Recognizing Seatbelt-Fastening Activity Using Wearable Sensor Technology
Abstract
Many fatal car accidents involve victims who were not wearing a seatbelt, even though systems for detecting such behavior and intervening to correct it already exist. Activity recognition using wearable sensors has been previously applied to many health-related fields with high accuracy. In this paper, activity recognition is used to generate an algorithm for real-time recognition of putting on a seatbelt, using a smartwatch. Initial data was collected from twelve participants to determine the validity of the approach. Novel features were extracted from the data and used to classify the action, with a final accuracy of 1.000 and an F-measure of 1.000 using the MultilayerPerceptron classifier using laboratory collected data. Then, an iterative real-time recognition user study was conducted to investigate classification accuracy in a naturalistic setting. The F-measure of naturalistic classification was 0.825 with MultilayerPerceptron. This work forms the basis for further studies which will aim to provide user feedback to increase seatbelt use.
Subject
Gesture RecognitionMachine Learning
Wearable Technology
Smartwatch
Seatbelt
Ubiquitous Computing
Citation
Stanfill, Ellen R; Leland, Jake M (2017). Recognizing Seatbelt-Fastening Activity Using Wearable Sensor Technology. Undergraduate Research Scholars Program. Available electronically from https : / /hdl .handle .net /1969 .1 /196635.