Prediction of shoulder load from wearable sensors for wheelchair related activities: A machine learning approach.
Shoulder problems like pain or pathology are highly prevalent in manual wheelchair users (MWU) with spinal cord injury, lead to limitations in participation, a reduced quality of life and are often associated with “shoulder overload” (Mercer et al., 2006). Laboratory based experiments have examined shoulder load for a variety of wheelchair related activities (WRA; van Drongelen et al., 2005), but no research has been conducted that investigates the actual shoulder load profile of MWU’s in daily conditions. Such a profile would help in understanding the relation of shoulder overload and shoulder problems and could thereby support clinical decision making in the prevention of shoulder problems. Inspired by recent work on machine learning (ML) prediction of joint load from wearable sensors, the current project's ultimate aim is to develop a generalizable ML algorithm that can predict shoulder load for a variety of WRA.
10 able bodied participants were trained before the actual measurement of the WRA of interest: wheelchair propulsion (WCP) at 0.56 and 1.1 m/s at 0%, 0.56 m/s at 6%, WCP in restricted space, short ramp 12% up and down, weight relieve lift, manual material handling with 2 kg and desk work. Participants were equipped with five Shimmer3 sensors on wheelchair, wheel, thorax, upper arm and forearm; EMG was collected from the biceps and medial deltoid muscles. An 8 camera Qualisys system was used to obtain kinematics conform ISB recommendations (Wu et al., 2005). A SmartWheel (Out-Front) for collecting propulsion kinetics replaced the original wheel of a standard Kuschal wheelchair. From laboratory kinematics and kinetics 3D shoulder joint reaction forces (JRF) were estimated with an OpenSim based musculoskeletal model (Wu et al., 2016) (MSM), which consequently served as target for the training of a variety of ML algorithms, using sensor data (acceleration, angular velocity, EMG) as input.
Results & Discussion
Starting with simple input (upper arm sensors only) and a linear neural network (NN, one input, one hidden, one output layer for total JRF; iteratively trained following a Leave One Subject Out approach, a R2 of around 60% was obtained between predicted JRF (NN) and target (MSM output), but results varied considerably over participants. In a next phase the complexity of the ML models was increased to deep learning models (recurrent NN) and more signals (e.g. forearm and thorax sensors) were added to the input, which, however, did not improve the overall performance considerably. Currently, it is explored whether using training the algorithms on individual datasets, for single tasks, can improve the performance. The explorative process will be presented and discussed in the light of the relevant results.
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van Drongelen, S., van der Woude, L. H., Janssen, T. W., Angenot, E. L., Chadwick, E. K., & Veeger, D. H. (2005). Glenohumeral contact forces and muscle forces evaluated in wheelchair-related activities of daily living in able-bodied subjects versus subjects with paraplegia and tetraplegia. Archives of Physical Medicine and Rehabilitation, 86(7), 1434-1440. https://doi.org/10.1016/j.apmr.2005.03.014
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