 seconds. The remaining 6 minutes and 30 seconds correspond to interactivity between standing and jumping, i.e. when subjects were bending their legs before take-off and after landing. The annotator is instructed to start labeling “jumping” just before subjects were bending the legs for take-off and to stop the labeling right after subject were standing back on his legs while landing. A HAR dataset that consists of IMU features (accelerometer and gyroscope data) and SJ labels (for “standing” / “jumping”) is then constructed from these 10 jumping sessions by merging and aggregating data every 20ms and stored into CSV files. The figure 1 plots a sample of a CMJ jump.

<figure><img src="image_2.png" /><figcaption>Figure 1. IMU features and HAR labels of a CMJ jump</figcaption></figure>

**Model Training, Prediction.** A hybrid CNN and RNN sequential model has been trained over the set of time window sequences of 2 seconds long of accelerometer and gyroscope data aggregated every 20ms that are extracted from the dataset in order to predict probabilities of both the “jumping” label and the “standing” one. It then requires a 6 × 100 array as input, 6 variables from the IMU and 100 slices of 20ms aggregated values over 2 seconds, and it requires a 2 × 1 array as output, the probabilities of both activities jumping and standing. This model consists of 2 Conv1D layers, 1 max polling layer, 2 LSTM layers followed by a Dropout layer each, 1 Flatten layer and 2 Dense layers. Such a model proves to be 99% accurate over the SJ dataset. In fact, the 2 activities involves slightly different IMU signals as the figure 1 shows. The figure 2 displays,
<figure><img src="image_3.png" /><figcaption>Figure 2. SJ Model Training Progress and Confusion Matrix</figcaption></figure>

on the one hand, the training progress over the number of epochs for both metrics accuracy and loss that shows a perfect fit between the model and the dataset and, on the other hand, the confusion matrix that shows almost perfect accuracies, precisions and recalls. This SJ model is then used to predict the probabilities of both activities to happen from a 2 seconds long sequence of 20ms long aggregated accelerometer and gyroscope data. And this prediction is performed every 500ms such that it makes it possible real-time detection of CMJ jumps when it occurs. Once a CMJ jump is detected, the aggregated IMU data including those of accelerometer, gyroscope and magnetometer are directly provided to the jump height estimation algorithms.

## 4. Jump Height Estimation Algorithms

This section details the algorithms implemented to estimate the jump height from IMU worn by an athlete. It follows a multi-step process, from data acquisition and correction to the different height estimation methods.

**Data Acquisition and Correction.** As mentioned above, input data are provided from triaxial IMU recordings of acceleration ($a_x, a_y, a_z$), angular velocity ($g_x, g_y, g_z$), and magnetic data ($m_x, m_y, m_z$). The pre-processing steps consists of (1) data cleaning, (2) low-pass filtering and (3) sensor orientation correction. A crucial preliminary step is identifying and handling any missing or corrupted values in the raw data streams. To reduce the inherent noise in inertial measurements and obtain smoother signals crucial for integration, a low-pass filter is applied to acceleration and angular velocity data. In this implementation, a 4th-order Butterworth filter with a 10 Hz cutoff frequency was used. Low-pass filtering is a common technique to enhance accelerometer signal quality before analysis. Acceleration measured by an IMU is expressed in the local sensor frame, whose orientation changes during movement. To isolate the vertical acceleration relative to Earth's gravity, it is crucial to transform acceleration measurements into a stable global frame. This is done by estimating the orientation of the sensor, typically using a sensor fusion algorithm (e.g. the Madgwick filter) that combines accelerometer, gyroscope, and magnetometer data. Orientation estimates, often represented by quaternions, are then used to rotate the measured acceleration vector into the global reference frame, yielding corrected vertical acceleration $a_v$. This correction

is fundamental for accurate displacement estimates based on acceleration integration.

**Take-Off and Landing Detection.** Once the corrected vertical acceleration is computed, jump phase identification (take-off and landing) merely consists of the analysis of this signal. Take-off is generally characterized by a strong positive vertical acceleration when the athlete leaves the ground. Landing is marked by a peak in negative acceleration (impact) or a return to acceleration close to gravity. Algorithms detect these events by comparing the corrected vertical acceleration to predefined thresholds. In our implementation, take-off is detected when $a_v$ exceeds 30% of gravitational acceleration $g$, and landing when $a_v$ drops below 65% of $g$. The algorithm matches each take-off event with the nearest subsequent landing event to define the time interval of each jump. Filtering is applied to exclude detections that do not correspond to valid jumps (e.g. due to flight durations that are too short or too long).

**Jump Height Estimation Algorithmic Methods.** Two main algorithmic methods were implemented to estimate the height of detected jumps: the double integration method (DI) using corrected vertical acceleration and the Time-of-Flight method (ToF). It is important to note that jump height estimation may refer to different definitions: the elevation of the center of mass from an initial standing position (JH-1) or from the exact moment of take-off (JH-2). Most IMU-based methods from take-off estimate JH-2.

The Double Integration Method. This method relies on fundamental kinematic laws: velocity is the integral of acceleration, and displacement is the integral of velocity. Applied to corrected vertical acceleration data $a_v$, it allows the computation of vertical velocity $v_v$ and the vertical displacement $h_v$ of the sensor. The first numerical integration of $a_v$ over the jump time interval (from take-off to landing) gives the relative vertical velocity. To obtain the absolute vertical velocity, the initial vertical velocity $v_0$ at take-off must be added. Accurate estimation of $v_0$ is critical for this method. In this implementation, $v_0$ is estimated by integrating the acceleration before take-off.

$$h = \max \left( \iint_{t} a_{v}(t) dt \right)$$

The second numerical integration of vertical velocity (including $v_0$) over the jump interval provides the vertical displacement of the sensor relative to its position at take-off ($Z(t_0) = 0$). The maximum jump height is then identified as the peak of this vertical displacement $h_v$ reached during flight. Though theoretically straightforward, double integration of accelerometer data, even when orientation corrected, is extremely sensitive to sensor noise and bias, which can lead to significant drift in velocity and especially position estimates. The figure 3 shows the corrected vertical acceleration $a_v$ from the figure 1 IMU data as well as the vertical speed $s_v$ and the vertical height $h_v$ as the time-of-flight computed from the IMU data. Furthermore, a body-worn sensor does not exactly measure the acceleration of
<figure><img src="image_4.png" /><figcaption>Figure 3. Jump Height Estimation with the Double Integration method</figcaption></figure>

the global center of mass – the standard biomechanical reference point – because the human body is a deformable system. Studies comparing IMU devices (such as Myotest-V using this method) with force platforms or OptoJump systems have sometimes shown large random errors and lower repeatability than other methods.

The Time-of-Flight Method. This method is simpler and widely used. It estimates jump height *h* from the duration of the airborne phase, i.e., the time between take-off and landing. Assuming that gravity is the only acting force during flight (ideal projectile motion), the maximum height can be calculated using the ballistic formula:

$$h = \frac{g \times t^2}{8}$$

This formula assumes that the center of mass follows a

<figure><img src="image_5.png" /><figcaption>Figure 4. Jump Height Estimation with the Time-of-Flight method</figcaption></figure>

symmetric trajectory during flight and that it is at the same height at take-off and landing. The figure 4 illustrates the exponential variation of jump height estimation based on the ToF method. However, this condition is rarely met in practice due to body movements (e.g., tucking the legs before landing). Therefore, the accuracy of this method heavily depends on precisely detecting take-off and landing times. Validation studies with IMU sensors using the ToF method (e.g., Myotest-T) have shown a systematic overestimation

of height compared to reference systems like OptoJump or force platforms. This overestimation is often attributed to how the sensor detects the start and end of flight, e.g. based on velocity peaks rather than actual take-off / landing moments. Despite this systematic bias, the ToF method used with some IMU devices (Myotest-T) has shown good validity and reproducibility in certain studies.

## 5. Experiments, Results & Discussion

The experiments have been conducted to assess how reliable IMU are for estimating jump height using the DI and ToF methods. It involves 8 subjects, 6 women, 2 men. Each subject performs 3 or 5 series of 5 jumps according to the following protocol: standing 5 seconds, jumping, standing 5 seconds, etc until the 5 jumps were done, for a total of 180 jumps performed in 36 series. Every jumps of every series are labeled by a human annotator and were recorded using IMU devices and the SICK TiM561 Lidar sensor. The 30 last series were also recorded using a VALD jump mat used as the gold standard for the time-of-flight detection and therefore the jump height estimation. Each subject wore at least 1 IMU device in the back and, for 21 series, they wore 1 other IMU device at the waist. Then, it leads to 285 recordings of IMU signals of 180 jumps. However, 263 recordings only have been selected for analyzing jump height estimation due to inaccurate jump labels and 32 of these recordings are associated with measurements from the VALD jump mat. In fact, it has been tedious to relate data extracted from the VALD jump mat application with those of the IMU devices and the Lidar sensor.

<table><caption>TABLE 1. EVALUATION OF JUMP HEIGHT ESTIMATION</caption><thead><tr><th></th><th>MSE</th><th>MAE</th><th>RMSE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>jump repetition counting</td><td>0.001</td><td>0.024</td><td>0.003</td><td>0.993</td></tr><tr><td>lidar flight time</td><td>0.077</td><td>0.277</td><td>0.212</td><td></td></tr><tr><td>lidar time-of-flight</td><td>0.041</td><td>0.203</td><td>0.165</td><td></td></tr><tr><td>take-off index</td><td>0.001</td><td>0.002</td><td>0.002</td><td>0.999</td></tr><tr><td>landing index</td><td>0.001</td><td>0.002</td><td>0.002</td><td>0.999</td></tr><tr><td>time-of-flight</td><td>0.090</td><td>0.300</td><td>0.247</td><td></td></tr><tr><td>ToF jump height</td><td>0.131</td><td>0.362</td><td>0.283</td><td></td></tr><tr><td>DI jump height</td><td>0.151</td><td>0.389</td><td>0.299</td><td></td></tr></tbody></table>

The experiments had to assess how reliable both IMU-based jump height DI and ToF estimations are compared to those of the VALD jump mat. It also involved how reliable the time-of-flight, the take-off and landing detections of the jumps are compared to those of the VALD jump mat and the SICK TiM561 Lidar sensor. Therefore, it has to assess how reliable the flight time based on the Lidar sensor is compared to that of the jump mat. In addition, the repetition counting of jumps in every series is evaluated as an evaluation preliminary step. Three metrics – MSR, MAE and RMSE – are used in order to assess the performance of the different estimations, and the coefficient of determination R² is used to assess their quality. The table 1 summarizes the results of the different evaluation axes. Negative values of R² have not been reported.
**Jump Repetition Counting.** Analysis results for jump repetition counting are almost correct as only 2 errors occurred over the 66 series that comprise the selected 263 jumps. In fact, an over-estimation count by 1 occurred in 1 jump series which led to MSE and RMSE error metrics of 0.01 and a $R^2$ of 0.99. The IMU signal investigation suggest that the subject was moving between 2 jumps.

**Lidar Accuracy.** Analysis results show that the data frequency of the SICK TiM561 Lidar sensor of 40Hz is not high enough as error metrics are way too high as well as compared to the fight time of the VALD jump mat as compared to the IMU-based time-of-flight. It over-estimates by an average 55ms the flight time w.r.t. the jump mat.

**Take-Off and Landing Detection.** Analysis results show amazing standings for both take-off and landing detections. However, take-offs and landings are compared to those of the Lidar sensor which is not precise enough. In fact, the different time-of-flight computing based on the take-off and landing detections lead to large error metrics as well as with the ToF method as with the DI one.

**Method Comparison and Selection.** The choice between these methods depends on the objectives, available hardware, and required precision. The ToF method is computationally simple but sensitive to biases in flight phase detection and the assumption of symmetry. Double integration, potentially more accurate if drift and orientation issues are fully managed, is highly sensitive to integration errors and sensor placement. The literature shows that the validity and reliability of IMU-based jump height measurements can significantly vary depending on the calculation method, sensor placement, and sampling frequency.

## 6. Conclusion

This study demonstrates that accurate jumping activity recognition and jump repetition counting can be achieved using a low-cost, real-time IMU system with merely 50Hz data frequency. However, this study also demonstrates that state-of-the-art methods, such as Time of Flight (ToF) and Double Integration (DI), alongside effective signal processing techniques, did not manage to estimate jump height with a high level of accuracy. This is an expected outcome with challenges raised by sensor noise and integration drift.

The results highlight the need either of IMU sensors with higher data frequency up to 200Hz or of sensor fusion with, for example, Local Positioning System in order to obtain accurate estimates for jump height monitoring in professional volleyball. The results also highlight the need of integrated systems in order to link data automatically provided by several systems.

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