Classification of human grasp forces in activities of daily living using a deep neural network

Abstract

The study of human grasp forces is fundamental for the development of rehabilitation programs and the design of prosthetic hands in order to restore hand function. The purpose of this work was to classify multiple grasp types used in activities of daily living (ADLs) based on finger force data. For this purpose, we developed a deep neural network (DNN) model using finger forces obtained during the performance of six tests through a novelty force sensing resistor (FSR) glove system. A study was carried out with 25 healthy subjects (mean age: 35.4±11.6) all right handed. The DNN classifier showed high overall performance, obtaining an accuracy of 93.19%, a precision of 93.33%, and a F1-score of 91.23%. Therefore, the DNN classifier in combination with the FSR glove system is an important tool for physiotherapists and health professionals to determine and identify finger grasp forces patterns. The DNN model will facilitate the development of tailored and personalized rehabilitation programs for subjects recovering of hand injurie and other hand diseases. In future work, prosthetic hand devices can be optimized to more accurately reproduce natural grasping patterns.