Design of fall detection system with floor pressure and infrared image

Tzeng, Huan-Wen; Chen, Mei-Yung; Chen, Jai-Yu

doi:10.1109/icsse.2010.5551751

Cited by 25 publications

(7 citation statements)

References 8 publications

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“…Vision-based sensors collect movement data by tracking tools and determining a participant's body image tilt or particular skeletal inscriptions from video or image data [8] to notice a fall. In most cases, ambient sensors detect falls by gathering infrared [9], radar [10], and other data from the surrounding sensor. Although it poses no privacy concerns, this has a slightly higher cost.…”

Section: Sensor-based Fall Detectionmentioning

confidence: 99%

Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Hnoohom¹,

Jitpattanakul²

2023

Intelligent Automation &Amp; Soft Computing

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Falls are the contributing factor to both fatal and nonfatal injuries in the elderly. Therefore, pre-impact fall detection, which identifies a fall before the body collides with the floor, would be essential. Recently, researchers have turned their attention from post-impact fall detection to pre-impact fall detection. Preimpact fall detection solutions typically use either a threshold-based or machine learning-based approach, although the threshold value would be difficult to accurately determine in threshold-based methods. Moreover, while additional features could sometimes assist in categorizing falls and non-falls more precisely, the estimated determination of the significant features would be too time-intensive, thus using a significant portion of the algorithm's operating time. In this work, we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial sensors. The proposed network was introduced to address the limitations of feature extraction, threshold definition, and algorithm complexity. After training on a large-scale motion dataset, the KFall dataset, and straightforward evaluation with standard metrics, the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%, respectively. In addition, we have investigated fall detection's performances of three state-of-the-art deep learning models such as a convolutional neural network (CNN), a long short-term memory neural network (LSTM), and a hybrid model (CNN-LSTM). The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models (CNN, LSTM, and CNN-LSTM) with significant improvements.

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Section: Sensor-based Fall Detectionmentioning

confidence: 99%

Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Hnoohom¹,

Jitpattanakul²

2023

Intelligent Automation &Amp; Soft Computing

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“…Ambience sensor based fall detection systems have also been studied. Different sensors or devices such as doppler radar [19], passive infrared sensors [20,37,22,5], pressure sensors [35,14], sound sensors [18] and Wi-Fi routers [36] have been tested for fall detection.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection with Unobtrusive Infrared Array Sensors

Fan

Zhang

Leung

et al. 2018

Lecture Notes in Electrical Engineering

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“…A variety of ambient sensors have been studied, including floor vibration sensors (Tzeng, Chen, & Chen, 2010), acoustic sensors (Li, Ho, & Popescu, 2012), and video systems. To be effective, floor sensors must be tuned to the particular deployment environment and can be fooled by heavy furniture, whereas acoustic sensors must contend with competing background sounds such as televisions that may be set at high volumes by older adults.…”

Section: Fall Detectionmentioning

confidence: 99%

Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses’ Perceptions and Lessons Learned

et al. 2018

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Hospitalized older adults are at high risk of falling. The HELPER system is a ceiling-mounted fall detection system that sends an alert to a smartphone when a fall is detected. This article describes the performance of the HELPER system, which was pilot tested in a geriatric mental health hospital. The system's accuracy in detecting falls was measured against the hospital records documenting falls. Following the pilot test, nurses were interviewed regarding their perceptions of this technology. In this study, the HELPER system missed one documented fall but detected four falls that were not documented. Although sensitivity (.80) of the system was high, numerous false alarms brought down positive predictive value (.01). Interviews with nurses provided valuable insights based on the operation of the technology in a real environment; these and other lessons learned will be particularly valuable to engineers developing this and other health and social care technologies.

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Design of fall detection system with floor pressure and infrared image

Cited by 25 publications

References 8 publications

Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Fall Detection with Unobtrusive Infrared Array Sensors

Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses’ Perceptions and Lessons Learned

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