Abstract
Optical motion tracking has enhanced human movement analysis in medicine, biomechanics, and rehabilitation science by providing highly accurate joint angle measurements over time. However, analyzing the large amount of recorded data is challenging. The process is usually simplified by calculating descriptive measures, such as the minimum, mean, or maximum, from the time series data. We propose a novel technique for the analysis of human motion data, which considers the complete time series data and is based on the F-statistic traditionally used in medical and biomechanical studies. The time series data is modeled by a Hidden Markov Model (HMM) and the Fstatistic is reformulated using the Kullback-Leibler divergence for comparing HMMs. This provides a novel technique to enhance the analysis of human movement data and includes an automatic generation of group-specific trajectories to simplify visual data analysis. It is further suitable as time-series based, univariate feature selection technique in machine learning.
Original language | English |
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Title of host publication | Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 |
Pages | 3870-3875 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Dec 2013 |
Externally published | Yes |
Event | IEEE International Conference on Systems, Man and Cybernetics 2013 - Manchester, United Kingdom Duration: 13 Oct 2013 → 16 Oct 2013 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6689802 (IEEE Conference Proceedings) |
Publication series
Name | Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 |
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Conference
Conference | IEEE International Conference on Systems, Man and Cybernetics 2013 |
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Abbreviated title | SMC 2013 |
Country/Territory | United Kingdom |
City | Manchester |
Period | 13/10/13 → 16/10/13 |
Internet address |
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Keywords
- Biomechanics
- F-statistic
- Hidden Markov model
- Human movement analysis