Analysis on Nursing Care Activity Related Stress Level for Reduction of Caregiving Workload

Miyaji, Atsushi; Matsui, Tomokazu; Zhong, Zhihua; Choi, Hyuckjin; Fujimoto, Manato; Yasumoto, Keiichi

doi:10.1145/3458744.3473346

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…The proposed method is less burdensome on users than the existing studies [ 13 , 42 ] because it is completed only by wearing a wearable device. Few studies combine daily living activity and biometric indicators.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Various systems are being researched to improve estimation accuracy. To analyze stress in workers, not only smartwatches, but also chest-mounted heart rate sensors (e.g., WHS-3 [ 41 ]) have been used [ 42 ]. However, systems that use multiple wearable sensors place a high burden on users and are not suitable for real-time stress detection in daily living [ 43 ].…”

Section: Related Researchmentioning

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%

Section: Related Researchmentioning

confidence: 99%

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

Matsumoto

Matsui

Suwa

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, various systems are being researched to improve the prediction accuracy. To analyze stress in workers, not only smartwatches but also chest-mounted heart rate sensors (e.g., WHS-3 [15]) have been used [16]. However, systems that use multiple wearable sensors place a high burden on the subject and are not suitable for real-time stress detection in daily living.…”

Section: Stress Estimationmentioning

confidence: 99%

Stress Prediction Using Per-Activity Biometric Data to Improve QoL in the Elderly

Matsumoto

Matsui

Suwa

et al. 2022

Lecture Notes in Computer Science

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To improve the QoL of the elderly, it is essential to predict their stress states. In general, the stress state varies from day to day or time to time depending on what activities are performed and how long/strong. However, most existing studies predict the stress state using biometric data and specific activities (e.g., sleep time, exercise time and amount) as explanatory variables, but do not consider all daily living activities. Therefore, it is necessary to predict the stress state by linking various daily living activities and biometric information. In this paper, we propose a method to improve the prediction accuracy of stress estimation by linking daily living activities data and biometric data. Specifically, we construct a machine learning model in which the objective variable is the result of a stress status questionnaire obtained every morning and evening, and the explanatory variables are the types of daily living activities performed in the 24 h prior to the questionnaire and the feature values calculated from the biometric data during each of the performed activities. The results of the evaluation experiments using the one month data collected from five elderly households, show that the proposed method (using per-activity biometric features) improves the prediction accuracy by more than 10% from the baseline methods (with biometric features without considering activities).

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Analysis on Nursing Care Activity Related Stress Level for Reduction of Caregiving Workload

Cited by 2 publications

References 8 publications

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

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

Stress Prediction Using Per-Activity Biometric Data to Improve QoL in the Elderly

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