Multi-person Daily Activity Recognition with Non-contact Sensors based on Activity Co-occurrence

Matsui, Tomokazu; Misaki, Shinya; Sato, Yuma; Fujimoto, Manato; Suwa, Hirohiko; Yasumoto, Keiichi

doi:10.23919/icmu50196.2021.9638856

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…To grant this wish, it is important to build an environment in which elderly people can understand their daily activities and manage their health conditions by themselves. There have been many studies on activities sensing/recognition technologies in the home and health condition monitoring using these technologies to build an environment for self health management and improve lifestyle habits of the elderly [1].…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate the effectiveness of the proposed method, we applied the method to the dataset [1] consisting of daily living data, biometric data, and stress status questionnaires (two questions in the morning and two questions in the evening) collected from five households of elderly people over 60 years old for one month. To confirm the validity of the per-activity biometric features, we compared the baseline method 1 (using 24-h RRI variance and Lorenz plot area [7,8] as features), the baseline method 2 (adding sleep time, which has been validated in the previous study [4], as a feature to the baseline method 1), and the proposed method (using RRI variance and Lorenz plot area for each activity type as features).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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

Self Cite

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“…Various technologies can be used [ 2 , 3 , 4 ]: vision systems (cameras) [ 5 , 6 , 7 , 8 ], wearable devices (with on-board accelerometers, pedometers, heart rate sensors…) [ 9 , 10 , 11 , 12 , 13 ], binary sensors (motion detectors, door/window sensors, pressure sensors, etc.) [ 14 , 15 , 16 , 17 , 18 , 19 ], which can be combined into larger devices (smart floors with pressure sensors to detect falls) [ 20 , 21 ], or sensors that can be interpreted as n-ary with thresholds (water flow sensors, electricity consumption sensors, etc.) [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition

Delaine

Faraut

2022

Sensors

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Monitoring Activities of Daily Living (ADL) has become a major occupation to respond to the aging population and prevent frailty. To do this, the scientific community is using Machine Learning (ML) techniques to learn the lifestyle habits of people at home. The most-used formalism to represent the behaviour of the inhabitant is the Hidden Markov Model (HMM) or Probabilistic Finite Automata (PFA), where events streams are considered. A common decomposition to design ADL using a mathematical model is Activities–Actions–Events (AAE). In this paper, we propose mathematical criteria to evaluate a priori the performance of these instrumentations for the goals of ADL recognition. We also present a case study to illustrate the use of these criteria.

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Preserving Data Utility in Differentially Private Smart Home Data

Stirapongsasuti,

Tiausas,

Nakamura

et al. 2024

IEEE Access

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Multi-person Daily Activity Recognition with Non-contact Sensors based on Activity Co-occurrence

Cited by 3 publications

References 35 publications

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

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

Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition

Preserving Data Utility in Differentially Private Smart Home Data

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