A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities

Abstract

Wearable sensors (WS) play a vital role in health assistance to improve the patient monitoring process. However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. Therefore, the data analysis requires additional effort to reduce the overall problems in sports rehabilitation. The existing research difficulties are overcome by applying the proposed spatial data correlation with a support vector machine (SDC-SVM). The algorithm uses the hyperplane function that recognizes sportsperson activities and improves overall activity recognition efficiency. The sensor data are analyzed according to the input margin, and the classification process is performed. In addition, feature correlation and input size are considered to maximize the overall classification procedure of WS data correlation using the size and margin of the input and previously stored data. In both the differentiation and classification instances, the spatiotemporal features of data are extracted and analyzed using support vectors. The proposed SDC-SVM method can improve recognition accuracy, F1 score, and computing time for the varying WS inputs, classifications, and subjects.