Evaluation of a fall detector based on accelerometers: A pilot study

Lindemann, Ulrich; Hock, Alyson J.; Stüber, M.; Keck, William; Becker, Clemens

doi:10.1007/bf02351026

Cited by 254 publications

(176 citation statements)

References 9 publications

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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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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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“…A very well addressed sub-task of Health Condition is the fall detection in which accelerometers are seen as the suitable technology. [17], [11], [57], [73], [65], [55], [16], [74], [56], [130], [137], [119]; A B [82]; 2xA B [23]; G [18]; AG [64] Floor vibration-based sensor [4]; array of acoustic sensors [94]; Tilt sensor + piezoelectronic A + vibration sensor [83]; tri-axial MTx [134]; A T +G T [50], [127]; multiple A T [44]; A T +BAR [114] Kin [72], [90]; Cam [101], [7], [41] IR, pressure sensor, audio…”

Section: Physiological Health Condition and Healthcarementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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“…However, for an automatic home alarm system, it is necessary that falls are detected with enough sensitivity, and without generating false alarms (i.e., high speciWcity). BFS based fall detectors may be the basis for reliable fall reports and automatic fall alarm systems (e.g., see Lindemann et al 2005), but at present, there is very little published information about BFS based fall detectors.…”

Section: Fall Detectorsmentioning

confidence: 99%

Mobility assessment in older people: new possibilities and challenges

2007

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A major challenge for researchers and clinicians who address health issues in the ageing population is to monitor functioning, and to timely initiate interventions that aim to prevent loss of functional abilities and to improve the quality of life of older people. With the progress of technologies in the last decades, methods have become available that use body Wxed sensors (BFS) to measure aspects of human performance under real-life conditions. These methods are based on the use of miniaturised and integrated sensors in combination with lightweight, small measuring devices that both can be carried on the body without interfering with normal behaviour. This paper addresses the potential relevance of new technology for monitoring motor function in older people, thereby speciWcally focusing on mobility assessment. After a short introduction with background information about BFS based technology, this paper identiWes areas of particular relevance, and discusses the application of ambulatory techniques for long-term monitoring of daily physical activity, fall detectors, fall risk evaluation, and assessment of motor performance such as gait and balance control. Examples are given how these techniques can become clinically relevant, particularly in the context of fall interventions for older people.

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Evaluation of a fall detector based on accelerometers: A pilot study

Cited by 254 publications

References 9 publications

Load on Osseointegrated Fixation of a Transfemoral Amputee during a Fall

Load on Osseointegrated Fixation of a Transfemoral Amputee during a Fall

Classification and suitability of sensing technologies for activity recognition

Mobility assessment in older people: new possibilities and challenges

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