Movement Recognition

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_Selection#Sensor-based_considerations_in_LMA_recognition 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.