Body motion data registered by wearable sensors can provide objective feedback to patients on the effectiveness of the rehabilitation interventions they undergo. Such a feedback may motivate patients to keep increasing the amount of exercise they perform, thus facilitating their recovery during physical rehabilitation therapy. In this work, we propose a novel wearable and affordable system able to predict in real-time different postures of the upper-extremities (UEs) by classifying force myographic (FMG) signals of the forearm. An easy to use force sensor resistor strap (FSRS) to extract the UE FMG signals was prototyped. The FSRS was designed to be placed on the proximal portion of the forearm and capture the activities of the main muscle groups with eight force input channels. The non-kernel based extreme learning machine (ELM) classifier with sigmoid based function was implemented for real-time classification due to its fast learning characteristics. A test protocol was designed to classify in real-time six UE postures that are needed to successfully complete a drinking task, which is a functional exercise often used in constraint-induced movement therapy. Each participant repeated the drinking task three times. FSRS data and classification results were recorded for analysis. Six healthy volunteers participated in the test. The obtained results confirmed that the FMG data captured from the FSRS produced distinct patterns for the selected UE postures of the drinking task. With the use of the non-kernel based ELM, the postures associated to the drinking task were predicted in real-time with an average overall accuracy of 92.33% and standard deviation of 3.19%.

Read the full paper here