Beacon-Based Time-Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes

Morita, Tatsuya; Taki, Kenta; Fujimoto, Manato; Suwa, Hirohiko; Arakawa, Yutaka; Yasumoto, Keiichi

doi:10.1155/2018/2625195

Cited by 14 publications

(9 citation statements)

References 44 publications

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“…For example, there are many systems in which radio waves are used. These include activity and gesture recognition systems using Wi-Fi [ 26 , 27 , 28 ] or radio frequency identification (RFID) systems [ 29 , 30 , 31 , 32 ] and a healthcare reporting generation support system that is designed for nursing homes that uses Bluetooth low energy (BLE) components [ 33 , 34 ]. However, because these systems are directly affected by radio wave reflections, they need to be reconfigured every time the environment or device location changes.…”

Section: Related Workmentioning

confidence: 99%

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

Matsui

Onishi

Misaki

et al. 2020

Sensors

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As aging populations continue to grow, primarily in developed countries, there are increasing demands for the system that monitors the activities of elderly people while continuing to allow them to pursue their individual, healthy, and independent lifestyles. Therefore, it is required to develop the activity of daily living (ADL) sensing systems that are based on high-performance sensors and information technologies. However, most of the systems that have been proposed to date have only been investigated and/or evaluated in experimental environments. When considering the spread of such systems to typical homes inhabited by elderly people, it is clear that such sensing systems will need to meet the following five requirements: (1) be inexpensive; (2) provide robustness; (3) protect privacy; (4) be maintenance-free; and, (5) work with a simple user interface. In this paper, we propose a novel senior-friendly ADL sensing system that can fulfill these requirements. More specifically, we achieve an easy collection of ADL data from elderly people while using a proposed system that consists of a small number of inexpensive energy harvesting sensors and simple annotation buttons, without the need for privacy-invasive cameras or microphones. In order to evaluate the practicality of our proposed system, we installed it in ten typical homes with elderly residents and collected the ADL data over a two-month period. We then visualized the collected data and performed activity recognition using a long short-term memory (LSTM) model. From the collected results, we confirmed that our proposed system, which is inexpensive and non-invasive, can correctly collect resident ADL data and could recognize activities from the collected data with a high recall rate of 72.3% on average. This result shows a high potential of our proposed system for application to services for elderly people.

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Section: Related Workmentioning

confidence: 99%

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

Matsui

Onishi

Misaki

et al. 2020

Sensors

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“…Table II shows a comparison of our proposed system against recent works that have used location estimation techniques as a supporting or standalone system for activity monitoring. Most of these systems are designed only to achieve room-level accuracy [13] [18] [19]. Other works that consider a higher number of activities, suffer in accuracy due to the complexity involved in a multi-activity classification problem (10 or more classes) [15] [20].…”

Section: Discussionmentioning

confidence: 99%

“…Their results provided an average accuracy of 89%. In [18] and [19], the authors implemented a BLE fingerprinting approach using beacons for monitoring of nursing home residents and for detecting frailty in older adults based on indoor localization habits, respectively. However, all these systems were designed to predict simple activities based on the user's location with just room-level accuracy.…”

Section: Related Workmentioning

confidence: 99%

Inferring Micro-Activities Using Wearable Sensing for ADL Recognition of Home-Care Patients

Sridharan

Bigham

Campbell

et al. 2020

IEEE J. Biomed. Health Inform.

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In this study, we propose a novel context-based location aware algorithm for identification of low-level microactivities that can be used to derive complex Activities of Daily Living (ADL) performed by home-care patients. This is achieved by gathering location information of the target user using a wearable beacon embedded with magnetometer and inertial sensors. The shortcomings of beacon signal stability and mismatch issues in magnetic field sequences are overcome by adopting a hybrid three-phase approach for deducing the locus of micro-activities and their associated zones in a smart home environment. The suggested approach is assessed in two different test environments where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labelled database. In addition to recognition of low-level activities, the proposed method also identifies the person's walking trajectory within the same zone or between different zones of the house. Experimental results demonstrate that it is possible to achieve centimeter-level accuracy for recognition of micro-activities and a classification accuracy of 85% for trajectory prediction. These results are encouraging and imply that collection of accurate low-level information for ADL recognition is possible through integration of inertial sensors, Magnetic Field and Bluetooth Low Energy (BLE) technologies from the wearable without relying on other infrastructural sensors.

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“…We have been attempting to solve the above problems with information and communication technology (ICT) (3,4) We consider that changes in caregivers' mental state (i.e., stress) might have a significant impact on their workload. Therefore, this study focuses on changes in stress caused by caregiving work with the aim of improving caregiving work efficiency.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Visualization of Relationship between Stress and Care Activities toward Reduction in Caregiver Workload

Miyaji¹,

Kimata²,

Matsui³

et al. 2022

Sensors and Materials

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In Japan, the demand for nursing homes is increasing owing to the rapid aging of the population, and the shortage of caregivers has become a serious problem. This problem has been recognized as a social issue because it increases the workload per caregiver. In response, we have been developing a platform that can easily collect data on nursing care activities to reduce the workload on the basis of the idea that the mental state (i.e., stress) of caregivers, which changes during care activities, might markedly affect their work efficiency. The objective of this study was to obtain new knowledge for reducing the workload of caregivers by visualizing and analyzing their stress. Specifically, we asked caregivers to wear the device, from which we obtained the objective stress indicators of R-R interval (RRI) and low frequency (LF)/high frequency (HF) ratio. We also obtained subjective stress indicators from questionnaires administered before the start of the experiment, before work, during breaks, and after work. To confirm that the stress level of caregivers depends on their care activity, work shift, and workday, we conducted an empirical experiment in an actual nursing home. We distributed devices and applications to five caregivers, obtained measurement data for a total of 28 days, and analyzed and visualized the changes in psychological states related to care activities, work shift, and workdays. As a result, we were able to collect data on the psychological state for 5 to 15 days per caregiver. Furthermore, the analysis of the data showed that, even though objective stress indicators and subjective evaluations did not necessarily coincide, stress tended to increase with specific care activities, work shift, and workdays. In addition, the subjective and objective stress indicators may change depending on the personality of the caregivers themselves.

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Beacon-Based Time-Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes

Cited by 14 publications

References 44 publications

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

Inferring Micro-Activities Using Wearable Sensing for ADL Recognition of Home-Care Patients

Analysis and Visualization of Relationship between Stress and Care Activities toward Reduction in Caregiver Workload

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