TY - JOUR
T1 - A method for extracting temporal parameters based on hidden markov models in body sensor networks with inertial sensors
AU - Guenterberg, Eric
AU - Yang, Allen Y.
AU - Ghasemzadeh, Hassan
AU - Jafari, Roozbeh
AU - Bajcsy, Ruzena
AU - Sastry, S. Shankar
N1 - Funding Information:
Manuscript received October 15, 2008; revised March 26, 2009. First published September 1, 2009; current version published November 4, 2009. This work was supported in part by The Team for Research in Ubiquitous Secure Technology, which receives support from the National Science Foundation under NSF Award CCF-0424422, Air Force Office of Scientific Research (AFOSR) (#FA9550-06-1-0244), Cisco, British Telecom, ESCHER, HP, IBM, iCAST, Intel, Microsoft, ORNL, Pirelli, Qualcomm, Sun, Symantec, Telecom Italia, and United Technologies, and in part by Army Research Office/Multidisciplinary University Research Initiative W911NF-06-1-0076.
PY - 2009/11
Y1 - 2009/11
N2 - Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using data from specific sensors, but for new sensors or sensing positions, new techniques must be developed. We introduce a generic method for temporal parameter extraction called the hidden Markov event model based on hidden Markov models. Our method constrains the state structure to facilitate precise location of key events. This method can be quickly adapted to new movements and new sensors/sensor placements. Furthermore, it generalizes well to subjects not used for training. A multiobjective optimization technique using genetic algorithms is applied to decrease error and increase cross-subject generalizability. Further, collaborative techniques are explored. We validate this method on a walking dataset by using inertial sensors placed on various locations on a human body. Our technique is designed to be computationally complex for training, but computationally simple at runtime to allow deployment on resource-constrained sensor nodes.
AB - Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using data from specific sensors, but for new sensors or sensing positions, new techniques must be developed. We introduce a generic method for temporal parameter extraction called the hidden Markov event model based on hidden Markov models. Our method constrains the state structure to facilitate precise location of key events. This method can be quickly adapted to new movements and new sensors/sensor placements. Furthermore, it generalizes well to subjects not used for training. A multiobjective optimization technique using genetic algorithms is applied to decrease error and increase cross-subject generalizability. Further, collaborative techniques are explored. We validate this method on a walking dataset by using inertial sensors placed on various locations on a human body. Our technique is designed to be computationally complex for training, but computationally simple at runtime to allow deployment on resource-constrained sensor nodes.
KW - Biped locomotion
KW - Body sensor networks
KW - Hidden Markov models
KW - Intelligent sensors
UR - http://www.scopus.com/inward/record.url?scp=70449578679&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449578679&partnerID=8YFLogxK
U2 - 10.1109/TITB.2009.2028421
DO - 10.1109/TITB.2009.2028421
M3 - Article
C2 - 19726268
AN - SCOPUS:70449578679
SN - 1089-7771
VL - 13
SP - 1019
EP - 1030
JO - IEEE Transactions on Information Technology in Biomedicine
JF - IEEE Transactions on Information Technology in Biomedicine
IS - 6
M1 - 5229323
ER -