Abstract
We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.
| Original language | English |
|---|---|
| Article number | 8513487 |
| Journal | Journal of Advanced Transportation |
| Volume | 2018 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
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ASJC Scopus subject areas
- Automotive Engineering
- Economics and Econometrics
- Mechanical Engineering
- Computer Science Applications
- Strategy and Management
Cite this
Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features. / Lee, Kanghyu; Han, David K.; Ko, Hanseok.
In: Journal of Advanced Transportation, Vol. 2018, 8513487, 01.01.2018.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features
AU - Lee, Kanghyu
AU - Han, David K.
AU - Ko, Hanseok
PY - 2018/1/1
Y1 - 2018/1/1
N2 - We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.
AB - We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.
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U2 - 10.1155/2018/8513487
DO - 10.1155/2018/8513487
M3 - Article
AN - SCOPUS:85058927669
VL - 2018
JO - Journal of Advanced Transportation
JF - Journal of Advanced Transportation
SN - 0197-6729
M1 - 8513487
ER -