Title :
Tai Chi motion recognition, embedding the HMM method on a wearable
Author :
Majoe, Dennis ; Widmer, Lars ; Tschiemer, Philip ; Gutknecht, Jürg
Author_Institution :
Comput. Syst. Inst., ETH Zurich, Zurich, Switzerland
Abstract :
Embedding complex mathematical algorithms in a wearable system requires a suitable software approach in order that the needs of the programmer and processor are met. This paper reports ongoing work in which wearable computing is combined with high level gesture recognition in order to examine body motions in natural environments. Tai Chi movements are recognized using a Hidden Markov Model (HMM) approach and the emphasis is given to the practical results obtained and the embedded approach.
Keywords :
gesture recognition; hidden Markov models; motion estimation; wearable computers; HMM method; Hidden Markov Model; Tai Chi motion recognition; complex mathematical algorithms; gesture recognition; processor needs; programmer needs; wearable computing system; Computer languages; Hidden Markov models; Magnetic field measurement; Magnetic sensors; Motion analysis; Motion measurement; Position measurement; Rotation measurement; Wearable computers; Wireless sensor networks; Hidden Markov Model based gesture recognition; Motion Capture; embedded system; wearable computer;
Conference_Titel :
Pervasive Computing (JCPC), 2009 Joint Conferences on
Conference_Location :
Tamsui, Taipei
Print_ISBN :
978-1-4244-5227-9
Electronic_ISBN :
978-1-4244-5228-6
DOI :
10.1109/JCPC.2009.5420163
