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

Abstract

Wearable sensors (WS) played 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 fuzzy data analysis requires additional effort to reduce the overall problems in sports rehabilitation. The research difficulties are overcome by applying the spatial correlation with a support vector machine (SDC-SVM). The algorithm uses the hyperplane function that recognizes the sports person activities and improves overall activity recognition efficiency. The sensor data is 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 data's spatiotemporal features are extracted and analyzed using support vectors. This proposed method can improve the accuracy of recognition, F1 score, and computing time for the varying WS inputs, classifications, and subjects.