# IMU-based Joint Trajectories and Gait Contact Event Detection: A Vicon-Referenced Analysis

Ardito Annavaleria, Mario Spirito, Lorenzo Cavallaro, Franconi Benedetta, Parente Domenico, Vaccaro Riccardo, Traverso Simone, Boccardo Nicolò, Laffranchi Matteo

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Ardito Annavaleria, Mario Spirito, Lorenzo Cavallaro, Franconi Benedetta, Parente Domenico, et al. IMU-based Joint Trajectories and Gait Contact Event Detection: A Vicon-Referenced Analysis. 2026. [https://hal.science/hal-05528175v1](https://hal.science/hal-05528175v1) Preprint submitted on 26 Feb 2026

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# IMU-based Joint Trajectories and Gait Contact Event Detection: A Vicon-Referenced Analysis

Annavaleria Ardito*

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

annavaleria.ardito@iit.it

Mario Spirito

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

mario.spirito@iit.it

Lorenzo Cavallaro

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

lorenzo.cavallaro@iit.it

Domenico Parente

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

domenico.parente@iit.it

Benedetta Franconi

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

benedetta.franconi@iit.it

Giulia Bodo

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

giulia.bodo@iit.it

Riccardo Vaccaro

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

riccardo.vaccaro@iit.it

Simone Traverso<fnref n="1" />

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

simone.traverso@iit.it

Nicolò Boccardo<fnref n="1" />

Rehab Technologies Lab

textitItalian Institute of Technology

Genova, Italy

nicolo.boccardo@iit.it

Matteo Laffranchi

Rehab Technologies Lab

Italian Institute of Technology

Genova, Italy

matteo.laffranchi@iit.it

**Abstract**—Soft exoskeletons, also known as exosuits, provide lightweight and ergonomic gait assistance for mobility-impaired individuals, leveraging textile architectures for better compatibility and comfort compared to rigid exoskeletons. Precise lower-limb kinematics are required for gait phase recognition and adaptive torque delivery aligned with biomechanical needs. To achieve this, it is necessary to estimate lower-limb joint angles and to validate these estimates against a reference system to ensure accurate alignment between the measured kinematics and the user's actual movement.

In this work, we present a preliminary validation of a custom IMU-based angular-pattern estimator, comparing its segments (thigh, shank and foot) angles estimation against those obtained from a Vicon optical motion-capture system through a custom Bodybuilder model to establish reliability for real-time control. In addition, a first implementation of gait-event detection id introduced for the purpose of gait-phase analysis.

**Index Terms**—Exosuit, robotic rehabilitation, lower limb, gait analysis

## I. INTRODUCTION

Mobility disorders arising from musculoskeletal, neurological, or age-related conditions are increasingly prevalent and profoundly affect quality of life and functional independence [1], [3]. Among lower-limb impairments, conditions such as stroke, Parkinson's disease, and osteoarthritis negatively affect

gait and postural control, heightening fall risk and limiting the ability to perform activities of daily living [5], [6].

Rehabilitation aims to restore or enhance walking ability through the coordinated effort of patients, clinicians, and caregivers. However, the rising demand for individualized, intensive therapy highlights the need for technologically advanced solutions that can complement and strengthen conventional approaches [4], [9]. Within this context, wearable robotic systems, such as orthoses and exoskeletons, have emerged as tools to assist joint motion and promote neuromotor recovery. Yet, conventional rigid exoskeletons often remain bulky, expensive, and limited in ergonomics and user comfort [7], [8], [15].

To overcome these challenges, soft exoskeletons, or exosuits, have gained attention as a promising new class of assistive devices. By leveraging lightweight, textile-based architectures, exosuits offer improved biomechanical compatibility and wearability, particularly for users retaining partial lower-limb function [12]. Unlike rigid-frame exoskeletons, exosuits transmit assistive forces through garments, straps, and soft anchors arranged in parallel with the musculoskeletal system, minimizing rigid components and reducing distal inertia.

A fundamental requirement for effective user assistance in soft exoskeletons is accurate gait phase recognition, which provides the temporal foundation for synchronized torque support aligned with biomechanical demands, such as ankle push-off during late stance or hip flexion during early swing [14]. The first step toward this goal is gait analysis, beginning with the definition and computation of angular trajectories for the lower limb segments (thigh, shank, and foot). These

This work was supported by the Istituto nazionale per l'assicurazione contro gli infortuni sul lavoro (INAIL) under grant agreement PR23-RR-P1 FeatherEXO.

<footnote>* Corresponding author.</footnote>

<footnote n="1">¹ S. Traverso and N. Boccardo are with Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), via Morego 30, Genova, 16163, Italy.</footnote>
angular signals can be derived from wearable sensors like inertial measurement units (IMUs), which enable robust gait mapping and phase detection for real-time analysis and control [16], [19], [10]. To ensure reliability, these IMU-derived trajectories require validation against a gold standard, such as the Vicon optical motion capture system [18], or even systems as Microsoft Kinect [17].

In this work, we present a preliminary validation study for the FeatherExo project, which aims to develop a multi-joint exosuit designed to assist walking in individuals who are partially impaired, by comparing IMU-based angular patterns with Vicon data.

The Materials and Methods section details the experimental setup, the custom Vicon Model used, and lower limb trajectories selected for comparison. The Results and Discussion then quantify their similarities, demonstrating that IMU mapping coincides with the gold standard to support future gait monitoring and control implementation.

## II. MATERIALS AND METHODS

This analysis, developed in the context of the design of FeatherExo currently targeting hip extension assistance, focuses on the development of a IMU-based sensing prototype to be integrated into the exoskeleton for gait phase detection and tracking. We propose an analysis aimed at verifying the consistency between custom IMU-derived angular patterns and biomechanical reconstructions obtained from Vicon motion capture system, used as the reference. Additionally, the identification of key gait events for motion reconstruction is presented.

After this preliminary validation concerning compatibility between IMU-derived and Vicon angular patterns be confirmed, the FeatherEXO project control developments will focus on higher- and mid-level strategies.

The following section describes the experimental setup used for both the IMU and Vicon systems, and introduces the method adopted for event detection.

### A. Experimental Apparatus

The experimental setup consists of four 9-DoF Bosch BNO055 IMUs per limb mounted on dedicated straps, each paired with three optical markers Fig.1.

Both IMUs and markers are mounted on a custom designed and 3D-printed support, Fig.1a, to ensure identical placement throughout the trials.

The setup includes a pelvis strap with a box containing electronic components (Raspberry Pi and power bank), two thigh straps, and two tibia straps. The pelvis supports are positioned on the lateral sides of the box, while the thigh and tibia supports are placed on the dedicated straps, with the feet support located on the heel of the shoes, all attached via Velcro (Fig.1b). The orientation data collected by the tethered IMUs are transmitted via CAN bus to the Raspberry Pi, which processes the trajectories in real time during the test and stores them.

<figure><img src="image_2.png" /><figcaption>Fig. 1. (a) CAD model of the 3D-printed support with mounted IMU and optical markers. (b) Exosuit straps setup in neutral pose, showing support mounting positions and global/local reference frames.</figcaption></figure>

### B. Bodybuilder Vicon Model

To verify angular signals, rather than using pre-existing Vicon models (Plug-in Gait), a custom model was implemented in the BodyBuilder language to compute lower limb kinematics during gait.

Each IMU is equipped with three dedicated optical markers that ensure precise reconstruction of its local reference frame within the global Vicon system.

The first section of the model defines the Cartesian frame for each body segment: one of the three markers serves as the origin, with x and z directions derived from vector differences between the origin location and the other two markers' position, while the y axis is automatically generated via cross-product. Since it is essential that the marker-based reference frames share the same direction and orientation as the IMU reference frames, the local coordinate systems of the pelvis and feet are rotated to align with the direction and orientation of the thigh and shank frames (Fig.1b). This adjustment is necessary because the reference frames of all eight IMUs are defined consistently with those of the thigh and shank.

The application of this frame transformation through a rotation matrix ensures consistency across all segment reference systems and enables visualization of rotational motion relative to the sagittal plane about a common axis, which is crucial for subsequent computations of both relative joint angles and absolute segment orientations.

Frame consistency is ensured by applying the rotation described by the following homogeneous transformation matrix, around the z axis:

$$M'_{segment} = M_{segment} \cdot \begin{bmatrix} \cos \theta & -\sin \theta & 0 \\ \sin \theta & \cos \theta & 0 \\ 0 & 0 & 1 \end{bmatrix}$$

with $\theta = -90^{\circ}$ for right pelvis, $\theta = 90^{\circ}$ for left pelvis and $\theta = 180^{\circ}$ for feet.
Then, relative joint angles (hip, knee, ankle) are calculated using Cardan method where rotations occur sequentially about the local x, y, and z axes of the proximal segment.

The relative rotation matrix between two adjacent segments is expressed as:

$$R_{rel} = R_{prox}^{T} R_{dist} \quad (1)$$

where $R_{prox}$ and $R_{dist}$ represent the rotation matrices of the proximal and distal segments. Using the Cardan XYZ parameterization, the rotation matrix can be written as:

$$R_{rel} = \begin{bmatrix} c_y c_z & -c_y s_z & s_y \\ c_x s_z + s_x s_y c_z & c_x c_z - s_x s_y s_z & -s_x c_y \\ s_x s_z - c_x s_y c_z & s_x c_z + c_x s_y s_z & c_x c_y \end{bmatrix} \quad (2)$$

where $c_i = \cos(\theta_i)$ and $s_i = \sin(\theta_i)$. Subsequently, angles $(\alpha, \beta, \gamma)$ are obtained as follows:

$$\left\{ \begin{array}{l} \alpha = \arctan 2(-R_{32}, R_{33}) \\ \beta = \arcsin(R_{31}) \\ \gamma = \arctan 2(-R_{21}, R_{11}) \end{array} \right. \quad (3)$$

where $\alpha$ denotes flexion-extension (rotation about the x-axis), $\beta$ denotes abduction-adduction (rotation about the y-axis), $\gamma$ denotes internal-external rotation (rotation about the z-axis) [20].

The absolute segment rotations are similarly calculated relative to the global reference frame.

### C. Data Acquisition Protocol and Elaboration

Data collection followed a standardized protocol in which trials began with the subject standing upright in a neutral position for several seconds. This static phase served to set the zero reference prior to dynamic gait acquisition on a treadmill at different walking speeds, which were appropriately processed. From these data, the absolute angular values of the three key segments were extracted for preliminary analysis; these represent each segment's angular pattern relative to the sagittal plane only. Since the focus lies on flexion/extension and dorsi/plantar-flexion movements, the rotation component analyzed corresponds to the x-axis rotation, selected directly from the model outputs. Once exported, the frame-based progression was converted to a temporal scale calculated from the Vicon trial start time.

Prior to performing the comparison between the signals acquired from the Raspberry-based IMU system and those from the Vicon motion capture system, both datasets were subjected to filtering to ensure equivalent signal processing and noise reduction.

For the Vicon data, a high-pass filter with the same characteristics as the one implemented in the Raspberry acquisition code was applied [21]. This step was carried out to remove low-frequency drifts, thus maintaining consistency with the filtering applied to the IMU signals during acquisition. The IMU signals, which were already processed through the embedded high-pass filter [21], underwent additional low-pass filtering during post-processing with parameters identical to the Vicon

Nexus software, which uses a 6 Hz cutoff frequency for joint kinematic data. The filtering procedure was implemented in MATLAB using the `filtfilt` function, which applies the filter in both forward and reverse directions. This approach effectively cancels out the phase shift introduced by one-directional filtering, thereby preserving the temporal integrity of the signals. Although the main objective of the study was not the characterization of gait events, an additional analysis was performed to identify the beginning and end of the stance phase, and consequently the onset of the swing phase.

The detection of heel-strike (HS) and toe-off (TO) events was carried out using the vertical position and velocity of the foot marker. In this context, it is important to consider the variation of foot position along the vertical axis, since vertical displacement provides a reliable indication of the contact and lift-off of the foot from the ground. Specifically, heel-strike is defined as the velocity zero-crossing from negative to positive value, confirmed by a minimum in position to avoid false positives, as shown in the graphs of Fig.2, while toe-off is identified, between consecutive heel strikes, at the maximum foot velocity peak (computed as the derivative of position by Vicon Nexus), clearly visible in the first graph of Fig.2.

<figure><img src="image_3.png" /><figcaption>Fig. 2. Foot marker vertical kinematics: position and velocity trajectories.</figcaption></figure>

The two systems described below implement these detection rules:

$$HS(i) = \begin{cases} v_{\text{foot}}(i-1) < 0, \\ v_{\text{foot}}(i) > 0, \\ v_{\text{foot}}(i+1) > 0, \\ p_{\text{foot}}(i) = \min_{k \in K_{\text{HS}}^{(n)}} p_{\text{foot}}(k) \end{cases}$$

$$TO(i) = \begin{cases} i_{\text{HS}}^{(n)} < i < i_{\text{HS}}^{(n+1)}, \\ v_{\text{foot}}(i) = \max_{k \in K_{\text{TO}}^{(n)}} v_{\text{foot}}(k) \end{cases}$$
## Segment angular pattern comparison and events detection

<figure><img src="image_4.png" /><figcaption>Fig. 3. Comparison of IMU-based kinematic model-computed angles for the thigh, shank, and foot segments with motion-capture-reconstructed angles. Additionally, heel-strike and toe-off detection are showcased.</figcaption></figure>

with

$$ \begin{aligned} K_{\text{HS}}^{(n)} &= \{k | k \text{ around } i \text{ to find local minimum}\}, \\ K_{\text{TO}}^{(n)} &= \{k | i_{\text{HS}}^{(n)} < k < i_{\text{HS}}^{(n+1)}\} \end{aligned} $$

# III. RESULTS AND DISCUSSION

## A. Results

Both qualitative and quantitative analyses show that the IMU-based angle reconstruction is well aligned with the gold standard. The angle patterns are comparable in terms of shape, periodicity, and maxima and minima, as illustrated in Fig.3. Table I reports the RMSE evaluation, computed both over the entire signal and at the maxima and minima points.

<table><caption>TABLE I
RMSE OF SEGMENT TRAJECTORY REPRODUCTION.</caption><thead><tr><th>Segment</th><th>ε<sub>tot</sub> (°)</th><th>ε<sub>OnMax</sub> (°)</th><th>ε<sub>OnMin</sub> (°)</th></tr></thead><tbody><tr><td>Thigh</td><td>2.26</td><td>0.55</td><td>0.51</td></tr><tr><td>Shank</td><td>4.58</td><td>1.34</td><td>5.37</td></tr><tr><td>Foot</td><td>4.56</td><td>3.06</td><td>1.05</td></tr></tbody></table>

## B. Discussion

RMSE values presented in Tab.I, all below 6°, validate IMU accuracy relative to the Vicon gold standard across all segments, with measured amplitude ranges. Fig.3 reveals synchronized trajectory patterns: thighs exhibit smooth flexion-extension cycles, shanks show pronounced swing-phase peaks and feet display consistent plantar-dorsiflexion patterns mirroring Vicon references.

Percentage errors relative to these amplitudes are 6.27% for thigh, 6.71% for shank, and 5.42% for foot-satisfactorily low values within gait analysis standards (≤ 10%) [22], confirming IMU reliability.

The mismatch between the IMU-based and Vicon signal, observable in Fig.3, do not stem from the Vicon model itself but could mainly be related to the Vicon data reconstruction process and the experimental setup: when one or more markers are temporarily hidden from cameras during acquisition by the same user's limb, the Vicon software reconstructs the missing trajectories using its built-in gap filling methods, including cyclic filling, which estimates data assuming repetitive gait patterns from prior cycles, or body rigid filling, which calculates positions based on rigid transformations from neighboring markers on the same segment: both methods create estimated data rather than real measurements, potentially smoothing peaks or altering trajectory shape. Consequently, these reconstruction effects could explain the deviation observed [23].

This finding highlights the need to refine the experimental setup, particularly by increasing the number of cameras during motion capture, to reduce marker loss, especially on the tibia during the swing phase. The same analysis applied to the right leg yielded comparable results, confirming the bilateral consistency of the IMU-based kinematic estimation.

The validity of the events detection method, Fig.2, was also doubled checked through visual inspection of synchronized video recordings: a camera was specifically positioned to capture only the participant's feet, allowing direct confirmation of the timing of heel-strike and toe-off events. Additionally,
video inspection revealed a qualitative similarity between the segmental angular and velocity trajectory, and the vertical displacement and velocity profiles of the foot marker.

## IV. CONCLUSION

This preliminary validation establishes a promising foundation for advancing the FeatherExo soft exoskeleton development. Future research will pursue two primary directions, firstly aimed at refining signal processing methods, and secondly at expanding gait event recognition capabilities, both supporting the development of high-level control.

The qualitative similarity observed between foot marker position and segment angular rotation warrants quantitative investigation, this analysis aims to formalize the detection thresholds and conditions developed for Vicon foot signals, enabling their adaptation to IMU-derived data.

Furthermore, gait phase recognition capabilities will be expanded to encompass additional stance sub-phases, including foot flat and heel off events, complementing the established heel-strike and toe-off detection. This comprehensive gait cycle segmentation is essential for implementing future phase-specific control strategies, also for the motor control.

These developments will lead to an adaptive control framework leveraging IMU data exclusively, capable of delivering biomechanically synchronized assistance within a portable, real-time system architecture.

## ACKNOWLEDGMENT

A. Ardito prepared the manuscript, served as the corresponding author, and conducted the experiments and data analysis. M. Spirito was responsible for the methodology and intellectual ownership of the IMU and Vicon data processing. L. Cavallaro was responsible for the methodology and intellectual ownership of the Vicon modeling. G. Bodo contributed to the data analysis, manuscript editing, and critical review. D. Parente and B. Franconi actively contributed to the execution of the experiments and data collection. S. Traverso, R. Vaccaro, N. Boccardo, and M. Laffranchi supervised the research, provided essential resources and funding acquisition support, and contributed to the final revision of the work.

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