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Detail
Journal Article
Accident detection algorithm using features associated with risk factors and acceleration data from stunt performers
- Authors
- Mingi Jeong, Sangyeoun Lee, Kang Bok Lee
- Issue Date
- 2022-08
- Citation
- ETRI Journal, v.44, no.4, pp.654-671
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.
- KSP Keywords
- Acceleration data, Accident detection, Accidental falls, Activities of Daily Living(ADLs), Comparative analysis, Detection Method, Detection algorithm, False Positive Rate, High performance, Integrated data, Intrusion detection system(IDS)
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
