Roy, A.; Dutta, H.; Bhuyan, A.K.; Biswas, S. On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach. Sensors2024, 24, 4444.
Roy, A.; Dutta, H.; Bhuyan, A.K.; Biswas, S. On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach. Sensors 2024, 24, 4444.
Roy, A.; Dutta, H.; Bhuyan, A.K.; Biswas, S. On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach. Sensors2024, 24, 4444.
Roy, A.; Dutta, H.; Bhuyan, A.K.; Biswas, S. On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach. Sensors 2024, 24, 4444.
Abstract
This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit (IMU) sensors installed as wearable device. The objective is to learn classification of multiple classes of human activities in real-time. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based, and distance-based, are employed to achieve the desired classification performance. A comparative study of these strategies has been presented in terms of classifiability and classification accuracies. The proposed system is shown to be computationally efficient, which is relevant in the context of limited computing resources on typical wearable devices. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and other system parameters. Extensive experimentation and simulation study have been conducted on a multi-user human activity data from the public do-main in order to validate the proposed learning paradigm.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
