Computation-Affordable Recognition System for Activity Identification using a Smart Phone at Home
Oscal Tzyh-Chiang Chen, Manh-Hung Ha, Yi Lun Lee · 2020
With consideration of the aging society, the homecare associated with seniors becomes a critical issue in which there is a need for activity recognition of subjects at home. In this work, we propose the computation-affordable recognition system which includes the pre-trained MobileNetV2, temporal bidirectional Gated Recurrent Unit (GRU), Finite State Machine (FSM), and Incremental Majority Voting (IMV), to identify four activities of standing, walking, sitting, and falling. To compromise the accuracy and complexity, the proposed recognition engine adopts the pre-trained MobileNetV2 on ImageNet accompanied with the temporal bidirectional GRU, named as MoBiG. The schemes of FSM and IMV are used to remove the infeasible activity state transitions and to preserve the reasonable continuous activity states, respectively. The application scenario is to adopt a portable camera locating at the living room to record daily activities which are identified by the proposed recognition system embedded on a smart phone. The experimental results reveal that the proposed recognition system yields an average accuracy of 93.1% where the MoBiG and FSM+IMV contributes 91.7% and 1.4%, respectively. Compared to conventional work, the recognition system proposed herein shows the well compromised performance between accuracy and complexity for homecare applications.