A fall detection system based on infrared array sensors with tracking capability for the elderly at home

Chen, Wei-Han; Ma, Hsi-Pin

doi:10.1109/healthcom.2015.7454538

Cited by 31 publications

(18 citation statements)

References 14 publications

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“…Most of them used 1-2 sensors mounted on the ceiling 22,23,27,[33][34][35][36]39,40 or on the wall. 21,28,30,31,37 Chen & Wang 29 proposed to place the sensor on a mini-robot that follows the subject. This placement was technically close to the one on the wall.…”

Section: Methodsmentioning

confidence: 99%

“…The sitting on the floor and lying postures were interpreted as fall conditions. The twelve other studies [21][22][23][28][29][30][31][33][34][35][36][38][39][40][41][42] proposed to realize both, posture and locomotion conditions. In the locomotion conditions, the participants had to repeat dynamic actions, especially walking, sitting down on a chair from a standing position, lying down on the floor from a standing or a sitting position, and falling.…”

Section: Methodsmentioning

confidence: 99%

“…At first, 1674 articles were identified through databases search and seven were added after manual search in articles bibliographies and empirical searches on articles, Google Scholar and Semantic Scholar. After exclusion of duplicates and full-text reading, 19 articles were included [21][22][23][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] (see details in Figure 1). Four authors "duplicated" their study in two different journals.…”

Section: The Steps Of Articles Selection Processmentioning

confidence: 99%

“…22,23,27,[29][30][31][32][33][34][35]39,40 The Melexis MLX9062x (16x4 pixels) were used in three studies. 28,36,37 The Omron D6T-1616-L-06 (16x16 pixels), the Omron D6T-8L-06 (1x8 pixels) and the Irisys (16x16 pixels) were each used in one study. 21,38,41,42 The majority of the studies used a system containing at last the sensor (s) and a microcontroller.…”

Section: Sensor Types and System Architecturesmentioning

confidence: 99%

“…21,38,41,42 The majority of the studies used a system containing at last the sensor (s) and a microcontroller. 22,23,[27][28][29][30][31][32][33][34]36,37,41,42 Only Chen & Wang 29 needed to add a battery to supply their system (mini-robot). Only Hayashida et al 33,34 gave information concerning power consumption.…”

Section: Sensor Types and System Architecturesmentioning

confidence: 99%

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Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

Ben-Sadoun¹,

Michel²,

Annweiler³

et al. 2022

CIA

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Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness-ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16×16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26-28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were "conference papers", five were "original articles" and one was a found in arXiv.org (an open access repository of scientific research). Since four authors "duplicated" their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85-90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: The Steps Of Articles Selection Processmentioning

confidence: 99%

Section: Sensor Types and System Architecturesmentioning

confidence: 99%

Section: Sensor Types and System Architecturesmentioning

confidence: 99%

See 3 more Smart Citations

Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

Ben-Sadoun¹,

Michel²,

Annweiler³

et al. 2022

CIA

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An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

Shu

Shu²

2021

Sci Rep

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Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

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A dual-typed and omnidirectional infrared perceptual network for indoor human tracking

Yang¹,

Liu²

2021

Meas. Sci. Technol.

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This paper proposes a dual-typed and omnidirectional infrared perceptual network for indoor human target location and tracking. Two types of infrared sensors, pyroelectric infrared sensors and thermopile array sensors, are used in the sensor network for side-view and top-view perception respectively. The sensor nodes are deployed to construct an omnidirectional sensing model for detecting human targets in an indoor scenario with irregular boundary, furniture and other obstacles. The improved credit location algorithm and the adaptive threshold algorithm are applied for the human location. The location ambiguity existing in the actual situations is analysed and the compensation methods are designed by using an equivalent measurement line and a state machine to reduce the location ambiguity. Finally, a fuzzy adaptive CS-jerk (current statistical-jerk) algorithm is applied for tracking the human target. The tracking results indicate that the dual-typed and omnidirectional system works well in the indoor environment.

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A fall detection system based on infrared array sensors with tracking capability for the elderly at home

Cited by 31 publications

References 14 publications

Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

A dual-typed and omnidirectional infrared perceptual network for indoor human tracking

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