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# Using Artificial Intelligence to Improve the Lives of Wheelchair Users

J. Charafeddine, Nadia Kichaa, Eric Monacellia

► To cite this version:

J. Charafeddine, Nadia Kichaa, Eric Monacellia. Using Artificial Intelligence to Improve the Lives of Wheelchair Users. 48th congress of the Société de Biomécanique., Oct 2023, Grenoble, France. pp.253-260, 10.1080/10255842.2023.2246304. hal-05032235

HAL Id: hal-05032235

https://hal.science/hal-05032235v1

Submitted on 22 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Using artificial intelligence to improve the lives of wheelchair users

J. Charafeddine$^{a,b}$, N. Kicha$^a$ and E. Monacelli$^a$

$^a$Université Paris-Saclay-UVSQ; $^b$Laboratoire d'Ingénierie des Systèmes de Versailles (LISV EA4048), Vélizy, France

## 1. Introduction

It is impressive that the virtual wheelchair offers a solution for regaining mobility for people with impairments and that it also offers a training program depending on the user's performance. However, measuring these performances overlooks one factor: the user's posture (Pouvrasseau 2019), which affects his stability and makes him fatigued (Aissaoui et al. 2001). By examining the seated person's behavior and how it relates to muscle fatigue, in this paper we highlight the relationship between wheelchair platform movement and the response of the person sitting on it. We also present a thorough analysis and evaluation of different machine learning algorithms for predicting physical fatigue in a virtual dynamic state scenario. The posture tracking system was implemented in the virtual wheelchair simulator developed at Laboratory of Systems Engineering of Versailles-LISV (Goncalves et al. 2014)

## 2. Methods

### 2.1. Data acquisition

We conducted an evaluation of the postural response of eight healthy subjects seated on an immersive simulator (Virtual wheelchair) equipped with a Kinect V2 sensor, which captures parts of the human body at a frequency of 30 Hz, a compact motion platform that reproduces rapid forward and backward movements through a scenario for approximately 10 min of successive ascending and descending slopes generated by the Unity 3D simulator, and a bipolar EMG sensor (Myon 3D) placed on the external oblique muscle of the seated person to measure muscle contraction. The recorded data of muscle activity by EMG sensors and the absolute position relative to the Kinect camera, restored in a CSV format file. Using this data, it is possible to measure the postural response and muscle contraction of the participant using MATLAB.

### 2.2. Signal processing

The measurements obtained with the EMG sensor must be processed to be usable. For signal processing, we used the root-mean-square method, which is well suited for bipolar EMG sensors because it allows both rectification and smoothing of the signal. They were bandpass filtered between 10 and 500 Hz using a sixth order Butterworth filter. After bandpass filtering, the processed EMG signal was normalized using the maximum voluntary contraction (MVE).

### 2.3. EMG and muscle fatigue

The Root Mean Square (RMS) values, which are variables related to the amplitude of the EMG signal, and the Median Power Frequency (MPF), which is related to the frequency content of the EMG signal, are often used to evaluate the onset and development of muscle fatigue (Rota 2013).

### 2.4. EMG classification

The task of moving our wheelchair was repeated three times, and the collected EMG data were divided into training and testing sections. Two sections were used for training the proposed classification model in MATLAB, while one section was used for testing.

(a) Binary indications of fatigue from the user were used to train our model (0 means no fatigue, 1 means fatigue). Thus, a valid set of labels as specified by the user would have the following form: `[0,0,0,0,0,,1,1,1,1,1,1,1,1]`, where each label corresponds to a sample detected by the EMG sensor. Therefore, SVM (Super vector machine) and KNN (K-Nearest Neighbors) classifiers can be trained to detect these changes in electrical activity and classify our given EMG signal as representative of a muscle fatigue state or not. This can be achieved by using signal feature extraction techniques (such as power spectral density (PSD), zero crossing rate (ZC), entropy, etc.) to extract relevant features from the EMG signal, which are then used as input to an SVM.

(b) Our model was trained in a different way using the Artificial Neural Networks (ANN-LSTM) (with the XTrain and YTrain data. XTrain stores the binary indications of fatigue from the user the EMG samples and YTrain stores the corresponding EMG label of that sample. The EMG label can be either non-fatigue or fatigue with a representation of 0 or 1, respectively. The variable XTest stores the test data of the EMG signal classified using the trained neural network, and the estimated label was stored in the variable YPred.
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<figure><img src="image_4.png" /><figcaption>**Figure 1.** Platform rotation's effect on body movement and EMG measurements.</figcaption></figure>

The neural model developed here consists of 800 hidden units and trained in an iterative process with epochs of up to 60 iterations. bandpass filtered between 10 and 500 Hz using a sixth order Butterworth filter. After bandpass filtering, the processed EMG signal was normalized using the maximum voluntary contraction (MVE).

## 3. Results and discussion

(a) Effects of the disorders on postural and muscular behavior: The result presented in the first subgraph (Figure 1) illustrates the relationship between the perturbations of the platform and the measurements acquired by the Kinect as a function of time (seconds). We observe that when the motion imposed by Unity, the platform becomes active and performs backward and forward rotations, which perturbed the camera measurement and shows an amplitude variation over time. This implies a strong relationship between the perturbation motion induced by the platform and the motion induced in the person sitting in the wheelchair, which appears in the balance between torso and platform rotations. that means that the presence of perturbation leads to a loss of balance manifested, among other things, by movements of the upper limbs of this person, which affects his muscle behaviour. The second subgraph of this figure relates the movement of the wheelchair to the EMG data (amplitude in volts as a function of time(s)). We see that the muscle contraction increases proportionally to the rotation of the platform.

Temporal analysis: to better understand the content of this EMG signal, the measurement of the MVE value is used as a reference to evaluate the effects of fatigue on muscle contraction. From the result graph, there is a variation in EMG amplitude

<figure><img src="image_5.png" /><figcaption>**Figure 2.** The relationship between frequency content and EMG activity.</figcaption></figure>

when the platform is mobilized, we can conclude that a fast recovery of maximum muscle force indicates that the body has efficiently recovered the energy against muscle fatigue, which means that the muscle is ready to perform another intense effort. In contrast, a slow recovery of maximal muscle force indicates that the body is still fatigued and the muscle (OE) needs more time to recover before it can perform another intense effort.

-Frequency analysis: we carried out fatigue tests, which involved subjecting the sample to varying levels of stress amplitude; which we note variation of EMG activity with frequency content (Figure 2) by calculating average frequency of the EMG power spectrum (MPF), this means that fatigue generally leads to a shift of the EMG power spectrum to lower frequencies, while higher frequencies can provide information about faster and more precise muscle contractions.

The result of this frequency analysis shows that the amplitude of the EMG signal increases with increasing frequency up to the cutoff frequency (fc = 0.217 Hz) and then decreases beyond this frequency. The increase in EMG activity that accompanies the increase or maintenance of frequency content corresponds to greater muscle excitation rather than fatigue. The increase in MPF observed during this exercise can be explained by the activation of fast-twitch muscle fibers involved in high-intensity muscle contractions. Conversely, the frequency of muscle action potentials detected by the EMG signal decreases. This is due to the fact that more easily fatigued muscle fibers tend to discharge more slowly, which may contribute to a decrease in the frequency of the EMG signal.

(b) The results of the training performance of the proposed network have been shown in Table 1.

The accuracy, sensitivity, and specificity of the SVM and KNN algorithms are shown to be 70% in the performance results, but the ANN algorithm
<figure><img src="image_6.png" /><figcaption>**Table 1.** Classification performance of models.</figcaption></figure>

<table><thead><tr><th>Algorithm</th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th></tr></thead><tbody><tr><td>SVM%</td><td>70</td><td>70</td><td>70</td></tr><tr><td>KNN%</td><td>70</td><td>70</td><td>70</td></tr><tr><td>ANN%</td><td>85.73</td><td>85.73</td><td>85.73</td></tr></tbody></table>

stands out with values of 85.73%, suggesting greater predicting capacity.

## 4. Conclusions

We discussed the postural behavior evaluation performed with the virtual wheelchair using a Kinect v2 camera and EMG sensor. The results show that the wheelchair user becomes unstable when disturbed, which affects his muscle behavior and leads to prolonged fatigue. In the context of muscle fatigue detection, we proposed two methods that allow us to classify the acquired signal into two classes 'fatigue' and 'non-fatigue' using supervised learning techniques such as SVM, KNN and ANN.

At a later stage, after applying the three-dimensional study to the muscles of the upper body, we can infer muscle fatigue from the movement without the need for EMG and develop an improvement strategy.

## ORCID

J. Charafeddine http://orcid.org/0000-0001-7732-3578

## References

Aissaoui R, Boucher C, Bourbonnais D, Lacoste M, Dansereau J. 2001. Effect of seat cushion on dynamic stability in sitting during a reaching task in wheelchair users with paraplegia. Arch Phys Med Rehabil. 82(2):274-281. 2001 Feb; doi:10.1053/apmr.2001.19473.

Goncalves F, Trenoras L, Monacelli E, Schmid A. 2014. Motion adaptation on a wheelchair driving simulator. 2014 2nd Workshop on Virtual and Augmented Assistive Technology (VAAT). IEEE; p. 17-22. doi:10.1109/VAAT.2014.6799463.

Pouvrasseau F, Charles S, Monacelli E, Routhier F, Malafosse B. 2019. Évaluation posturale sur fauteuil roulant : évaluation de faisabilité de la plateforme Virtual Fauteuil. Neurophysiologie Clinique. 49(6):416. doi:10.1016/j.neucli.2019.10.029.

Rota S, Rogowski I, Champely S, Hautier C, et.al. 2013. Reliability of EMG normalisation methods for upper-limb muscles. J Sports Sci. 31(15):1696-1704. doi:10.1080/02640414.2013.796063.

**KEYWORDS** Virtual wheelchair; EMG; supervised classifiers; muscle fatigue

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jinan.charafeddine@lisv.uvsq.fr