TY - GEN
T1 - PEMAR
T2 - 13th IEEE International Conference on Pervasive Computing and Communication, PerCom Workshops 2015
AU - Vaka, Prakash
AU - Shen, Feichen
AU - Chandrashekar, Mayanka
AU - Lee, Yugyung
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/24
Y1 - 2015/6/24
N2 - The growing affordability of smart phones and mobile devices has only added to this trend by encouraging prolonged durations of inactivity. In this paper, we present a middleware, called the Pervasive Middleware for Activity Recognition (PEMAR) that aims to increase the level of physical activity by creating a middleware for active games on mobile devices. For the PEMAR application, we present a human centered and adaptive approach that recognizes and learns human activities continuously by employing an activity library. The activity models in the library will be annotated with patterns of human activities and their contexts for general usage of activity models. This will be beneficial to many pervasive applications in terms of the availability of the accurate activity models as well as the reduction of burden for gesture training. The PEMAR middleware is composed of the following: (1) semantic models for human activity, (2) activity analysis, (3) activity recognition, (4) adaptation of motion models, and (5) motion based game applications. We evaluate the proposed PEMAR model in terms of its recognition accuracy and performance. In addition, we demonstrate the usage of the middleware through interactive activity gaming applications.
AB - The growing affordability of smart phones and mobile devices has only added to this trend by encouraging prolonged durations of inactivity. In this paper, we present a middleware, called the Pervasive Middleware for Activity Recognition (PEMAR) that aims to increase the level of physical activity by creating a middleware for active games on mobile devices. For the PEMAR application, we present a human centered and adaptive approach that recognizes and learns human activities continuously by employing an activity library. The activity models in the library will be annotated with patterns of human activities and their contexts for general usage of activity models. This will be beneficial to many pervasive applications in terms of the availability of the accurate activity models as well as the reduction of burden for gesture training. The PEMAR middleware is composed of the following: (1) semantic models for human activity, (2) activity analysis, (3) activity recognition, (4) adaptation of motion models, and (5) motion based game applications. We evaluate the proposed PEMAR model in terms of its recognition accuracy and performance. In addition, we demonstrate the usage of the middleware through interactive activity gaming applications.
KW - Activity Recognition
KW - Motion-based Game Apps
KW - Pervaisve Middelware
KW - Real-time Data analytics
UR - http://www.scopus.com/inward/record.url?scp=84946088286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946088286&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2015.7134073
DO - 10.1109/PERCOMW.2015.7134073
M3 - Conference contribution
AN - SCOPUS:84946088286
T3 - 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
SP - 409
EP - 414
BT - 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 March 2015 through 27 March 2015
ER -