Fall detection in toilet rooms using thermal imaging sensors

Kido, Saki; Miyasaka, Tomoya; Tanaka, Toshiaki; Shimizu, Takao; Saga, Tsuneo

doi:10.1109/si.2009.5384550

Cited by 33 publications

(12 citation statements)

References 4 publications

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“…Regarding the data used to create the discriminant formula, a high discrimination rate of 98% or greater was obtained for all sensors. The obtained results were comparable to those of previous studies by Kido et al [14] and Miyasaka et al [15]. Therefore, herein, we consider that the creation of the discriminant formula was properly implemented.…”

Section: A Validity Of Discriminant Formulasupporting

confidence: 90%

“…Furthermore, temperature data does not reveal identifying characteristics such as facial features; thus, privacy can be maintained without requiring special image processing. Previously, our research group analyzed how 10 healthy adults moved in a bathroom when using the toilet and obtained a fall discrimination rate of 97.8% [14]. Thereafter, further improvements were made to achieve a fall discrimination rate of 99.5% [15].…”

Section: Introductionmentioning

confidence: 99%

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Use of Thermal Sensors for Fall Detection in a Simulated Toilet Environment

Shirogane¹,

Takahashi²,

Murata³

et al. 2019

International Journal of New Technology and Research

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Falls are likely to occur in a toilet. However, people typically are alone when they use the toilet; thus, if a fall occurs, it may be difficult to ask for help. Therefore, we constructed a fall detection system using a thermal image sensor that cannot distinguish details, such as a face, and has high suitability for the toilet environment. In this study, we investigate the influence of the sensor position on the fall discrimination rate. Sensors were installed at four locations and normal toilet movements and falling postures were recorded using eight healthy adults. Discrimination formulas were prepared using thermal images obtained for four subjects, and discrimination rates of falling postures were calculated for the remaining four subjects, which were then compared relative to sensor positions. The highest discrimination rate (95.7%) was obtained with the sensor installed in front of the toilet at the height of the body center of the subject sitting on the toilet. The lowest discrimination rate (60.2%) was obtained with the sensor installed in front of the toilet and close to the ceiling with respect to the subject sitting on the toilet. To detect falls using thermal imaging, sensor positions should be based on various factors, such as whether the entire body can be imaged relative to the falling posture without depending only on the height and direction or whether the difference between normal activity and falling posture is clear, including the environment of the toilet and the assumed fall situation.

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Section: A Validity Of Discriminant Formulasupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Use of Thermal Sensors for Fall Detection in a Simulated Toilet Environment

Shirogane¹,

Takahashi²,

Murata³

et al. 2019

International Journal of New Technology and Research

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“…However, neither WiFall nor RT-Fall can work if there is more than one person in the room. Kido et al [25] used thermal imaging sensors to differentiate between the normal activity and the falling activity in a toilet room where the temperature should be less than 31 • C. However, the applicability of the proposed method may be limited due to privacy issues.…”

Section: Environmental-sensor-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

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“…[190] also proposes a fall detection system for private homes by analysing the shape of the detected object. In [94] a fall detection system for bathrooms are proposed, using a thermal sensor mounted above the toilet.…”

Section: Detection and Tracking Of Humansmentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

608

269

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Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras.This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

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Fall detection in toilet rooms using thermal imaging sensors

Cited by 33 publications

References 4 publications

Use of Thermal Sensors for Fall Detection in a Simulated Toilet Environment

Use of Thermal Sensors for Fall Detection in a Simulated Toilet Environment

An Image-Based Fall Detection System for the Elderly

Thermal cameras and applications: a survey

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