Efficient energy smart sensor for fall detection based on accelerometer data and CNN model

Achour, Brahim; Filali, Idir; Belkadi, Malika; Laghrouche, Mourad

doi:10.1109/edis57230.2022.9996539

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…They proposed an algorithm and experimented to detect falls, trips, and portents (e.g., heavy footsteps, sudden knee movements, sudden swaying, abrupt body reflexes) by considering four different physiological statuses of the subjects (i.e., sleepiness, fatigue, normal, and inebriation). Achour et al [ 13 ] developed an accelerometer-data-based algorithm to detect worker falls with a focus on reducing power consumption by using sensor timers.…”

Section: Introductionmentioning

confidence: 99%

A GRU-Based Model for Detecting Common Accidents of Construction Workers

Dzeng,

Watanabe,

Hsueh

et al. 2024

Sensors

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Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13).

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Section: Introductionmentioning

confidence: 99%