TY - GEN
T1 - Demo abstract
T2 - 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015
AU - Salekin, Asif
AU - Wang, Hongning
AU - Stankovic, John
N1 - Funding Information:
This work was supported, in part, by NSF grant CNS-1319302.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - Many elderly who are suffering from dementia are also suffering from agitation. While most assisted living facilities and home health care situations rely upon caregivers to monitor and record agitation of their patients, the accuracy is limited because the caregiver must be present during the agitation and must record the event properly. Accurate 24-7 data would help physicians with improved diagnoses and care. To solve this problem we developed KinVocal, a system that continuously monitors and detects agitated vocal events and can be used for the elderly population suffering from dementia. KinVocal, using a novel combination of acoustic signal processing and multiple text mining techniques, automatically detects and records the 8 major vocal agitations for dementia patients as defined by the medical community. This includes: constant unwarranted request for attention or help, making verbal sexual advances, crying, screaming, laughing, cursing, speaking in repetitive sentences, and negativism. The novelty of KinVocal includes the comprehensiveness of addressing all 8 vocal events, using the text of the vocalizations only when accurate, combining text and acoustic features when necessary, and employing text mining and feature identification. A comprehensive performance evaluation includes using data from Youtube and movies, controlled experiments, and real in-home deployments. The results show high accuracy for all 8 vocal events.
AB - Many elderly who are suffering from dementia are also suffering from agitation. While most assisted living facilities and home health care situations rely upon caregivers to monitor and record agitation of their patients, the accuracy is limited because the caregiver must be present during the agitation and must record the event properly. Accurate 24-7 data would help physicians with improved diagnoses and care. To solve this problem we developed KinVocal, a system that continuously monitors and detects agitated vocal events and can be used for the elderly population suffering from dementia. KinVocal, using a novel combination of acoustic signal processing and multiple text mining techniques, automatically detects and records the 8 major vocal agitations for dementia patients as defined by the medical community. This includes: constant unwarranted request for attention or help, making verbal sexual advances, crying, screaming, laughing, cursing, speaking in repetitive sentences, and negativism. The novelty of KinVocal includes the comprehensiveness of addressing all 8 vocal events, using the text of the vocalizations only when accurate, combining text and acoustic features when necessary, and employing text mining and feature identification. A comprehensive performance evaluation includes using data from Youtube and movies, controlled experiments, and real in-home deployments. The results show high accuracy for all 8 vocal events.
KW - Dementia
KW - Text mining
KW - Vocal agitation
KW - Word sense
UR - http://www.scopus.com/inward/record.url?scp=84962898734&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962898734&partnerID=8YFLogxK
U2 - 10.1145/2809695.2817853
DO - 10.1145/2809695.2817853
M3 - Conference contribution
AN - SCOPUS:84962898734
T3 - SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
SP - 459
EP - 460
BT - SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 1 November 2015 through 4 November 2015
ER -