Early identification of declining balance in higher functioning older adults, an inertial sensor based method

Sobolev, Boris; Greene, Barry R.; Cunningham, Conal; Crosby, Lisa; Kenny, Rose Anne

doi:10.1016/j.gaitpost.2014.01.003

Cited by 19 publications

(18 citation statements)

References 20 publications

(32 reference statements)

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“…These findings have brought increasing interest into the design of systems and algorithms for gait analysis. Several methods have been proposed to estimate the most relevant gait parameters and assist geriatricians in the assessment of gait ability [7]- [10]. However, most of these solutions can only be applied to short walks or to specific tests such as the Timed Up-and-Go (TUG) test [8].…”

Section: Introductionmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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Abstract-A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can be applied only to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The user's gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real-time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ∼ 84% accuracy in the recognition of abnormal gait segments lasting ∼ 5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

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Section: Introductionmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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“…The TUG test assessed with a wrist-based accelerometer has been also linked to the prediction of disability levels in community-dwellers older adults [137], while the inertial-based assessment reliability of the TUG via testing measures of variability over 5 continuous days during single-task and dual-tasks was analysed in Reference [138]. Sheehan et al [139] adopted the same approach with an accelerometer attached to each shank to investigate the monitoring of functioning decline by testing older subjects one year apart. Finally, the possibility to adopt hip and ankle-based sensors to distinguish five different dynamic balance exercises and evaluate their performance via machine learning techniques has been considered in [140].…”

Section: Wearables For Senior Citizens: Related Work and Limitationsmentioning

confidence: 99%

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

Tedesco

Barton

O’Flynn

2017

Sensors

107

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The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

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“…This recognition can be performed by wearables [9] or even by smartphones [10]. It uses accelerometers to assist older adults; in [11], authors present an accelerometer for movement monitoring; in [12], a method based on accelerometers for identification of declining balance is proposed; finally, accelerometers are used to detect some predefined movements (stand-to-sit and sit-to-stand) in young and old adults [13]. Furthermore, in [14], the authors design and implement an embedded system to estimate the pedestrian walking position.…”

Section: Related Workmentioning

confidence: 99%

OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0

Roda-Sánchez¹,

Garrido-Hidalgo²,

Hortelano³

et al. 2018

Journal of Sensors

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Industry 4.0 is leading the Fourth Industrial Revolution transforming traditional factories into smart factories governed by the Internet of Things (IoT). In order to assist smart factory employees, this paper introduces OperaBLE, a Bluetooth Low Energy (BLE) wearable proposal which is aimed at enhancing working conditions and efficiency in Industry 4.0 scenarios. We have developed two innovative algorithms for OperaBLE focused on power awareness as the key-enabling attribute towards success: Low-Frequency Movement Characterisation Algorithm (LoMoCA) and Adaptive Heart Rate Algorithm (AHRA). Novel experiments have been carried out using OperaBLE to determine its operability, reliability, and lifespan. Results obtained during experimentation demonstrate how OperaBLE empowers human-machine collaboration embedding workers in closed-loop performance and ensuring nonharmful working conditions by means of power-aware algorithms. OperaBLE is due to bring digitalisation into smart factories, playing an essential role in the emerging wearable revolution to arise in the following years towards smart production systems.

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Early identification of declining balance in higher functioning older adults, an inertial sensor based method

Cited by 19 publications

References 20 publications

An On-Node Processing Approach for Anomaly Detection in Gait

An On-Node Processing Approach for Anomaly Detection in Gait

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0

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