Resolving Data Overload and Latency Issues in Multivariate Time-Series IoMT Data for Mental Health Monitoring

• Published in: IEEE sensors journal Vol. 21; no. 22; pp. 25421 - 25428
• Main Authors: Gupta, Divya, Bhatia, M. P. S., Kumar, Akshi
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; CNN; Computational modeling; Data models; Datasets; Electronic devices; Health care; Health services; Internet of medical things; Interoperability; IoMT; k-medoids; Medical services; Mental health; mental state; Monitoring; Multivariate analysis; Overloading; Real time; Real-time systems; Remote monitoring; Sensors; Wearable technology
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3095853

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.