A case study: effect of wrist sensor displacement on HAR performance using LSTM and attention mechanism
- Resource Type
- Conference
- Authors
- Wang, Xin; Wang, Yan; Lu, Chenggang; Yu, Hongnian; He, Hongli; Li, Zhikang
- Source
- 2021 International Conference on Advanced Mechatronic Systems (ICAMechS) Advanced Mechatronic Systems (ICAMechS), 2021 International Conference on. :103-108 Dec, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Wrist
Training
Performance evaluation
Mechatronics
Activity recognition
Data collection
Data models
HAR
sensor displacement
LSTM
attention mechanism
- Language
- ISSN
- 2325-0690
Loose wearing or self-placement usually causes sensor displacement, which can deteriorate the performance of classifiers in real use. As a case study, this paper focuses on investigating the effect of wrist-worn sensor displacement on human activity recognition. We construct a new HAR dataset from different positions of the wrist. We create a LSTM model and an multi-stage attention model for the evaluation of our three designed scenarios. Experimental results show that the classification accuracies are affected by sensor positions and the worst performance occurs when test data are from a new position for a model. In addition, the results also indicate the superior performance of the attention model on all the scenarios compared with the LSTM model.