@inproceedings{121,
  author = {Jake Leland and Ellen Stanfill and Josh Cherian and Tracy Hammond},
  title = {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 effective 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 different users. From this data, we identified 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 effective interventions to ensure seatbelt use.},
  year = {2021},
  journal = {Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems},
  chapter = {1},
  pages = {6},
  month = {05},
  url = {https://par.nsf.gov/biblio/10294626},
  doi = {10.1145/3411763.3451705},
}