Development of spike train algorithms for physiotherapy assessment using deep learning approaches - Nor Rashid, Fadilla' Atyka (2021)
Physiotherapy nowadays has become a demanding medication for curing bones related injuries and pain to restore someone's to health in order to gain back the ability to cope with daily living tasks. As the technologies of sensors have risen, smart physiotherapy monitoring systems become trendy researches due to its potential to enhance the quality of physiotherapy assessment. However, varied sensor technologies of physiotherapy assessment have lacked versatility and robustness. This research proposed a spike train feature extraction for physiotherapy assessment to enhance the patient's progression. However, the concerns are how capable is spike trains in achieving high accuracy as other related works on recognising and assessing rehabilitation movements. In this context, spike trains are defined as sequences of recorded times when neurons fire spikes or also known as action potentials. This study implemented a spike train as a primary method of feature extraction that illustrated a significant pattern for each exercise performed.Three datasets, UI-PRMD dataset, K3Da dataset, and Self-Collected dataset have been adopted in the studies to be encoded into spike trains formal representation which resulting to an average of 415 spike patterns. Next, the patterns of raster plots were being trained as the input into a deep learning framework to evaluate the accuracy of the pattern's uniqueness. Furthermore, this study makes use of the occurrence of spikes' number, which is known as firing rate, to distinguish movements' correctness and being compared with the deep learning evaluation measures to prove the efficiency of deep learning prediction. The proposed framework achieved recognition rates of 99.44%, 98.21%, and 100.00% for UI-PRMD, K3Da, and self-collected datasets, respectively. These results proved that the proposed framework achieved targetable accuracy for all datasets trained with various CNN architectures. Next, the experimental results of physiotherapy assessment indicate that the correctness prediction by the proposed framework closely follows the ground-truth value for the movements. This study is among the first successful attempts of implementing spike train into a deep learning framework for a real-time-based rehabilitation session case study with promising results. Hence, spike train is the foremost choice as features that are hugely rewarding towards deep learning as it can visually differentiate each of the physiotherapy movements with unique patterns.