TY - GEN
T1 - BESI
T2 - 2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017
AU - Alam, Ridwan
AU - Dugan, Joshua
AU - Homdee, Nutta
AU - Gandhi, Neeraj
AU - Ghaemmaghami, Benjamin
AU - Meda, Harshitha
AU - Bankole, Azziza
AU - Anderson, Martha
AU - Gong, Jiaqi
AU - Smith-Jackson, Tonya
AU - Lach, John
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/14
Y1 - 2017/8/14
N2 - Advances in sensing, wireless communication, and data analytics have enabled various monitoring systems for smart health applications. However, many challenges remain to deploy such systems in actual homes, such as achieving robustness, unobtrusiveness, fault tolerance, privacy, and minimal user burden. This paper presents how these challenges were overcome in the realization and successful prototype deployment of the Behavioral and Environmental Sensing and Intervention (BESI) system. BESI is designed to sense behavioral activities using wearables and monitor environmental parameters with in-home sensors. With such data, behavioral patterns can then be modeled to determine associations with environmental attributes and, when appropriate, real-time notifications or interventions can be made based on these models. Challenges in building platforms with residential deployment constraints are discussed. BESI is currently deployed for an in-home study on dementia, and the results are presented to illustrate data collection procedures and system performance.
AB - Advances in sensing, wireless communication, and data analytics have enabled various monitoring systems for smart health applications. However, many challenges remain to deploy such systems in actual homes, such as achieving robustness, unobtrusiveness, fault tolerance, privacy, and minimal user burden. This paper presents how these challenges were overcome in the realization and successful prototype deployment of the Behavioral and Environmental Sensing and Intervention (BESI) system. BESI is designed to sense behavioral activities using wearables and monitor environmental parameters with in-home sensors. With such data, behavioral patterns can then be modeled to determine associations with environmental attributes and, when appropriate, real-time notifications or interventions can be made based on these models. Challenges in building platforms with residential deployment constraints are discussed. BESI is currently deployed for an in-home study on dementia, and the results are presented to illustrate data collection procedures and system performance.
KW - Cyber-physical-system
KW - Deployment
KW - Health Applications
KW - Intervention
KW - Residential
KW - Sensing
KW - Wearable
UR - https://www.scopus.com/pages/publications/85029391351
U2 - 10.1109/CHASE.2017.73
DO - 10.1109/CHASE.2017.73
M3 - Conference contribution
AN - SCOPUS:85029391351
T3 - Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017
SP - 147
EP - 156
BT - Proceedings - 2017 IEEE 2nd International Conference on Connected Health
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 July 2017 through 19 July 2017
ER -