Identification of fastening compliance for intelligent work-at-height safety belts based on multimodal time-series analysis
TL;DR
An intelligent safety belt model with self-identification and self-sensing capabilities is developed, which significantly improves safety supervision for work at height and provides proactive protection for personnel performing elevated tasks.
Abstract
Working-at-height safety is a critical concern in domains such as industrial production and power maintenance. Traditional safety belts only offer passive protection and lack real-time monitoring of proper wearing and scenario suitability, which may cause accidents due to non-standard fastening or environmental misjudgment. To address this issue, this paper develops an intelligent safety belt model with self-identification and self-sensing capabilities. The model combines multisensor fusion techniques with temporal analysis models to achieve precise recognition of working-at-height scenarios and real-time assessment of safety belt fastening compliance. A collaborative fusion method for multi-source sensor data is established. Recurrent neural networks are used to analyze the time-series data collected from the various sensors, capturing the temporal dependencies of action sequences. Convolutional neural networks are employed to extract spatial features from proximity-sensor outputs, enabling accurate assessment of the spatial correctness of safety belt attachment points. An attention-based fusion prediction is applied to combine temporal and spatial representations, enabling accurate assessment of safety belt fastening compliance during working-at-height operations. Experimental results show that the proposed model significantly improves safety supervision for work at height. It also provides proactive protection for personnel performing elevated tasks.