{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T02:34:21Z","timestamp":1775010861304,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0012451"],"award-info":[{"award-number":["P0012451"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in \u201cpast information\u201d through LSTM by regarding the driver motion as time-series data.<\/jats:p>","DOI":"10.3390\/s21196541","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"6541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags"],"prefix":"10.3390","volume":"21","author":[{"given":"So-Hyeon","family":"Jo","sequence":"first","affiliation":[{"name":"Department of Control and Instrumentation Engineering, Pukyong National University, Busan 48513, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joo","family":"Woo","sequence":"additional","affiliation":[{"name":"Department of Control and Instrumentation Engineering, Pukyong National University, Busan 48513, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gi-Sig","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Control and Instrumentation Engineering, Pukyong National University, Busan 48513, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9261-0716","authenticated-orcid":false,"given":"Baek-Soon","family":"Kwon","sequence":"additional","affiliation":[{"name":"School of Mechanical System Engineering, Kunsan National University, Gunsan 54150, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae-Hoon","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of IT, Information and Control Engineering Information and Control Engineering Major, Kunsan National University, Gunsan 54150, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Thompson, D.C., Rivara, F., and Thompson, R. 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