Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
Source abstract
Abstract
Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Context-Aware Activity Recognition Systems · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Information Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan
Department of Civil Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan
Research Center of Smart Construction, National Central University, Jhongli, Taoyuan 32001, Taiwan
Department of Building and Real Estate, Hong Kong Polytechnic University, Hong Kong, China
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