Article thumbnail

Classification of Upper Limb Motions from Around-Shoulder Muscle Activities: Hand Biofeedback

By Jose González, Yuse Horiuchi and Wenwei Yu


Mining information from EMG signals to detect complex motion intention has attracted growing research attention, especially for upper-limb prosthetic hand applications. In most of the studies, recordings of forearm muscle activities were used as the signal sources, from which the intention of wrist and hand motions were detected using pattern recognition technology. However, most daily-life upper limb activities need coordination of the shoulder-arm-hand complex, therefore, relying only on the local information to recognize the body coordinated motion has many disadvantages because natural continuous arm-hand motions can’t be realized. Also, achieving a dynamical coupling between the user and the prosthesis will not be possible. This study objective was to investigate whether it is possible to associate the around-shoulder muscles’ Electromyogram (EMG) activities with the different hand grips and arm directions movements. Experiments were conducted to record the EMG of different arm and hand motions and the data were analyzed to decide the contribution of each sensor, in order to distinguish the arm-hand motions as a function of the reaching time. Results showed that it is possible to differentiate hand grips and arm position while doing a reaching and grasping task. Also, these results are of great importance as one step to achieve a close loop dynamical coupling between the user and the prosthesis

Topics: Article
Publisher: Bentham Open
OAI identifier:
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles


  1. (2003). A comparison of the oxford and manus intelligent hand prostheses. In:
  2. A prosthetic hand control by non stationary emg at the beginning of motion.
  3. (2007). An MMG-based control method of prosthetic manipulators using acceleration sensors. Robot Soc Jpn
  4. (2006). Are there distinct neural representations of object and limb dynamics.
  5. (2008). Classification of upper arm EMG signals during object-specific grasp.
  6. (1982). Coordination of arm and wrist motion during a reaching task.
  7. (1993). interdependent channels for location and orientation in sensorimotor Transformation for reaching and grasping.
  8. (2009). Multichannel audio aided dynamical perception for prosthetic hand biofeedback. Rehabilitation Robot ICORR’09,
  9. (2004). Multivariate AR modeling of electromyography for the classification of upper arm movements. Clin Neurophysiol
  10. (1999). On-line learning methods for emg prosthetic hand controlling.
  11. (2001). On-line supervising mechanism for learning data in surface electromyogram motion classifiers.
  12. (2004). Patterns of muscle activity underlying object-specific grasp by the macaque monkey.
  13. (1995). Postural and synergic control for three-dimensional movements of reaching and grasping.
  14. (2002). Research on upper extremity prosthesis based on human motion analysis-development of internally powered functional-cosmetics prosthetic hand.
  15. (2001). Shoulder and hand displacements during hitting, reaching, and grasping movements in hemiparetic cerebral palsy. Motor Control
  16. (2009). signal classification for human computer interaction: a review.
  17. (1998). The Hand: How its use shapes the brain, language, and human culture.
  18. (1994). Trajectory formation from surface EMG signals using a neural network model.