Movement Recognition

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.)