A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 24; p. 9674
• Main Authors: Wang, Yina, Hao, Wenjie, Yu, Yanjun, Yang, Junyou, Yang, Guang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2023; MDPI
• Subjects: Accuracy; Analysis; bidirectional long- and short-term memory; Caregivers; Human body; Human mechanics; human three-dimensional model; individual differences; Kalman filters; Methods; multi-head attention; Neural networks; Robots; Sensors; Time series; transfer care; bidirectional long- and short-term memory; human three-dimensional model; transfer care; multi-head attention; individual differences
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23249674

The excretion care robot's (ECR) accurate recognition of transfer-assisted actions is crucial during its usage. However, transfer action recognition is a challenging task, especially since the differentiation of actions seriously affects its recognition speed, robustness, and generalization ability. We propose a novel approach for transfer action recognition assisted by a bidirectional long- and short-term memory (Bi-LSTM) network combined with a multi-head attention mechanism. Firstly, we utilize posture sensors to detect human movements and establish a lightweight three-dimensional (3D) model of the lower limbs. In particular, we adopt a discrete extended Kalman filter (DEKF) to improve the accuracy and foresight of pose solving. Then, we construct an action prediction model that incorporates a fused Bi-LSTM with Multi-head attention (MHA Bi-LSTM). The MHA extracts key information related to differentiated movements from different dimensions and assigns varying weights. Utilizing the Bi-LSTM network effectively combines past and future information to enhance the prediction results of differentiated actions. Finally, comparisons were made by three subjects in the proposed method and with two other time series based neural network models. The reliability of the MHA Bi-LSTM method was verified. These experimental results show that the introduced MHA Bi-LSTM model has a higher accuracy in predicting posture sensor-based excretory care actions. Our method provides a promising approach for handling transfer-assisted action individual differentiation in excretion care tasks.