Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization

Nyan, M.N.; Tay, Francis Eng Hock; Tan, Adeline; Seah, K.H.W.

doi:10.1016/j.medengphy.2005.11.008

Cited by 92 publications

(90 citation statements)

References 17 publications

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“…[5] and [6]). The idea of fall event detection is to detect the occurrence of a fall event through human motion characteristics prior to impact [7], during impact [8], or post impact [9]. Traditionally, fall detection as an alarming device is beneficial to ensure the timely arrival of remote medical assistance without or with minimal human intervention, thereby reducing injury-related complications and mortalities.…”

Section: Introductionmentioning

confidence: 99%

Trunk Angular Kinematics During Slip-Induced Backward Falls and Activities of Daily Living

Liu

Lockhart

2014

Journal of Biomechanical Engineering

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Prior to developing any specific fall detection algorithm, it is critical to distinguish the unique motion features associated with fall accidents. The current study aimed to investigate the upper trunk angular kinematics during slip-induced backward falls and activities of daily living (ADLs). Ten healthy older adults (age ¼ 75 6 6 yr (mean 6 SD)) were involved in a laboratory study. Sagittal trunk angular kinematics were measured using optical motion analysis system during normal walking, slip-induced backward falls, lying down, bending over, and various types of sitting down (SN). Trunk angular phase-plane plots were generated to reveal the motion features of falls. It was found that backward falls were characterized by a simultaneous occurrence of a slight trunk extension and an extremely high trunk extension velocity (peak average ¼ 139.7 deg/s), as compared to ADLs (peak average ¼ 84.1 deg/s). It was concluded that the trunk extension angular kinematics of falls were clearly distinguishable from those of ADLs from the perspective of angular phase-plane plot. Such motion features can be utilized in future studies to develop a new prior-to-impact fall detection algorithm.

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Section: Introductionmentioning

confidence: 99%

Trunk Angular Kinematics During Slip-Induced Backward Falls and Activities of Daily Living

Liu

Lockhart

2014

Journal of Biomechanical Engineering

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“…Therefore alternative methods are required, namely by using low voltage or mechanical excitation. [1]. The key challenge here is the increment of lead time before impact of a fall.…”

Section: Fall Preventionmentioning

confidence: 99%

“…MEMSWear I was initiated to improve the healthcare for the elderly through a 100% detection of falls [1]. The system comprises of micro sensors such as G-switch, accelerometers and gyroscopes with integrated circuits incorporated into a smart wear.…”

Section: Introductionmentioning

confidence: 99%

“…MEMSWear I was capable of detecting falls based on sensors threshold values. Once a fall is detected, the incorporated Bluetooth technology allows an alarm signal to be sent to a remote mobile device, which allows immediate deployment of assistance to assist the person [1]. Furthermore, to prevent any false alarm, algorithms were incorporated to distinguish eight commonly performed human activities such as walking, stair climbing, sitting and lying.…”

Section: Introductionmentioning

confidence: 99%

“…A hip protection device will be deployed to minimize injury should a fall occur which requires response within critical timing. From MEMSWear I, the time between a fall and impact is found to be 200ms as measured from a triaxial gyroscope [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Overview of MEMSWear II - Incorporating MEMS Technology into smart shirt for Geriatric care

Dagang

Hapipi

et al. 2006

J. Phys.: Conf. Ser.

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This paper presents a method of using smart sensors for continuous detection of vital physiological and physical gait signals that can be relayed to a fall prediction algorithm for predicting an imminent faint fall. A novel MEMS based BP sensor will be discussed briefly. Once an imminent faint fall is detected, fall prevention and injury minimization devices will be activated. A body area network consisting sensor circuits utilizing short range ISM communication will be used to enable signal transmission to a processor which acts as a communication gateway as well. This processor would then encode and send the signals via Bluetooth to designated devices to effect inform caregivers or family members.

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Sensors and Features for Assisted Living Technologies

Uddin

2022

Applied Machine Learning for Assisted Living

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Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization

Cited by 92 publications

References 17 publications

Trunk Angular Kinematics During Slip-Induced Backward Falls and Activities of Daily Living

Trunk Angular Kinematics During Slip-Induced Backward Falls and Activities of Daily Living

Overview of MEMSWear II - Incorporating MEMS Technology into smart shirt for Geriatric care

Sensors and Features for Assisted Living Technologies

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