Enhancing Human Activity Recognition in Wrist-Worn Sensor Data Through Compensation Strategies for Sensor Displacement

Abstract

Human man Activity Recognition (HAR) using wearable sensors, particularly wrist-worn devices, has garnered significant research interest. However, challenges such as sensor displacement and variations in wearing habits can affect the accuracy of HAR systems. Two compensation strategies for sensor displacemnt are proposed to address these issues. The first strategy is hybrid data fusion, which involves merging sensor data collected from different displacement locations on the wrist. This technique aims to mitigate the discrepancies in data distribution that result from the multiple wearing positions along the wrist, thereby enhancing the overall accuracy of HAR models. The second strategy is cross-location transfer fine-tuning, which involves pretraining a model with data from typical wrist locations and then fine-tuning it with data from a new sensor location. This approach improves the model’s ability to adapt and perform accurately when the sensor is placed in a different position, significantly enhancing its performance and generalization capabilities. To verify the effectiveness of these proposed compensation strategies, we built an LSTM baseline model and introduce a new Multi-stage Feature Extraction (MSFE) model that integrates 1D CNN and attention. Experiments on common activities such as walking, standing, using stairs, and lying down, with data recorded at multiple locations along the wrist, have shown that both hybrid data fusion and cross-location transfer fine-tuning strategies notably improve the recognition accuracy of HAR models. The proposed MSFE model achieves higher recognition accuracies than the LSTM model in all six experimental scenarios, particularly in Scenario 5 involving sensor displacement, with an improvement of up to 31.65%. Additionally, thecross-location transfer fine-tuning strategy enhances the recognition accuracy by 9.19% for Subject 3 with sensor displacement at the right wrist location. These advancements in handling sensor displacement and wearing variations are crucial for developing more reliable and versatile wearable technologies.INDEX TERMS Sensor displacement compensation, human activity recognition, deep learning, wrist.

Keywords

• Sensor displacement compensation
• human activity recognition
• deep learning
• wrist