Machine-Learning-Based Methodology for Estimation of Shoulder Load in Wheelchair-Related Activities Using Wearables

Abstract

There is a high prevalence of shoulder problems in manual wheelchair users (MWUs) with a spinal cord injury. How shoulder load relates to shoulder problems remains unclear. This study aimed to develop a machine-learning-based methodology to estimate the shoulder load in wheelchair-related activities of daily living using wearable sensors. Ten able-bodied participants equipped with five inertial measurement units (IMU) on their thorax, right arm, and wheelchair performed activities exemplary of daily life of MWUs. Electromyography (EMG) was recorded from the long head of the biceps and medial part of the deltoid. A neural network was trained to predict the shoulder load based on IMU and EMG data. Different cross-validation strategies, sensor setups, and model architectures were examined. The predicted shoulder load was compared to the shoulder load determined with musculoskeletal modeling. A subject-specific biLSTM model trained on a sparse sensor setup yielded the most promising results (mean correlation coefficient = 0.74 ± 0.14, relative root-mean-squared error = 8.93% ± 2.49%). The shoulder-load profiles had a mean similarity of 0.84 ± 0.10 over all activities. This study demonstrates the feasibility of using wearable sensors and neural networks to estimate the shoulder load in wheelchair-related activities of daily living.

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

“…This study developed a machine learning method to estimate shoulder load in wheelchair-related activities of daily living using wearable sensors. The method was validated using data from 10 ablebodied participants and showed promising results [28]. Human activity recognition (HAR) based on wearable sensor data is a rapidly developing field with many practical applications.…”

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

“…In particular, using supervised ML models to circumvent biomechanical models that are computationally expensive has become commonplace. Numerous studies have reported ML applications for generating in vivo insights (e.g., joint and muscle loading) from either OMC or IMC inputs [28][29][30][31][32][33][34][35][36][37][38] or markerless methods [27]. Among ML approaches, deep learning models, including Convolutional Neural Networks (CNN) and, increasingly, Recurrent Neural Networks (RNN), have become popular in lowerextremity biomechanical analyses [26].…”