HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map

Ye Htet, Thi Thi Zin (), Pyke Tin, Hiroki Tamura, Kazuhiro Kondo and Etsuo Chosa
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Ye Htet: Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Thi Thi Zin: Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Pyke Tin: International Relation Center, University of Miyazaki, Miyazaki 889-2192, Japan
Hiroki Tamura: Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Kazuhiro Kondo: Faculty of Medicine, University of Miyazaki, Miyazaki 889-1692, Japan
Etsuo Chosa: Faculty of Medicine, University of Miyazaki, Miyazaki 889-1692, Japan

IJERPH, 2022, vol. 19, issue 19, 1-21

Abstract: Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).

Keywords: action recognition; depth colorization; e-Healthcare; Hidden Markov Model; Histogram of Oriented Gradients; older persons; person detection; Support Vector Machine; Viterbi Algorithm; YOLOv5 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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