Resolving data overload and latency issues in multivariate time-series IoMT data for mental health monitoring

Gupta, D, Bhatia, MPS and Kumar, A ORCID: https://orcid.org/0000-0003-4263-7168 (2021) Resolving data overload and latency issues in multivariate time-series IoMT data for mental health monitoring. IEEE Sensors Journal, 21 (22). pp. 25421-25428. ISSN 1530-437X

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Abstract

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.