Using LSTM for Detection of Wrist Related Disorders

Abstract

We have build a device named RepaiR for strength measurements and isokinetic rehabilitation of the wrist joint. We have performed series of measurements on 25 healthy individuals and 10 patients with neuromuscular and traumatic impairments. Our initial goal was to verify that the measured data contain sufficient information to distinguish between healthy and not healthy subjects as a proof of concept. We have implemented Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) that processes the time structured measurements. LSTM effectively models the varying length of the input vector and the long term dependencies. We compare performances of our models on the data sets with varying minimal input vector lengths. We have proven that the measurements can be used to detect neuromuscular impairments and our best performing model worked with 77,6 % accuracy.