Fall detection with body-worn sensors

Schwickert, Lars; Becker, Clemens; Lindemann, Ulrich; Maréchal, Cédric Le; Bourke, Alan K.; Chiari, Lorenzo; Helbostad, Jorunn L.; Zijlstra, Wiebren; Aminian, Kamiar; Todd, Chris; Bandinelli, Stefania; Klenk, Jochen

doi:10.1007/s00391-013-0559-8

Cited by 133 publications

(88 citation statements)

References 56 publications

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“…The results somehow match the divergence results among previous studies [23], which suggest that the raw signals from accelerometers may not be applied directly. An algorithm for post-data processing should be developed.…”

Section: Discussionsupporting

confidence: 87%

“…It is essential to determine fall characteristics and how they differ from that of normal activities. The current state of such studies is well documented by Schwickert's recent review article, which examined 96 selected studies from numinous electronic databases [23]. Published evidence-based support for commercially available fall detection devices is still lacking.…”

Section: Discussionmentioning

confidence: 99%

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A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors

et al. 2017

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Abstract:As the population ages, falls are becoming a major health problem, not only for those with some degree of balance or mobility impairment, but also among healthy active seniors. Previous studies suggest that the degradation of human sensorimotor function related to age contributes to falls. Hip bones are among the most frequently fractured body parts resulting from falls. Hip fractures are a frequent cause of early death, functional dependence, and high medical care costs. The current prevention method is to use hip protectors. Unfortunately, it often fails to do so because the pocket containing the pad can move away from the area during falls. Additionally, some seniors refuse to use hip protectors because they find them constraining. Hence, a new protector that is only activated during a fall is much desired. The current study explored the possibility via biomechanical analyses for building a wearable sensor system that triggers a mini-airbag system during a fall, i.e., the air-pad is only present for protection when a fall occurs. The results have revealed that two sensors placed on the left and right shoulder would be best for a detection of any-direction fall and could be applied for building a wearable sensor system for prevention of hip fractures resulting from falls.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors

et al. 2017

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“…In most previous studies the sampling frequency is higher than 50 Hz [8]. Again this can depend on the algorithm, since the NN and SVM algorithms that we have presented use the raw acceleration values without performing any kind of filtering operations, in contrast with many previous works [13,32].…”

Section: -Discussionmentioning

confidence: 98%

A comparison of public datasets for acceleration-based fall detection

Igual

Medrano

Plaza

2015

Medical Engineering & Physics

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Falls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range.

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“…Ludwig et al's subcategories (A.2-A.5) all entail the automated detection of some such adverse situations, like a fall, a cardiac event, or some other dangerous situation (such as a dementia sufferer wandering away from home). The scientific literature contains many reported algorithms which aim to automate the detection of falls in the home, primarily using accelerometry-based wearable sensors; see Shany et al [1] and Schwickert et al [6] for a review on fall detection algorithms. However, due to the relative rarity of fall events (approximately one in three people over 65 years will fall each year) there have been very few reports of the testing of these algorithms on data from real-world fall events; Bagalà et al provide one of the very few reports of algorithmic performance on real-world falls, although these fallers were suffering a form of Parkinson's disease, hence it is unclear how generalizable these results are [7].…”

Section: Applications In Reactivementioning

confidence: 99%

What Does Big Data Mean for Wearable Sensor Systems?

Redmond¹,

Lovell²,

Yang³

et al. 2014

Yearb Med Inform

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SummaryObjectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

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Fall detection with body-worn sensors

Cited by 133 publications

References 56 publications

A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors

A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors

A comparison of public datasets for acceleration-based fall detection

What Does Big Data Mean for Wearable Sensor Systems?

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