Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey

Abbate, Stefano; Avvenuti, Marco; Corsini, Paolo; Light, Janet; Vecchio, Alessio

doi:10.5772/13802

Cited by 86 publications

(66 citation statements)

References 34 publications

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“…Fifth, both vision and ambience based approaches require a pre-built infrastructure, which could be valuable to use them in home and hospital, but not to use them outdoor [28].…”

Section: Discussionmentioning

confidence: 99%

A Panoramic Study of Fall Detection Technologies

2016

IJCSI

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Falls are a major risk of injury for elderly aged 65 or over, blind people, people with balance disorder and leg weakness. In this regard, assistive technology which aims to identify fall events at real time can reduce the rate of impairments and mortality. This study offer a literature research reference value for bioengineers for further research. Much of the past and the current fall detection research, the vital signals features and the way features are extracted and fed to a classifier are introduced. The study concludes with an assessment of the current technologies highlighting their critical limitations along with suggestions for future research direction in this rapidly developing field of study.

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“…Fifth, both vision and ambience based approaches require a pre-built infrastructure, which could be valuable to use them in home and hospital, but not to use them outdoor [28].…”

Section: Discussionmentioning

confidence: 99%

A Panoramic Study of Fall Detection Technologies

2016

IJCSI

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“…A set of trials consists of 20 fall actions (front-lying, frontprotection-lying, front-knees, front-knees-lying, front-right, front-left, front-quickrecovery, front-slow-recovery, back-sitting, back-lying, back-right, back-left, rightsideway, right-recovery, left-sideway, left-recovery, syncope, syncope-wall, podium, rolling-out-bed) and 16 ADLs (lying-bed, rising-bed, sit-bed, sit-chair, sit-sofa, sit-air, walking-forward, jogging, walking-backward, bending, bending-pick-up, stumble, limp, squatting-down, trip-over, coughing-sneezing). We adopted these from [19] and each lasted about 15 s on the average. The 14 volunteers repeated each test for five times.…”

Section: Datasetmentioning

confidence: 99%

Investigation of Sensor Placement for Accurate Fall Detection

Ntanasis

Pippa

Özdemir

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.

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“…Gait sensors and accelerometers will not only be able to predict falls, but to determine the physiological root and recommend training or rehabilitation (e.g. greater flex of hips to compensate weak ankles) 12 . Contextaware systems have the capacity to be cognizant of environmental activities and characteristics through networked equipment, such as mobile, pervasive and ubiquitous computing components.…”

Section: Innovation In Technologies For Dementia Carementioning

confidence: 99%

“…Gas and water sensors can turn the appliance off if it has been left on for too long. Gait analysis employs motion sensors and accelerometers can predict falls before they occur 12 . Electronic keys and door sensors can accommodate for entry and exit of the home and fall detectors can alert caregivers or emergency personnel when an incident occurs 9,[12][13][14] .…”

mentioning

confidence: 99%

“…Gait analysis employs motion sensors and accelerometers can predict falls before they occur 12 . Electronic keys and door sensors can accommodate for entry and exit of the home and fall detectors can alert caregivers or emergency personnel when an incident occurs 9,[12][13][14] . It is also important to recognize that wandering can serve to keep the person physically active and allow them to express needs or emotions that they otherwise might not be able to communicate (e.g.…”

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confidence: 99%

See 1 more Smart Citation

The Future of Assistive Technologies for Dementia

Peterson¹,

Prasad²,

Prasad³

2012

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Purpose The use of Assistive Technologies (ATs) for residential dementia care is increasing, yet there is a gap between what individuals want, what developers design, and how outcomes are evaluated. Despite widespread acceptance that ATs improve quality of living (QOL), there is relatively little data to support such claims. This article discusses the current state-of-the-art AT-design, its use and assessment in relation to dementia care and projected future trends that can be incorporated into research now. Method By reviewing a history of ATs used in residential dementia care, incorporating societal and healthcare trends and applying theories of science, a futuristic view of AT-development and use is presented. The theoretical foundation is rooted in phenomenology, universal design, aging in place and gerontechnology. This research is supported by results from a European Commission-funded project where ATs were integrated and tested in real life conditions and evaluated qualitatively and quantitatively by older adults with dementia as well as their formal and informal caregivers. Results & Discussion The results shows the need for future ATs to be more integrated into the environment, combined with ambient and intelligent technologies, the Internet of Things (IoT), and the potential of cloud computing. They will also become more personalized to individual needs and user requirements.

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Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey

Cited by 86 publications

References 34 publications

A Panoramic Study of Fall Detection Technologies

A Panoramic Study of Fall Detection Technologies

Investigation of Sensor Placement for Accurate Fall Detection

The Future of Assistive Technologies for Dementia

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