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.
Highlights
Introduction tivities of DailyLiving (ADL) Recog-The proportion of the elderly in the population in most advanced countries exceeds 15% [1], and the problems associated with aging including dementia and chronic illnesses are increasing year by year
This study develops a recognition syssystem for Activities of Daily Living (ADL) using Deep Neural Network (DNN) and compares it with the single layer BPNN, multi-layer tem for ADL using DNN and compares it with the single layer BPNN, multi-layer BPNN, BPNN, and Convolutional Neuron Networks (CNN) methods to evaluate the feasibility of the proposed system
A feasible detection system for ADL is beneficial for practitioners, especially for health care and safety applications designed to protect seniors from falling
Summary
Neural networks (NN) perform well when dealing with simple problems, such as back-propagation (BP), where choosing the appropriate features is important [15,16,17] This methodology may not be sufficient to deal with complex architectures and high-dimensional data. In 2012, the Google Brain team applied deep learning to process YouTube videos [13]. The Convolutional Architecture for Fast Feature Embedding (Caffe) is another typical deep learning framework [26]. It has the advantages of being easy to use, fast training, and modularity [26,27]. The above-studies demonstrate that DNN-oriented approaches and applications have become more efficient
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