An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
원문 초록
With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.
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SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
Label Leakage in Federated Inertial-based Human Activity Recognition
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
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