TY - GEN
T1 - Collaborative signal processing for action recognition in body sensor networks
T2 - 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010
AU - Ghasemzadeh, Hassan
AU - Loseu, Vitali
AU - Jafari, Roozbeh
PY - 2010
Y1 - 2010
N2 - Body sensor networks are emerging as a promising platform for remote human monitoring. With the aim of extracting bio-kinematic parameters from distributed body-worn sensors, these systems require collaboration of sensor nodes to obtain relevant information from an overwhelmingly large volume of data. Clearly, efficient data reduction techniques and distributed signal processing algorithms are needed. In this paper, we present a data processing technique that constructs motion transcripts from inertial sensors and identifies human movements by taking collaboration between the nodes into consideration. Transcripts of basic motions, called primitives, are built to reduce the complexity of the sensor data. This model leads to a distributed algorithm for segmentation and action recognition. We demonstrate the effectiveness of our framework using data collected from five normal subjects performing ten transitional movements. The results clearly illustrate the effectiveness of our framework. In particular, we obtain a classification accuracy of 84.13% with only one sensor node involved in the classification process.
AB - Body sensor networks are emerging as a promising platform for remote human monitoring. With the aim of extracting bio-kinematic parameters from distributed body-worn sensors, these systems require collaboration of sensor nodes to obtain relevant information from an overwhelmingly large volume of data. Clearly, efficient data reduction techniques and distributed signal processing algorithms are needed. In this paper, we present a data processing technique that constructs motion transcripts from inertial sensors and identifies human movements by taking collaboration between the nodes into consideration. Transcripts of basic motions, called primitives, are built to reduce the complexity of the sensor data. This model leads to a distributed algorithm for segmentation and action recognition. We demonstrate the effectiveness of our framework using data collected from five normal subjects performing ten transitional movements. The results clearly illustrate the effectiveness of our framework. In particular, we obtain a classification accuracy of 84.13% with only one sensor node involved in the classification process.
KW - body sensor networks
KW - collaborative signal processing
KW - distributed classification
KW - motion transcripts
UR - http://www.scopus.com/inward/record.url?scp=77954494367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954494367&partnerID=8YFLogxK
U2 - 10.1145/1791212.1791242
DO - 10.1145/1791212.1791242
M3 - Conference contribution
AN - SCOPUS:77954494367
SN - 9781605589886
T3 - Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10
SP - 244
EP - 255
BT - Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10
Y2 - 12 April 2010 through 16 April 2010
ER -