Difference between revisions of "Movement Recognition"
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= Movement recognition in the Blackbox = | = Movement recognition in the Blackbox = | ||
| − | The Blackbox currently supports Laban Basic Effort recognition through the [http://wiki.iat.sfu.ca/BlackBox/index.php/EffortDetect EffortDetect] system. | + | The Blackbox currently supports Laban Basic Effort recognition through the [http://wiki.iat.sfu.ca/BlackBox/index.php/EffortDetect EffortDetect] system. For a discussion on LMA recognition in general, see the section on [[Sensor_Selection#Sensor-based_considerations_in_LMA_recognition| sensor-based considerations in LMA recognition]]. |
= Movement recognition outside the Blackbox = | = Movement recognition outside the Blackbox = | ||
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= References = | = 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. | |
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Latest revision as of 12:31, 23 December 2011
Movement recognition in the Blackbox
The Blackbox currently supports Laban Basic Effort recognition through the EffortDetect system. For a discussion on LMA recognition in general, see the section on sensor-based considerations in 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.
