Active Pathological Tremor Compensation using Functional Electrical Stimulations

PROJECT DESCRIPTION:

We are proposing an active compensation approach to attenuate pathological tremor of the upper limbs. From the sensed physical motion and the Electromyograph (EMG) signals of the upper limbs, a filtering algorithm would distinguish between the intended motion and involuntary component, and to counteract the tremor in real-time by controlling the flexor and extensor muscles via functional electrical stimulations (FES) to contract out of phase with their tremorogenic activation pattern.

Background

Tremor is defined as the involuntary rhythmic or semi rhythmic oscillation of a body part resulting from alternating of simultaneous contractions of antagonistic muscle groups. Pathological tremor affects more than 5% of the population age 40 and above. Common causes of pathological tremor include essential tremor, Parkinson's disease, multiple sclerosis, stroke, spinal cord injuries, orthostatic tremor, primary writing tremor, etc. Daily lives of these patients are greatly impaired by the involuntary hand motion, and in some severe cases, it becomes impossible for them to perform simple chores like drinking from a cup or inserting a key into a keyhole.

Scientific Goal/Objective

We are proposing an active compensation approach to attenuate pathological tremor of the upper limbs. From the sensed physical motion and the Electromyograph (EMG) signals of the upper limbs, a filtering algorithm would distinguish between the intended motion and involuntary component, and to counteract the tremor in real-time by controlling the flexor and extensor muscles via functional electrical stimulations (FES) to contract out of phase with their tremorogenic activation pattern.

Methodology

1. Modeling

   From an engineering perspective, each type of pathological tremor has its own distinctive structure. For example, Parkinsonian tremor is a resting tremor that lies in the band of 3 - 6 Hz and has high amplitude, essential tremor is characterized by a 4 - 12 Hz postural and kinetic tremor, and multiple sclerosis and stroke tremors are intention tremor – typified by an increase in tremor amplitude near the termination of a visually guided goal-directed movement. We will seek to understand in depth the engineering characteristics of different types of pathological tremor, and to propose mathematical models of these quasi-periodic movements in ways that are suitable for engineering manipulations.

2. Sensing

   There is a 20 ms time delay between the moment an EMG signal is sensed and the actual movement of the muscle. This provides our approach a critical time window to process the signal and actuate the appropriate muscle groups.

   The corresponding EMG signals and the resultant hand movements will be measured and analyzed. We will evaluate the current available models to correlate EMG signals and the resultant physical motion especially in describing the tremulous component. New EMG models will be investigated to integrate seamlessly with our proposed pathological tremor models. Decoding the noisy EMG signal is a challenging task, with an appropriate model we could fuse the sensed physical motion to improve the EMG signal-to-noise ratio.

3. Filtering

   The key technical challenge in tremor filtering is the real-time criterion of the application. Tremor has a distinctly higher frequency band than most voluntary motion. However, most classical frequency selective filters cause phase shift in the filtered signal, which means the filtered erroneous motion that we attempt to cancel is a time delayed version of the actual physical motion. To overcome this causality barrier, we propose to research and design an adaptive zero-phase filter with predictive capability. Learning algorithms will also be explored to extract the tremulous component from the sensed motion.

4. Actuation

   FES has been shown by neuroscientists to be a feasible mean to attenuate pathological tremor. If we could successfully decipher the tremulous erroneous motion component from the sensed EMG and the physical motion, we would be able to actuate the appropriate muscle groups with the correct intensity, sequence and timing to nullify the pathological hand tremor.

GRANT:

• S$20,000, Biomedical and Pharmaceutical Engineering Cluster Seed Grant, NTU.
  January 2006 - January 2007.
• S$443,720, National Medical Research Council (NMRC) Individual Research Grant.
  October 2006 - September 2009.
• Euro 30,000, Merlion Programme, French Embassy
  July 2008 to June 2010

PERSONNEL:

Dr. ANG Wei Tech
Ph.D.

Assistant Professor

wtang@ntu.edu.sg

PUBLICATIONS:

Refereed Journal:

1. F. Widjaja, C. Y. Shee, D. G. Zhang, W. T. Ang, P. Poignet, A. P. L. Bó, and D. Guiraud, “Current progress on pathological tremor modeling and active compensation using functional electrical stimulation,” Gerontechnology, vol. 7, no. 2, p. 240, May 2008.

Refereed Journal (under review/revision):

1. D. G. Zhang, F. Windjaja, C. Y. Shee, P. Poignet, and W. T. Ang, “Neural oscillator based control for pathological tremor attenuation,” Biological Cybernetics, (revision).

Refereed Conference:

1. F. Widjaja, C. Y. Shee, P. Poignet, and W. T. Ang, “FES artifact suppression for real-time tremor compensation,” IEEE 11th Intl. Conf. Rehabilitation Robotics, Kyoto, Japan, June 2009, (in press).
2. F. Widjaja, C. Y. Shee, W. L. Au, P. Poignet, and W. T. Ang, “An extended Kalman filtering of accelerometer and electromyography data for attenuation of pathological tremor,” in Proc. 2nd IEEE RAS & EMBS Intl. Conf. Biomedical Robotics and Biomechatronics, Scottsdale, AZ, USA, Oct. 2008, pp. 193–198.
3. D. G. Zhang, P. Poignet, and W. T. Ang, “Neural oscillator based control for wrist tremor attenuation via FES,” in 13th Annu. Intl. FES Society Conf., Freiburg, Germany, Sep. 2008.
4. A. P. L. Bó, P. Poignet, F. Widjaja, and W. T. Ang, “Online pathological tremor characterization using extended Kalman filtering,” in Proc. 30th Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, Aug. 2008, pp. 1753–1756.
5. D. G. Zhang, W. T. Ang, and P. Poignet, “A neuromusculoskeletal model exploring peripheral mechanism of tremor,” in Proc. 30th Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, Aug. 2008, pp. 3716–3719.
6. F. Widjaja, C. Y. Shee, W. T. Latt, W. L. Au, P. Poignet, and W. T. Ang, “Kalman filtering of accelerometer and electromyography (EMG) data in pathological tremor sensing system,” in Proc. IEEE Intl. Conf. Robotics and Automation, Pasadena, CA, USA, May 2008, pp. 3250–3255.
7. M. Rank, O. Stursberg, and W. T. Ang, “A rate-dependent linear modeling approach for pathological tremor applications,” in Proc. 6th IASTED Intl. Conf. Biomedical Engineering, Innsbruck, Austria, Feb. 2008.
8. F. Widjaja, C. Y. Shee, W. L. Au, P. Poignet, and W. T. Ang, “Towards a sensing system for quantification of pathological tremor,” in Intl. Conf. Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, Nov. 2007, pp. 986–991.
9. D. Zhang, W. T. Ang, F. Windjaja, and S. C. Yap, “Neural oscillator based control for wrist tremor attenuation,” in Proc. 5th IEEE Intl. Conf. Computational Cybernetics, Gammarth, Tunisia, 2007, pp. 197–202.
10. D. G. Zhang and W. T. Ang, “Reciprocal EMG controlled FES for pathological tremor suppression of forearm,” in Proc. 29th Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, Lyon, France, Aug. 2007, pp. 4810–4813.
11. D. G. Zhang, H. G. Tan, W. Ferdinan, and W. T. Ang, “Functional electrical stimulation in rehabilitation engineering: A survey,” in 1st Intl. Convention for Rehabilitation Engineering & Assistive Technology, Singapore, Apr. 2007.
12. D. G. Zhang and W. T. Ang, “Tremor suppression of elbow joint via functional electrical stimulation: A simulation study,” in IEEE Intl. Conf. on Automation Science and Engineering, Shanghai, China, Oct. 2006, pp. 182–187.

Refereed Conference (submitted/review):

1. F. Widjaja, C. Y. Shee, P. Poignet and W. T. Ang, “Filtering of Intended Motion in Real-time Tremor Compensation for Human Upper Limb Using Surface Electromyography,” in Proc. 31th Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, USA, Sep. 2009, (submitted).