Architecture of a Non-Intrusive IoT System for Frailty Detection in Older People

Ciubotaru, Bogdan-Iulian; Sasu, Gabriel-Vasilică; Goga, Nicolae; Vasilățeanu, Andrei; Popovici, Alexandru-Filip

doi:10.3390/electronics12092043

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…One such approach involves the use of an inertial measurement unit (IMU) device, such as a smartwatch or smartphone, equipped with an accelerometer and angular velocity sensor. The data collected using this device are then analysed to assess the subject's gait [15][16][17][18][19]. Another approach utilizes Bluetooth low-energy (BLE) beacons to measure changes in individuals' indoor activity and behaviour [20][21][22].…”

Section: Measuring Walking Speed During Daily Activitiesmentioning

confidence: 99%

MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

Utsumi

Hashimoto

2023

Electronics

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With the ageing of society, the number of households with older individuals or couples living alone is increasing. An “ageing-in-place” approach allows older adults to continue to live at home and receive help only when needed. However, this approach is insufficient for emergencies, such as falls, as well as for individuals with gradually deteriorating health conditions, such as frailty. Unexpected accidents significantly reduce the quality of life (QoL) of older adults. This paper proposes a new framework, the mobility ubiquitous sensor edge environment (MUS3E), to digitally transform ordinary houses to detect the movement of older individuals throughout their home environment and to notify family members and care providers of residents’ health status and safety information. The framework can be easily and inexpensively installed in any home, transforming an ordinary house into a smart home with an automated function for monitoring older residents. It uses ambient sensors such as passive infrared ray sensors to automatically measure health conditions by measuring factors such as walking speed. Residents need not interact with or control the system and can go about their daily lives. Since the sensors used in this system are mass-produced consumer products, they are inexpensive and easily replaceable, as there are many alternatives. In this study, we were able to demonstrate the practically and feasibility of this framework using a prototype that uses open architecture Internet of Things (IoT) software (Debian GNU/Linux 11, Arduino 1.8.19, ESP8266 2.7.4, ESP32 1.0.6, PubSubClient 2.8.0, ESPPerfectTime 0.3.0, mosquitto 2.0.11) components to digitally transform the living environment of older individuals.

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Section: Measuring Walking Speed During Daily Activitiesmentioning

confidence: 99%

MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

Utsumi

Hashimoto

2023

Electronics

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“…Based on these requirements, an IoT system with a Technical Readiness Level of 4 (technology validated in a laboratory environment) was successfully developed and tested [22]. The system uses high amounts of data and processes it under high-performance and low-cost conditions (in terms of hardware resources and processing power), and the results are available in [22]. The primary data source for the prototype is the raw data obtained from the sensors (accelerometer, gyroscope, orientation, optical) embedded in a smartwatch worn on the wrist.…”

mentioning

confidence: 99%

“…The primary data source for the prototype is the raw data obtained from the sensors (accelerometer, gyroscope, orientation, optical) embedded in a smartwatch worn on the wrist. Compared with the five other projects described in [22], our system is easy to use and non-intrusive, and it has robust security ensured by standard and custom mechanisms.…”

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confidence: 99%

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Prototype Results of an Internet of Things System Using Wearables and Artificial Intelligence for the Detection of Frailty in Elderly People

et al. 2023

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As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.

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Frailty Insights Detection System (FIDS)—A Comprehensive and Intuitive Dashboard Using Artificial Intelligence and Web Technologies

Ciubotaru,

Sasu,

Goga

et al. 2024

Applied Sciences

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Frailty, known as a syndrome affecting the elderly, have a direct impact on both social well-being and body’s ability to function properly. Specific to geriatric healthcare, the early detection of frailty helps the specialists to mitigate risks of severe health outcomes. This article presents the development process of a system used to determine frailty-specific parameters, focusing on easy-to-use, non-intrusive nature and reliance on objectively measured parameters. The multitude of methodologies and metrics involved in frailty assessment emphasize the multidimensional aspects of this process and the lack of a common and widely accepted methodology as being the gold standard. After the research phase, the frailty-specific parameters considered are physical activity, energy expenditure, unintentional weight loss, and exhaustion, along with additional parameters like daily sedentary time, steps history, heart rate, and body mass index. The system architecture, artificial intelligence models, feature selection, and final prototype results are presented. The last section addresses the challenges, limitations, and future work related to the Frailty Insights Detection System (FIDS).

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Architecture of a Non-Intrusive IoT System for Frailty Detection in Older People

Cited by 3 publications

References 16 publications

MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

Prototype Results of an Internet of Things System Using Wearables and Artificial Intelligence for the Detection of Frailty in Elderly People

Frailty Insights Detection System (FIDS)—A Comprehensive and Intuitive Dashboard Using Artificial Intelligence and Web Technologies

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