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Article

Toward Capturing Scientific Evidence in Elderly Care: Efficient Extraction of Changing Facial Feature Points

1
Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan
2
Department of Data & Innovation, Kochi University of Technology, 185 Miyanigutu, Tosayamada-cho, Kami-shi 782-8502, Japan
3
RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Hirayama, K.; Saiki, S.; Nakamura, M. Capturing User-Defined Facial Features for Scientific Evidence of Elderly Care. In Proceedings of the PerFlow 2020, Online, 23 March 2020.
Academic Editor: Alicja Wieczorkowska
Sensors 2021, 21(20), 6726; https://doi.org/10.3390/s21206726
Received: 30 August 2021 / Revised: 25 September 2021 / Accepted: 5 October 2021 / Published: 10 October 2021
(This article belongs to the Special Issue Analytics and Applications of Audio and Image Sensing Techniques)
To capture scientific evidence in elderly care, a user-defined facial expression sensing service was proposed in our previous study. Since the time-series data of feature values have been growing at a high rate as the measurement time increases, it may be difficult to find points of interest, especially for detecting changes from the elderly facial expression, such as many elderly people can only be shown in a micro facial expression due to facial wrinkles and aging. The purpose of this paper is to implement a method to efficiently find points of interest (PoI) from the facial feature time-series data of the elderly. In the proposed method, the concept of changing point detection into the analysis of feature values is incorporated by us, to automatically detect big fluctuations or changes in the trend in feature values and detect the moment when the subject’s facial expression changed significantly. Our key idea is to introduce the novel concept of composite feature value to achieve higher accuracy and apply change-point detection to it as well as to single feature values. Furthermore, the PoI finding results from the facial feature time-series data of young volunteers and the elderly are analyzed and evaluated. By the experiments, it is found that the proposed method is able to capture the moment of large facial movements even for people with micro facial expressions and obtain information that can be used as a clue to investigate their response to care. View Full-Text
Keywords: scientific long-term care; facial expression; points of interest; changing point detection scientific long-term care; facial expression; points of interest; changing point detection
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MDPI and ACS Style

Hirayama, K.; Chen, S.; Saiki, S.; Nakamura, M. Toward Capturing Scientific Evidence in Elderly Care: Efficient Extraction of Changing Facial Feature Points. Sensors 2021, 21, 6726. https://doi.org/10.3390/s21206726

AMA Style

Hirayama K, Chen S, Saiki S, Nakamura M. Toward Capturing Scientific Evidence in Elderly Care: Efficient Extraction of Changing Facial Feature Points. Sensors. 2021; 21(20):6726. https://doi.org/10.3390/s21206726

Chicago/Turabian Style

Hirayama, Kosuke, Sinan Chen, Sachio Saiki, and Masahide Nakamura. 2021. "Toward Capturing Scientific Evidence in Elderly Care: Efficient Extraction of Changing Facial Feature Points" Sensors 21, no. 20: 6726. https://doi.org/10.3390/s21206726

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