DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Xiaoqun | ko |
dc.contributor.author | Jang, Jake | ko |
dc.contributor.author | Xiong, Shuping | ko |
dc.date.accessioned | 2021-07-28T01:30:10Z | - |
dc.date.available | 2021-07-28T01:30:10Z | - |
dc.date.created | 2021-07-21 | - |
dc.date.created | 2021-07-21 | - |
dc.date.issued | 2021-07-26 | - |
dc.identifier.citation | 12th International Conference on Applied Human Factors and Ergonomics (AHFE 2021), v.273, pp.278 - 285 | - |
dc.identifier.isbn | 9783030807122 | - |
dc.identifier.issn | 2367-3370 | - |
dc.identifier.uri | http://hdl.handle.net/10203/286869 | - |
dc.description.abstract | Falls are the leading cause of death and non-fatal injuries among older people, thus pre-impact fall detection that detects a fall before body-ground impact is of crucial significance. 32 young subjects performed different types of falls and daily activities, and their motion data was recorded by a wearable inertial sensor to establish a large-scale motion dataset. Five commonly used machine learning algorithms were applied and compared thoroughly in terms of accuracy and practicality for pre-impact fall detection. Results showed that in terms of sensitivity, specificity and lead time, both support vector machine (SVM: 99.77%, 93.10%, 362 ± 150 ms) and random forest (RF: 100%, 92.90%, 357 ± 145 ms) achieved better results than other 3 models. SVM showed a much shorter latency (66 ms) than RF (1047 ms) running in a microcontroller. Those findings suggest that SVM has the highest potential to be embedded into a wearable sensor based system to provide real-time fall protection for the elderly. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. | - |
dc.language | English | - |
dc.publisher | AHFE | - |
dc.title | Machine Learning-based Pre-impact Fall Detection and Injury Prevention for the Elderly with Wearable Inertial Sensors | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85112292711 | - |
dc.type.rims | CONF | - |
dc.citation.volume | 273 | - |
dc.citation.beginningpage | 278 | - |
dc.citation.endingpage | 285 | - |
dc.citation.publicationname | 12th International Conference on Applied Human Factors and Ergonomics (AHFE 2021) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1007/978-3-030-80713-9_36 | - |
dc.contributor.localauthor | Xiong, Shuping | - |
dc.contributor.nonIdAuthor | Yu, Xiaoqun | - |
dc.contributor.nonIdAuthor | Jang, Jake | - |
dc.type.journalArticle | Conference Paper | - |
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