Resolving Data Overload and Latency Issues in Multivariate Time-Series IoMT Data for Mental Health Monitoring
Pervasive healthcare services have evolved substantially in the recent years with IoMT rapidly changing the pace and scale of healthcare delivery. A promising application of IoMT is to fetch patterns of mental behaviour symptomatology based on bio-signals and transfer it to the corresponding hospita...
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Published in | IEEE sensors journal Vol. 21; no. 22; pp. 25421 - 25428 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
15.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Pervasive healthcare services have evolved substantially in the recent years with IoMT rapidly changing the pace and scale of healthcare delivery. A promising application of IoMT is to fetch patterns of mental behaviour symptomatology based on bio-signals and transfer it to the corresponding hospital or psychologist for remote monitoring. But the data volume & performance, device diversity & interoperability, hacking & unauthorized use and acceptance & adoption barriers still restrain the practical and competent use of these devices. This research presents a plausible solution to surmount the data overload and processing latency in real-time sensory data collected through wearable devices for mental health monitoring. We propose a modified k-medoid data clustering technique based on time-frame restricted intra-cluster similarity calculations to obtain a summarized version of the original benchmark WESAD dataset for which the degree of information lost is minimum. A CNN is then trained on this summarized dataset for classification of mental state into the baseline, stress and amusement categories. The results show a significant reduction in the average execution time by 34% with a comparable accuracy to the original dataset, thus offering prompt real-time healthcare analytics. |
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ISSN: | 1530-437X 1558-1748 |
DOI: | 10.1109/JSEN.2021.3095853 |