TY - GEN
T1 - A phonological expression for physical movement monitoring in body sensor networks
AU - Ghasemzadeh, Hassan
AU - Barnes, Jaime
AU - Guenterberg, Eric
AU - Jafari, Roozbeh
PY - 2008
Y1 - 2008
N2 - Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for physical movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.
AB - Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for physical movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.
UR - https://www.scopus.com/pages/publications/67650682936
UR - https://www.scopus.com/pages/publications/67650682936#tab=citedBy
U2 - 10.1109/MAHSS.2008.4660059
DO - 10.1109/MAHSS.2008.4660059
M3 - Conference contribution
AN - SCOPUS:67650682936
SN - 9781424425754
T3 - 2008 5th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2008
SP - 58
EP - 68
BT - 2008 5th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2008
T2 - 2008 5th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2008
Y2 - 29 September 2008 through 2 October 2008
ER -