Beacon-based multi-person activity monitoring system for day care center

Komai, Kiyoaki; Fujimoto, Manato; Arakawa, Yutaka; Suwa, Hirohiko; Kashimoto, Yukitoshi; Yasumoto, Keiichi

doi:10.1109/percomw.2016.7457140

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…Komai et al [34] present a system to monitor the activity of multiple seniors simultaneously based on Bluetooth Low Energy (BLE) with a beacon in the user's name card and the Received Signal Strength Indicator (RSSI). Likewise, [114] uses BLE and RSSI to create a low-cost indoor-localization method to track and estimate the user's room.…”

Section: B Smart Homesmentioning

confidence: 99%

“…Consequently, ambient assisted living focuses on creating environments for older adults that integrate smart healthcare techniques for better care without human intervention. Given that around 90% of older adults prefer staying at their own homes, many solutions are based on smart home systems such as those proposed in [34]- [37]. User studies [36], [38], [39], however, uncover the importance of including robotic agents capable of social interactions with the user to provide both psychological and physical assistance to older adults.…”

Section: Introductionmentioning

confidence: 99%

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Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Mahmoud¹,

et al. 2021

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The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

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Section: B Smart Homesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Mahmoud¹,

et al. 2021

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“…We also tried to measure the activities of elderly persons in a nursing home [22], [23] by using Bluetooth Low Energy Beacon, which is the same technology used for our another system explained in Section 3.4.…”

Section: Sensing In a School And A Nursing Homementioning

confidence: 99%

Sensing and Changing Human Behavior for Workplace Wellness

Arakawa

2019

Journal of Information Processing

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Recently, companies have begun to care more about the well-being of their employees. With the spread of sensors, the Internet of Things, and artificial intelligence, the movement to build a better working environment by utilizing these technologies has been spreading. Especially, research on behavior that can change lifestyle habits is becoming popular. In this paper, we summarize workplace behavior research and projects for sensing and changing human behavior in a workplace and aim to improve the productivity and wellness of employees. Also, we introduce concepts for future workplaces and some of our related achievements. For physical state sensing, we have developed a continuous posture-sensing chair, which will soon be available commercially. For internal state sensing, we propose a method for estimating quality of life with wearable sensors. Our system have already achieved to estimate QoL (Quality of Life) around 90% with only 9 questions. In addition, we propose interactive digital signage to provide habit-changing reminders. Through one month experiment, we confirmed that our system can be feasible in daily life.

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“…In this section, we explain the basic working environment in nursing homes and their motivation, which is crucial to conduct our study. Then, we describe previous works [2,[7][8][9][10] and explain the requirements for the development of our proposed system, as described in Section 3. Journal of Sensors in health checks, rehabilitation support, food preparation, restroom support, conversations, and so on.…”

Section: Preliminary Workmentioning

confidence: 99%

“…To realize the above system, it is necessary to monitor a resident in the nursing home. First, as a prototype system, we presented a novel system [7,8] in which users have BLE beacons and installed scanners on the environment side. In this system, we divided a nursing home into several areas and installed scanners in each one.…”

Section: Previous Workmentioning

confidence: 99%

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

et al. 2018

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As the world’s population of senior citizens continues to grow, the burden on the professionals who care for them (carers) is also increasing. In nursing homes, carers often write daily reports to improve the resident’s quality of life. However, since each carer needs to simultaneously care for multiple residents, they have difficulty thoroughly recording the activities of residents. In this paper, we address this problem by proposing an automatic daily report generation system that monitors the activities of nursing home residents. The proposed system estimates the multiple locations (areas) at which residents are situated with a BLE beacon, using a variety of methods to analyze the RSSI values of BLE signals, and recognizes the activity of each resident from the estimated area information. The information of the estimated activity of residents is stored in a server with timestamps, and the server automatically generates daily reports based on them. To show the effectiveness of the proposed system, we conducted an experiment for five days with four participants in cooperation with an actual nursing home. We determined the proposed system’s effectiveness with the following four evaluations: (1) comparison of performance of different machine-learning algorithms, (2) comparison of smoothing methods, (3) comparison of time windows, and (4) evaluation of generated daily reports. Our evaluations show the most effective combination pattern among 156 patterns to accurately generate daily reports. We conclude that the proposed system has high effectiveness, high usability, and high flexibility.

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Beacon-based multi-person activity monitoring system for day care center

Cited by 23 publications

References 8 publications

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Sensing and Changing Human Behavior for Workplace Wellness

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

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