Analysis of Visualized Bioindicators Related to Activities of Daily Living

Matsui, Tomokazu; Onishi, Kosei; Misaki, Shinya; Suwa, Hirohiko; Fujimoto, Manato; Mizumoto, Takeshi; Sasaki, Wataru; Kimura, Aki; Maruyama, Kiyoyasu; Yasumoto, Keiichi

doi:10.1007/978-3-030-75100-5_62

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The impact of daily activities on stress may vary from person to person, because everyone’s favorite daily activities are different. Matsui et al [ 24 ] measured biological indices of housework during daily life in the home and aimed to obtain knowledge for the establishment of a QoL estimation method by estimating stress. We also conducted a subjective evaluation of the subjects’ preferences for housework by means of a questionnaire.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

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Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly

Matsumoto

Matsui

Suwa

et al. 2023

Sensors

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It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific activities (e.g., sleep duration, exercise duration/amount, etc.) as explanatory variables and do not consider all daily living activities. It is necessary to link various daily living activities and biometric information in order to estimate the stress state more accurately. Specifically, we construct a stress estimation model using machine learning with the answers to a stress status questionnaire obtained every morning and evening as the ground truth and the biometric data during each of the performed activities and the new proposed indicator including biological and activity perspectives as the features. We used the following methods: Baseline Method 1, in which the RRI variance and Lorenz plot area for 4 h after waking and 24 h before the questionnaire were used as features; Baseline Method 2, in which sleep time was added as a feature to Baseline Method 1; the proposed method, in which the Lorenz plot area per activity and total time per activity were added. We compared the results with the proposed method, which added the new indicators as the features. The results of the evaluation experiments using the one-month data collected from five elderly households showed that the proposed method had an average estimation accuracy of 59%, 7% better than Baseline Method 1 (52%) and 4% better than Baseline Method 2 (55%).

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Section: Discussion and Limitationsmentioning

confidence: 99%

“…It is useful to predict QoL from physical capacity and physical activity and daily living activity data. The biological approach is based on a physiological analysis using signals collected from ECG [ 22 ], skin electrical activity [ 23 ], RRI [ 24 ], and myoelectric sensors [ 25 ].…”

Section: Introductionmentioning

confidence: 99%