Movement Recognition
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Movement recognition in the Blackbox
The Blackbox currently supports Laban Basic Effort recognition through the EffortDetect system.
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.)