Sensor-Based Tracking and Big Data Processing of Patient Activities in Ambient Assisted Living

Nienhold, Dino; Dornberger, Rolf; Korkut, Safak

doi:10.1109/ichi.2016.87

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…A wearable device and smart-phone sensors will be connected to patient's body to collect physiological data. Such sensors are measuring vital-sign, such as, blood oxygen saturation, skin temperature and heart-rate, variety of healthcare sensors are available today [9,31,34,35,41]. Monitoring these symptoms in patient's body is very important, as any abnormal data could potentially end up with a disease [35,41].…”

Section: -Wearable Devices (Patients Layer)mentioning

confidence: 99%

“…Thus, developing an effective healthcare monitoring system, several factors need to considered, such as data availability, consistency, and reliability. However, the most important factor is data freshness that needs to be well supported during runtime [20,31] by the healthcare systems which deal with people health. More precisely, when authorised people monitoring patient's symptom, the data it has to be refreshed with high frequency so that doctor's platform update continuously to pull the latest data from patients with warbles.…”

Section: System Requirementsmentioning

confidence: 99%

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Remote health monitoring of elderly through wearable sensors

Al-Khafajiy¹,

Baker²,

Chalmers³

et al. 2019

Multimed Tools Appl

277

103

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Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person's health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person's physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.

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Section: -Wearable Devices (Patients Layer)mentioning

confidence: 99%

Section: System Requirementsmentioning

confidence: 99%

Remote health monitoring of elderly through wearable sensors

Al-Khafajiy¹,

Baker²,

Chalmers³

et al. 2019

Multimed Tools Appl

277

103

View full text Add to dashboard Cite

show abstract

“…But implementing one system alone is not enough to detect a specific behaviour, because the elderly needs a system that can track their activities and their location at the same time to predict their behaviour. Nienhold et al [16] proposed an idea to study the daily activities and location of the elderly person by placing motion sensors in each room, and a wearable placed on arm, leg and breast for activities tracking with an accuracy rate of over 83%. The limitations of this approach were low accuracy, many sensors and the indoor location detected by using motion sensors which were difficult to install and costly.…”

Section: Literature Reviewmentioning

confidence: 99%

A wearable device for machine learning based elderly's activity tracking and indoor location system

et al. 2021

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The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.

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“…On the other hand, health information technology has been available since 1990's [15], but only recent development of sensor-based information gathering allows real-time tracking of people living at home or assisted care [16]. In addition to individual-based information, also existing data registered from the environment can be used for other but its original purpose [17].…”

Section: Literature Reviewmentioning

confidence: 99%