Preliminary evaluation of a full-time falling monitor for the elderly

Diaz, Ainhoa Iglesias; Prado, Manuel; Roa, Laura M.; Reina‐Tosina, Javier; Sanchez, German-David Goez

doi:10.1109/iembs.2004.1403637

Cited by 52 publications

(27 citation statements)

References 11 publications

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“…6 shows the acceleration and rotational rate of the trunk and thigh for sitting fast. Both a Amax and a Bmax are larger than thresholds used in [13], [5], [9], [7], and [3]. Therefore, sitting down fast is not distinguishable from a typical fall.…”

Section: Sit Down Fastmentioning

confidence: 93%

Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Stankovic

Hanson

et al. 2009

2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks

451

247

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Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs.In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.

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Section: Sit Down Fastmentioning

confidence: 93%

Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Stankovic

Hanson

et al. 2009

2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks

451

247

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“…• The efficacy of the algorithm that is implemented by the control circuit to detect automatically the time of descent using some sensors (e.g., accelerometers (18)(19)(20)(21)(22)(23) , gyroscope (21,(24)(25) , load (26) ) (18,20,24,(27)(28)(29) . Here, the term "time of descent" is defined as the moment when the amputees cease to have control over their movements leading their body to hit the ground.…”

Section: 2mentioning

confidence: 99%

Load on Osseointegrated Fixation of a Transfemoral Amputee during a Fall

Frossard

2010

Prosthetics &Amp; Orthotics International

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Mitigation of fall-related injuries for populations of transfemoral amputees fitted with a socket or an osseointegrated fixation is challenging. Wearing a protective device fitted within the prosthesis might be a possible solution, provided that issues with automated fall detection and time of deployment of the protective mechanism are solved. The first objective of this study was to give some examples of the times and durations of descent during a real forward fall of a transfemoral amputee that occurred inadvertently while attending a gait measurement session to assess the load applied on the residuum. The second objective was to present five semi-automated methods of detection of the time of descent using the load data. The load was measured directly at 200 Hz using a six-channel transducer. The average time and duration of descent were 242±42 ms [145 ms-310 ms] and 619±42 ms [550 ms -715 ms], respectively. This study demonstrated that the transition between walking and falling was characterised by times of descent that occurred sequentially. The sensitivity and specificity of an automated algorithm might be improved by combining several methods of detection based on the deviation of the loads measured from their own trends and from a template previously established.

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“…Prado [19] developed an intelligent 4 axis accelerometer unit (IAU) worn like a patch, fixed to the back at the height of the sacrum. The IAU was evaluated by Diaz [20] in a laboratory study carried out over 8 volunteers, it showed that the device was able to distinguish true falling events from normal activities like fast walking or going up/downstairs. Recently, Bourke proposed fall algorithms separately based on thresholds on both signals from a tri-axial accelerometer [21] and a biaxial gyroscope [22] and reached a performance of 100%.…”

Section: State Of the Artmentioning

confidence: 99%

Fall detection - Principles and Methods

Noury

Fleury

Rumeau

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

476

287

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Abstract--Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.

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Preliminary evaluation of a full-time falling monitor for the elderly

Cited by 52 publications

References 11 publications

Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Load on Osseointegrated Fixation of a Transfemoral Amputee during a Fall

Fall detection - Principles and Methods

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