TY - GEN
T1 - Smart Aging Wellness Sensor Networks
T2 - 2nd International Conference on Computing, Communications, and Cyber-Security, IC4S 2020
AU - Pandya, Sharnil
AU - Mistry, Mayur
AU - Kotecha, Ketan
AU - Sur, Anirban
AU - Ghanchi, Asif
AU - Patadiya, Vedant
AU - Limbachiya, Kuldeep
AU - Shivam, Anand
PY - 2021/5/24
Y1 - 2021/5/24
N2 - In the growing automation of existing world, activity modeling is being used in the field of technology to serve various purposes. One such field, which will be majorly benefited from daily activity modeling and life- living activities analysis, is monitoring of seasonal behavior pattern of elderly people, which can be further utilized in their remote health analysis and monitoring. Today’s demand is to develop a system with minimum human interaction and automatic anomaly detection and alert system. The proposed research work emphasizes to diagnose elderly persons daily behavioral patterns by observing their day-to-day routine activities with respect to time, location and context. To grow the accurateness of the structure, numerous sensing as well as actuator units have been deployed in elderly homes. Popular this research paper, we have recommended a unique sensing fusion technique to monitor seasonal, social, weather related and wellness observations of routine tasks. A novel daily activity learning model has been proposed which can record contextual data observations of various locations of a smart home and alert caretakers in the case of anomaly detection. We have analyzed monthly data of two old-aged smart homes with more than 5000 test samples. Results acquired from the investigation validate the accuracy and the efficiency of the proposed system which are recorded for 20 activities.
AB - In the growing automation of existing world, activity modeling is being used in the field of technology to serve various purposes. One such field, which will be majorly benefited from daily activity modeling and life- living activities analysis, is monitoring of seasonal behavior pattern of elderly people, which can be further utilized in their remote health analysis and monitoring. Today’s demand is to develop a system with minimum human interaction and automatic anomaly detection and alert system. The proposed research work emphasizes to diagnose elderly persons daily behavioral patterns by observing their day-to-day routine activities with respect to time, location and context. To grow the accurateness of the structure, numerous sensing as well as actuator units have been deployed in elderly homes. Popular this research paper, we have recommended a unique sensing fusion technique to monitor seasonal, social, weather related and wellness observations of routine tasks. A novel daily activity learning model has been proposed which can record contextual data observations of various locations of a smart home and alert caretakers in the case of anomaly detection. We have analyzed monthly data of two old-aged smart homes with more than 5000 test samples. Results acquired from the investigation validate the accuracy and the efficiency of the proposed system which are recorded for 20 activities.
KW - Activities of daily living
KW - Activity modeling
KW - Anomaly detection
KW - Cognitive computing
UR - http://www.scopus.com/inward/record.url?scp=85111248696&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-0733-2_1
DO - 10.1007/978-981-16-0733-2_1
M3 - Conference contribution
AN - SCOPUS:85111248696
SN - 9789811607325
T3 - Lecture Notes in Networks and Systems
SP - 3
EP - 21
BT - Proceedings of Second International Conference on Computing, Communications, and Cyber-Security
A2 - Singh, Pradeep Kumar
A2 - Wierzchoń, Sławomir T.
A2 - Tanwar, Sudeep
A2 - Ganzha, Maria
A2 - Rodrigues, Joel J. P. C.
PB - Springer
CY - Singapore
Y2 - 3 October 2020 through 4 October 2020
ER -