Sosnoff, J. J. | bioRxiv Science

bioRxiv2020

Calibration and Evaluation of a Force Measurement Glove for Field-Based Monitoring of Manual Wheelchair Users

Manual wheelchair users exhibit pain related to repetitive and demanding shoulder activities of daily living. Wearable sensing systems like force measurement gloves can provide insights about upper extremity loading in the community and home environments. We calibrated and evaluated the accuracy of a novel force measurement glove with a body-worn data logger. The device was calibrated with loads of 0-800 N applied to the palmar surface of the glove. Calibration conditions were tested that varied the stiffness of the material in the glove, the temperature, and the curvature of the force applicator. Calibration equations from each condition were evaluated by comparing the gloves force prediction with the output of an instrumented wheelchair rim during propulsion and weight relief exercises. The force measurement glove detected 72.7% of 355 propulsion peaks and had a strong linear correlation with the instrumented rim force measurements (r=0.80). The most accurate calibration equation was constructed using data from all conditions, with an RMS force measurement error of 64.7 N and 31.7 N for weight relief exercise and propulsion, respectively. The force measurement glove design described here may serve as a useful tool for detection of loading events and relative magnitude changes.

bioengineering↗

bioRxiv2018

Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures, A Machine Learning Approach

BackgroundBalance impairment affects over 75% of individuals with multiple sclerosis (MS), and leads to an increased risk of falling. Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk.\n\nMethodsThis secondary data analysis included 153 participants (50 controls and 103 individuals with MS) who underwent posturography based balance assessment (30s eyes open standing on a force platform) and physiological fall risk assessment (Physiological Profile Assessment - PPA). Participants were further classified into four subgroups based on fall risk: controls (n=50, 64.9 {+/-} 4.9 years old, PPA < 1); low-risk MS (n=34, 54.0 {+/-} 13.1 years old, PPA < 1); moderate-risk MS (n=27, 58.3 {+/-} 8.3 years old, 1 [≤] PPA < 2); high-risk MS (n=42, 56.8 {+/-} 9.7 years old, PPA [≥] 2). Twenty common sway metrics were derived following standard procedures, and subsequently used to train a machine learning algorithm (random forest - RF, with 10-fold cross validation) to predict individuals fall risk grouping. The feature importance from the RF algorithms was used to select the strongest sway metric for fall risk prediction.\n\nResults and DiscussionThe sway-metric based RF classifier had high classification accuracy in discriminating controls from MS individuals (> 86%). Sway sample entropy, a sway regularity metric, was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings. These findings may set the foundation for the development of guidelines for reporting balance impairment in individuals with MS.

bioengineering↗