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A case study: effect of wrist sensor displacement on HAR performance using LSTM and attention mechanism

Wang, Xin; Wang, Yan; Lu, Chenggang; Yu, Hongnian; He, Hongli; Li, Zhikang


Xin Wang

Yan Wang

Chenggang Lu

Hongli He

Zhikang Li


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.

Presentation Conference Type Conference Paper (Published)
Conference Name 2021 International Conference on Advanced Mechatronic Systems (ICAMechS)
Start Date Dec 9, 2021
End Date Dec 12, 2021
Online Publication Date Jan 3, 2022
Publication Date 2022
Deposit Date May 12, 2022
Publisher Institute of Electrical and Electronics Engineers
Pages 103-108
Series ISSN 2325-0690
Book Title 2021 International Conference on Advanced Mechatronic Systems (ICAMechS)
Keywords HAR, sensor displacement, LSTM, attention mechanism
Public URL