Design and implementation of a distributed fall detection system based on wireless sensor networks

Luo, Xiaomu; Liu, Tong; Liu, Jun; Guo, Xuemei; Wang, Guoli

doi:10.1186/1687-1499-2012-118

Cited by 51 publications

(24 citation statements)

References 35 publications

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“…A two-layer HMM approach, SensFall [14], is used to identify falls from other normal activities. In the first layer, the HMM classifies an unknown activity as normal vertical activity or 'other', while in second stage the 'other' activity is classified as either normal horizontal activity or as a fall.…”

Section: Related Workmentioning

confidence: 99%

Detecting falls with X-Factor Hidden Markov Models

Khan

Karg

Kuli

et al. 2017

Applied Soft Computing

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Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity that occurs rarely and infrequently. This poses a challenge for traditional supervised classification algorithms, because there may be very little training data for falls (or none at all) to build generalizable models for falls. This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL. We propose three 'X-Factor' Hidden Markov Model (XHMMs) approaches. The XHMMs have 'inflated' output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove 'outliers' from the normal ADL that serves as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data. We show that the traditional method of choosing threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem. We also show that supervised classification methods perform poorly when very limited fall data is available during the training phase.

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Section: Related Workmentioning

confidence: 99%

Detecting falls with X-Factor Hidden Markov Models

Khan

Karg

Kuli

et al. 2017

Applied Soft Computing

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“…Limited funding for public healthcare services and the shortage of registered nurses are also driving factors for the adoption of a home-based assisted living paradigm. Therefore, healthy aging at home has become one of the most active research areas [1], especially the problem of abnormal activity detection [2,3,4,5]. The elderly living alone in isolated areas have been in need of emergency attention, and in the worst cases, some were found dead in their homes [6].…”

Section: Introductionmentioning

confidence: 99%

“…Bearing those factors in mind, a Pyroelectric Infrared (PIR) sensing paradigm offers a promising alternative to the optical and wearable counterparts [3]. PIR sensors are non-intrusive sensors and only sensitive to the infrared radiation changes induced by human motion, which makes them robust to interference caused by clustered background and illumination variance.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

Luo

Tan

Guan

et al. 2016

Sensors

Self Cite

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Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.

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“…Tri-axial accelerometers [10] make it possible to recognize activities [11], thus improving fall detection systems. Pyroelectric InfraRed (PIR) sensors can also be used to classify different human activities [12]. Several techniques can be used to analyze the data, such as machine learning algorithms.…”

Section: Wearable Devices and Sensorsmentioning

confidence: 99%

Towards a social and context-aware multi-sensor fall detection and risk assessment platform

Backere

Ongenae

Abeele

et al. 2015

Computers in Biology and Medicine

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For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms.In this paper, a social-and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task.The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors.

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Design and implementation of a distributed fall detection system based on wireless sensor networks

Cited by 51 publications

References 35 publications

Detecting falls with X-Factor Hidden Markov Models

Detecting falls with X-Factor Hidden Markov Models

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

Towards a social and context-aware multi-sensor fall detection and risk assessment platform

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