Print Email Facebook Twitter Assessment of Vigilance Level during Work Title Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability Author Wang, Hanyu (Northwestern Polytechnical University) Chen, Dengkai (Northwestern Polytechnical University) Huang, Y. (TU Delft Design Conceptualization and Communication; Northwestern Polytechnical University) Zhang, Yahan (Shanghai Jiao Tong University) Qiao, Yidan (Northwestern Polytechnical University) Xiao, Jianghao (Northwestern Polytechnical University) Xie, Ning (Northwestern Polytechnical University) Fan, Hao (Zhejiang University) Date 2023 Abstract This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train parameters of the modified hidden Markov model for a vigilance assessment. The data were collected to train the model using the Baum–Welch algorithm and to obtain the state transition probability matrix (Formula presented.) and the observation probability matrix (Formula presented.). Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and the Viterbi algorithm was used to find the optimal state, which was compared with the actual state. The constructed vigilance assessment model had a high accuracy rate, and the accuracy rate of data prediction for these three volunteers exceeded 80%. Our approach can be used in wearable products to improve their vigilance level assessment functionality or in other fields that have key positions with high concentration requirements and monotonous repetitive work. Subject heart rate variabilityhidden Markov modelpsychomotor vigilance taskvigilancevisual search taskwearable device To reference this document use: http://resolver.tudelft.nl/uuid:f63eedfa-b572-4ed8-ba67-baf1dfbb35b2 DOI https://doi.org/10.3390/brainsci13040638 Source Brain Sciences, 13 (4) Part of collection Institutional Repository Document type journal article Rights © 2023 Hanyu Wang, Dengkai Chen, Y. Huang, Yahan Zhang, Yidan Qiao, Jianghao Xiao, Ning Xie, Hao Fan Files PDF brainsci_13_00638_v2.pdf 3.45 MB Close viewer /islandora/object/uuid:f63eedfa-b572-4ed8-ba67-baf1dfbb35b2/datastream/OBJ/view