Difference between revisions of "Movement Recognition"
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| − | + | Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H., & Celler, B. G. (2006). Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Information Technology in Biomedicine, IEEE Transactions on, 10(1), 156–167. | |
| − | + | Mathie, M. J., Celler, B. G., Lovell, N. H., & Coster, A. C. F. (2004). Classification of basic daily movements using a triaxial accelerometer. Medical & Biological Engineering & Computing, 42(5), 679-687. doi:10.1007/BF02347551 | |
| − | + | Morency, L. P., Quattoni, A., & Darrell, T. (2007). Latent-dynamic discriminative models for continuous gesture recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–8). | |
| − | + | Sekine, M., Tamura, T., Togawa, T., & Fukui, Y. (2000). Classification of waist-acceleration signals in a continuous walking record. Medical Engineering & Physics, 22(4), 285-291. doi:16/S1350-4533(00)00041-2 | |
| − | + | Taylor, G. W., & Hinton, G. E. (2009). Factored conditional restricted boltzmann machines for modeling motion style. Proceedings of the 26th annual international conference on machine learning (pp. 1025–1032). | |
| − | + | Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10(8), 7772–7788. | |
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Revision as of 12:27, 23 December 2011
Movement recognition in the Blackbox
The Blackbox currently supports Laban Basic Effort recognition through the EffortDetect system. See also the discussion below on LMA recognition.
Movement recognition outside the Blackbox
In general, a variety of heuristic techniques are applicable to movement information derived from sensor data. The following techniques below represent some that have been reported in the literature:
- Frequency-domain analysis (Yang & Hsu, 2010)
- Analysis of variance
- Analysis of frequency peaks
- Discrete wavelet transform(Sekine, Tamura, Togawa, & Fukui, 2000)
- Signal magnitude area (Karantonis, Narayanan, M. Mathie, Lovell, & Celler, 2006)
- Statistical approaches (Yang & Hsu, 2010)
- Decision trees (M. J. Mathie, Celler, Lovell, & Coster, 2004)
- k-nearest neighbor
- support vector machines
- Naïve Bayes classifier
- Gaussian mixture model
- Hidden Markov models
- Dynamic Conditional Random Field (Morency, Quattoni, & Darrell, 2007)
- Boltzmann machines (Taylor & Hinton, 2009)
(Note: Dynamic conditional random fields and Boltzmann machines were suggested by AAAI reviewers for any future versions of EffortDetect.)
References
Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H., & Celler, B. G. (2006). Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Information Technology in Biomedicine, IEEE Transactions on, 10(1), 156–167.
Mathie, M. J., Celler, B. G., Lovell, N. H., & Coster, A. C. F. (2004). Classification of basic daily movements using a triaxial accelerometer. Medical & Biological Engineering & Computing, 42(5), 679-687. doi:10.1007/BF02347551
Morency, L. P., Quattoni, A., & Darrell, T. (2007). Latent-dynamic discriminative models for continuous gesture recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–8).
Sekine, M., Tamura, T., Togawa, T., & Fukui, Y. (2000). Classification of waist-acceleration signals in a continuous walking record. Medical Engineering & Physics, 22(4), 285-291. doi:16/S1350-4533(00)00041-2
Taylor, G. W., & Hinton, G. E. (2009). Factored conditional restricted boltzmann machines for modeling motion style. Proceedings of the 26th annual international conference on machine learning (pp. 1025–1032).
Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10(8), 7772–7788.
