Abstract
This paper proposes a biosignal distortion detection algorithm for a driver healthcare system based on a contact biosensor and a linked adaptive neuro-fuzzy inference system (ANFIS), and demonstrate its superiority using actual vehicle experiments. Contact biosensors are highly sensitive to vehicle vibration and turning. Although vehicle suspension contributes significantly to ride quality, vibration transfers to the driver and contact between the driver and biosensor can become unstable when executing a turn, causing the driver's biosignal to not be measured well. This study estimated the driver's biosignal state using acceleration, angular velocity, and slip ratio measurements obtained from sensor fusion. When the measurement exceeded a defined threshold, the driver healthcare system removed unreliable biosignal data. We adopted ANFIS to improve the proposed sensor fusion algorithm estimate accuracy for the driver's biosignal state and improved the healthcare system robustness to road conditions. The effectiveness of the proposed algorithm was demonstrated experimentally by comparing the system using sensor fusion and linked ANFIS.
| Original language | English |
|---|---|
| Pages (from-to) | 7725-7735 |
| Number of pages | 11 |
| Journal | Journal of Intelligent and Fuzzy Systems |
| Volume | 37 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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Keywords
- biosignal distortion detection
- driver healthcare system
- Linked adaptive neuro fuzzy inference system
- sensor fusion
ASJC Scopus subject areas
- Statistics and Probability
- Engineering(all)
- Artificial Intelligence
Cite this
Linked adaptive neuro-fuzzy inference system for biosignal distortion detection system. / Park, Jun Yong; Kim, Dong W.; Kang, Tae Koo; Lim, Myo Taeg.
In: Journal of Intelligent and Fuzzy Systems, Vol. 37, No. 6, 01.01.2019, p. 7725-7735.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Linked adaptive neuro-fuzzy inference system for biosignal distortion detection system
AU - Park, Jun Yong
AU - Kim, Dong W.
AU - Kang, Tae Koo
AU - Lim, Myo Taeg
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper proposes a biosignal distortion detection algorithm for a driver healthcare system based on a contact biosensor and a linked adaptive neuro-fuzzy inference system (ANFIS), and demonstrate its superiority using actual vehicle experiments. Contact biosensors are highly sensitive to vehicle vibration and turning. Although vehicle suspension contributes significantly to ride quality, vibration transfers to the driver and contact between the driver and biosensor can become unstable when executing a turn, causing the driver's biosignal to not be measured well. This study estimated the driver's biosignal state using acceleration, angular velocity, and slip ratio measurements obtained from sensor fusion. When the measurement exceeded a defined threshold, the driver healthcare system removed unreliable biosignal data. We adopted ANFIS to improve the proposed sensor fusion algorithm estimate accuracy for the driver's biosignal state and improved the healthcare system robustness to road conditions. The effectiveness of the proposed algorithm was demonstrated experimentally by comparing the system using sensor fusion and linked ANFIS.
AB - This paper proposes a biosignal distortion detection algorithm for a driver healthcare system based on a contact biosensor and a linked adaptive neuro-fuzzy inference system (ANFIS), and demonstrate its superiority using actual vehicle experiments. Contact biosensors are highly sensitive to vehicle vibration and turning. Although vehicle suspension contributes significantly to ride quality, vibration transfers to the driver and contact between the driver and biosensor can become unstable when executing a turn, causing the driver's biosignal to not be measured well. This study estimated the driver's biosignal state using acceleration, angular velocity, and slip ratio measurements obtained from sensor fusion. When the measurement exceeded a defined threshold, the driver healthcare system removed unreliable biosignal data. We adopted ANFIS to improve the proposed sensor fusion algorithm estimate accuracy for the driver's biosignal state and improved the healthcare system robustness to road conditions. The effectiveness of the proposed algorithm was demonstrated experimentally by comparing the system using sensor fusion and linked ANFIS.
KW - biosignal distortion detection
KW - driver healthcare system
KW - Linked adaptive neuro fuzzy inference system
KW - sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85077469025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077469025&partnerID=8YFLogxK
U2 - 10.3233/JIFS-182532
DO - 10.3233/JIFS-182532
M3 - Article
AN - SCOPUS:85077469025
VL - 37
SP - 7725
EP - 7735
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
SN - 1064-1246
IS - 6
ER -