A segmented-mean feature extraction method for glove-based system to enhance physiotherapy for accurate and speedy recuperation of limbs

Abstract

It is always desirable to have an accurate system that allows fast recovery of patients undergoing physiotherapy in terms of integrated health and cost benefits. The caregivers and medical personnel too gain a lot of expertise through the innovations involved in treatment methodology. This system proposed here was developed successfully with a straightforward Segmented-Mean feature construction method that enables its portability to suit smart biomedical devices. In this work, four different exercises were completed by four different subjects in two sessions and the feedback system was generated from every single trial performance via a visual display in a smart phone. The accuracy of the system's output depends on the precise representation of two important things namely, correct gesture and timings. These two parameters have to be captured from the signals that are generated by the hand glove during the manual physiotherapy as guided by the experts during the teaching (i.e. training) phase. Any deviation from the model should also be captured and reflected in the feedback to align the physio-movements towards perfection to minimise adverse effects. So the feature has to be constructed with complete representation and obviously, as fast as possible. The proposed Segmented-Mean method calculates the mean of data that arrives from the significant electrodes periodically, thus preserving the performance of the subject and is found suitable in estimating the enactment of exercises and required deviations (if any), accurately and as appropriate. The proposed Segmented-Mean method helps the construction of features easily than other conventional methods by reducing the computational complexity and therefore, the response time. Hence, shifting the importance to actual physiotherapy monitoring with an accurate system that works on simple feature construction made feasible.