Publication: Study of drowsiness from simple physiological signals testing: A signal processing perspective
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Issued Date
2020
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-85084044635
Rights Holder(s)
Scopus
Bibliographic Citation
2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. (2020), p.738-741
Suggested Citation
Aeimpreeda N., Sukaimod P., Khongsabai P., Thothong C., Sueaseenak D. Study of drowsiness from simple physiological signals testing: A signal processing perspective. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. (2020), p.738-741. doi:10.1109/ICAIIC48513.2020.9065035 Retrieved from: https://hdl.handle.net/20.500.14740/4657
Abstract
This paper reports a study of simple physiological signals that are electrodermal activity, pulse rate, and head nodding in drowsiness state and normal state. For the experiment protocol, the subjects were sat in a dim and quiet place and physiological signals were collected for 15 minutes by using Biopac MP36. A questionnaire was used to assess subject's feeling before and after the experiment. To classify the state of drowsiness, we have followed the criteria of previous study. Low-pass filter 2-5 Hz was used when pulse rate and electrodermal activity were recorded. In first row of a graph is an electrodermal activity that does not pass filter this is will define about the filter Low-pass band in how many hertz we use to filter in this electrodermal activity graph. All of data in pulse rate and electrodermal activity that we collected is from Biopac MP36. Then we collected head nodding in degree from Arduino mega with acceleration sensor module and example of data is in Figure 3. Thus, this paper uses all collected data and questionnaires to be observed and studied all signal and trend about the change rate in drowsiness state and normal state of people. All in all, we found that Electrodermal activity has their own platform represent to drowsiness stage. Electrodermal activity signals decreasing continually similar graph of cos θ and stop going down if human is sleeping peacefully. For pulse rate signals, the graph always fluctuated during drowsiness and normal stage. Head nodding cannot define a differentiate between normal stage and drowsiness stage. © 2020 IEEE.