Recognizing Seatbelt-Fastening Behavior with Wearable Technology and Machine Learning

Abstract

Nearly 1.35 million people are killed in automobile accidents every year, and nearly half of all individuals involved in these accidents were not wearing their seatbelt at the time of the crash. This lack of safety precaution occurs in spite of the numerous safety sensors and warning indicators embedded within modern vehicles. This presents a clear need for more efective methods of encouraging consistent seatbelt use. To that end, this work leverages wearable technology and activity recognition techniques to detect when individuals have buckled their seatbelt. To develop such a system, we collected smartwatch data from 26 diferent users. From this data, we identifed trends which inspired the development of novel features. Using these features, we trained models to identify the motion of fastening a seatbelt in real-time. This model serves as the basis for future work in which systems can provide personalized and efective interventions to ensure seatbelt use. CCS CONCEPTS• Computing methodologies → Machine learning algorithms; • Human-centered computing → Smartphones; Mobile devices; Mobile computing.

Exploring the Impact of the NULL Class on In-the-Wild Human Activity Recognition

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A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm

“…Scholars have addressed the issue of safety belt compliance through two distinct approaches. The first approach involves the installation of cameras along highways to monitor the usage of safety belts [3][4][5][6][7]. However, the installed cameras may have to consider the fact that there are two modes of computer vision models (single-stage and two-stage mode), and, therefore, the Stage 1 computer vision models are the ones recommended for real-time detection [9,10].…”