Design of an unobtrusive system for fall detection in multiple occupancy residences

Ariani, Arni; Redmond, Stephen J.; Zhang, Zhaonan; Narayanan, Michael R.; Lovell, Nigel H.

doi:10.1109/embc.2013.6610594

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Wearable detection approaches use sensors, such as accelerometers and gyroscopes, to detect and measure motion, location, and posture by measuring acceleration and orientation [129] [181]. Ambient detection approaches use devices such as pressure sensors and PIR for movement detection [182]. They also rely on audio and vibration data analysis [183].…”

Section: Movement Tracking and Fall Detectionmentioning

confidence: 99%

A survey on health monitoring systems for health smart homes

Mshali

Lemlouma

Moloney

et al. 2018

International Journal of Industrial Ergonomics

185

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Aging population ratios are rising significantly. Health monitoring systems (HMS) in smart environments have evolved rapidly to become a viable alternative to traditional healthcare solutions. The aim of HMS is to not only reduce costs but to also provide timely e-health services to individuals wishing to maintain their independence. In this way, elderly people can avoid, for as long as possible, any interaction with healthcare institutions (e.g. nursing homes and hospitals), which in turn reduces pressure on the health system. To fully realise this vision of seamless e-health services supporting people in need of them, a number of challenges that need further investigation still exist. To this end, we provide an overview of the current state of the art for smart health monitoring systems. We review HMS in smart environments from a general perspective and with a particular focus on systems for the elderly and dependent people. We look at the challenges for these systems from the perspective of developing the technology itself, system requirements, system design and modelling. We present a consolidated picture of the most important functions and services offered by HMS for monitoring and detecting human behaviour including its concepts, approaches, and processing techniques. Moreover, we provide an extensive, in-depth analysis and evaluation of the existing research findings in the area of e-health systems. Finally, we present challenges and open issues facing the smart HMS field and we make recommendations on how to improve future systems.

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Section: Movement Tracking and Fall Detectionmentioning

confidence: 99%

A survey on health monitoring systems for health smart homes

Mshali

Lemlouma

Moloney

et al. 2018

International Journal of Industrial Ergonomics

185

View full text Add to dashboard Cite

show abstract

“…This method has been used in a wide range of applications for both military and civilian purposes, such as thermal-IR imaging, night vision, surveillance and tracking, weather forecasting, medical imaging, and thermography, to name a few. Some examples of IR-based tracking systems for telehealth applications can be found in Ariani et al (2013), Ariani et al (2012), Hao et al (2009 and Zhang et al (2011), where the authors combine IR sensors and pressure mat sensors to develop unobtrusive fall-detection systems for the elderly.…”

Section: Angle Of Arrival (Aoa)mentioning

confidence: 99%

“…To address the aforementioned issue, Ariani et al (2013) and Ariani et al (2012) proposed the use of heuristic-decision tree systems, based on dual technology sensors. The authors simulated patented microwave and passive infra red (PIR) technology to achieve better coverage and to minimise dead zones as well as to lessen directional sensitivity to movement (Ariani et al 2012).…”

Section: Current State-of-the-art Fall Detection Systemsmentioning

confidence: 99%

Indoor location-aware medical systems for smart homecare and telehealth monitoring: state-of-the-art

Santoso

Redmond

2015

Physiol. Meas.

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This paper presents a comprehensive literature review of current progress in the application of state-of-the-art indoor positioning systems for telecare and telehealth monitoring. This review is the first in the literature that provides a comprehensive discussion on how existing wireless indoor positioning systems can benefit the development of home-based care systems. More specifically, this review provides an in-depth comparative study of how both system users and medical practitioners can get benefit from indoor positioning technologies; e.g. for real-time monitoring of patients suffering chronic cardiovascular conditions, general monitoring of activities of daily living (ADLs), fall detection systems for the elderly as well as indoor navigation systems for those suffering from visual impairments. Furthermore, it also details various aspects worth considering when choosing a certain technology for a specific healthcare application; e.g. the spatial precision demanded by the application, trade-offs between unobtrusiveness and complexity, and issues surrounding compliance and adherence with the use of wearable tags. Beyond the current state-of-the-art, this review also rigorously discusses several research opportunities and the challenges associated with each.

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“…Most literature focusing on ambient sensing to monitor cognitive health of behavioural disturbances have reported on technology development and feasibility studies with older adults and PwDs [ 46 , 47 , 48 , 49 ]. The literature has traditionally examined routine-action monitoring such as posture analysis [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ] and safety monitoring [ 57 ], fall detection (e.g., [ 58 , 59 , 60 , 61 , 62 , 63 ], indoor localization (e.g., [ 64 , 65 , 66 ], and wandering (e.g., [ 67 ]). Healthcare professionals’ perspectives on the use of ambient sensing technologies have been explored in a variety of settings, including at a people’s home [ 68 , 69 ], in laboratories [ 70 , 71 ] and in healthcare institutions [ 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

Choukou

Mbabaali

East

2021

IJERPH

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The number of Canadians with dementia is expected to rise to 674,000 in the years to come. Finding ways to monitor behavioural disturbance in patients with dementia (PwDs) is crucial. PwDs can unintentionally behave in ways that are harmful to them and the people around them, such as other residents or care providers. Current practice does not involve technology to monitor PwD behaviours. Events are reported randomly by nonstaff members or when a staff member notices the absence of a PwD from a scheduled event. This study aims to explore the potential of implementing a novel detector of behavioural disturbances (DBD) in long-term care homes by mapping the perceptions of healthcare professionals and family members about this technology. Qualitative information was gathered from a focus group involving eight healthcare professionals working in a tertiary care facility and a partner of a resident admitted in the same facility. Thematic analysis resulted in three themes: (A) the ability of the DBD to detect relevant dementia-related behavioural disturbances that are typical of PwD; (B) the characteristics of the DBD and clinical needs and preferences; (C) the integration of the DBD into daily routines. The results tend to confirm the adequacy of the DBD to the day-to-day needs for the detection of behavioural disturbances and hazardous behaviours. The DBD was considered to be useful and easy to use in the tertiary care facility examined in this study. The participants intend to use the DBD in the future, which means that it has a high degree of acceptance.

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Design of an unobtrusive system for fall detection in multiple occupancy residences

Cited by 9 publications

References 15 publications

A survey on health monitoring systems for health smart homes

A survey on health monitoring systems for health smart homes

Indoor location-aware medical systems for smart homecare and telehealth monitoring: state-of-the-art

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

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