TY - GEN
T1 - AI-based Detection of Signs of Depression from Physiological Data obtained from Health Trackers
AU - Panindre, Prabodh
AU - Mandal, Anurag
AU - Paradkar, Manasi
AU - Kumar, Sunil
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - According to the National Institute of Mental Health, Major Depressive Disorder affected an estimated 21.0 million American adults in 2020, which represents 8.4% of the U.S. population aged 18 or older in a given year. Even though the percentage is substantial, it reflects only the diagnosed cases. Most depression cases remain undiagnosed and thus untreated. Real-time monitoring of physiological indicators of depression using wearable health monitoring devices can help increase the chances of early detection and eventual treatment. In this research, various Artificial Intelligence algorithms are developed to look for signs of stress and anomalies in activity patterns from the data captured by wearable health devices. The Random Forest algorithm performed well in detecting depression from users' activity levels, while the K-Nearest Neighbours algorithm detected stress, one of the key indicators of depression, with an accuracy of 96.2% from Heart Rate variability. This research takes advantage of real-time access to one's physiological data to minimize the number of undiagnosed depression cases.
AB - According to the National Institute of Mental Health, Major Depressive Disorder affected an estimated 21.0 million American adults in 2020, which represents 8.4% of the U.S. population aged 18 or older in a given year. Even though the percentage is substantial, it reflects only the diagnosed cases. Most depression cases remain undiagnosed and thus untreated. Real-time monitoring of physiological indicators of depression using wearable health monitoring devices can help increase the chances of early detection and eventual treatment. In this research, various Artificial Intelligence algorithms are developed to look for signs of stress and anomalies in activity patterns from the data captured by wearable health devices. The Random Forest algorithm performed well in detecting depression from users' activity levels, while the K-Nearest Neighbours algorithm detected stress, one of the key indicators of depression, with an accuracy of 96.2% from Heart Rate variability. This research takes advantage of real-time access to one's physiological data to minimize the number of undiagnosed depression cases.
KW - Artificial Intelligence
KW - Depression
KW - Mental health
KW - Smart Electronic healthcare
KW - Wearable Health Monitoring Systems
UR - http://www.scopus.com/inward/record.url?scp=85163666448&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163666448&partnerID=8YFLogxK
U2 - 10.1109/ICAAIC56838.2023.10140310
DO - 10.1109/ICAAIC56838.2023.10140310
M3 - Conference contribution
AN - SCOPUS:85163666448
T3 - Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
SP - 644
EP - 649
BT - Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
Y2 - 4 May 2023 through 6 May 2023
ER -