Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference

<abstract> <p>Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.</p> </abstract>

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Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 3 publications

References 49 publications

Sensor placement determination for a wearable device in dual-arm manipulation tasks

Sensor placement determination for a wearable device in dual-arm manipulation tasks

Convolutional Neural Network-Based Human Activity Recognition for Edge Fitness and Context-Aware Health Monitoring Devices

Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms

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