Tremor suppression through impedance control.
ABSTRACT This paper presents a method for designing tremor suppression systems that achieve a specified reduction in pathological tremor power through controlling the impedance of the human-machine interface. Position, rate, and acceleration feedback are examined and two techniques for the selection of feedback coefficients are discussed. Both techniques seek a desired closed-loop human-machine frequency response and require the development of open-loop human-machine models through system identification. The design techniques were used to develop a tremor suppression system that was subsequently evaluated using human subjects. It is concluded that nonadaptive tremor suppression systems that utilize impedance control to achieve a specified reduction in tremor power can be successfully designed when accurate open-loop human-machine models are available.
- SourceAvailable from: Norihiro Koizumi[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we propose a method for identifying systems incorporating a mechanical oscillation part for a non-invasive ultrasound theragnostic system(NIUTS). The NIUTS tracks and follows movement in an area requiring treatment (renal stones, in this study) by irradiating the area with high intensity focused ultrasound (HIFU). Blur noise caused by oscillation of the mechanical system adversely affects the servo performance. To solve this problem and enhance the servo performance, it is first necessary to identify those parts of the NIUTS system that incorporate a mechanical oscillation part. Secondly, we implemented a mechanical oscillation suppression filter based on the abovementioned method for identifying the mechanical oscillation part.International Journal of Automation Technology (IJAT). 01/2014; 8(1):110-119.
- [Show abstract] [Hide abstract]
ABSTRACT: Tremor is a rhythmical and involuntary oscillatory movement of a body part. In addition to social embarrassment, tremor can be debilitating for daily activities. Recently, wearable active exoskeletons emerged as a noninvasive tremor suppression alternative to medication or surgery. The challenge in musculoskeletal tremor suppression is identifying and attenuating the tremor motion without adding resistance to the patient’s intentional motion. In this research, an adaptive tremor suppression algorithm was designed to estimate the tremor fundamental frequency and calculate the proper suppressive force to be applied by the orthosis to the patient’s arm. Stability of the closed-loop system and robustness against the parametric uncertainties were analyzed. An experimental setup was designed and developed to emulate the dynamics of a human wrist with intentional and tremor motion. A pneumatic cylinder and a sliding mode integral controller was used to apply orthotic suppressive force. The algorithm was implemented with an NI cRIO real-time controller and tested using clinical data from ten patients with severe pathological tremor. Experimental results showed tracking of the tremor frequency with less than 3-s response time, and an average 34.5 dB (98.1%) and 11.8 dB (74.3%) reduction of tremor amplitude at the fundamental and second-harmonic frequencies, respectively. The average resistance force to the intentional motion was 0.7 N and the average position error was 2.08% (0.18 dB). The results were compared with passive tremor suppression using a tunable magnetorheological damper.IEEE/ASME Transactions on Mechatronics 04/2014; · 3.14 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: In this article, a tremor attenuation algorithm is designed based on the backstepping method. A high-pass filter was designed in frequency domain and its state space realization was derived to be used as an estimator for the tremorous motion. Using the pole placement method an appropriate state feedback control law was designed to stabilize its output. The dynamics of the human arm was considered and a backstepping controller was derived for the applied torque to the arm. The global asymptotic stability of the resulting closed-loop system was proved using Lyapunov method. The controller is robust against parametric uncertainty and unmodeled nonlinear terms of the arm dynamical model. Simulation results shows the successful suppression of tremor in the presence of parametric uncertainty.ASME 2011 International Mechanical Engineering Congress and Exposition; 11/2011
Tremor Suppression Through Impedance Control
Stephen Pledgie1, Kenneth Barner2, Sunil Agrawal3
University of Delaware
Newark, Delaware 19716
duPont Hospital for Children
Wilmington, Delaware 19899
1 Ph.D. Candidate, Biomechanics and Movement Science Program
2 Department of Computer and Electrical Engineering
3 Department of Mechanical Engineering
4 Extended Manipulation Laboratory
This paper presents a method for designing tremor suppression systems that achieve a
specified reduction in pathological tremor power through controlling the impedance of the hu-
man-machine interface. Position, rate, and acceleration feedback are examined and two tech-
niques for the selection of feedback coefficients are discussed. Both techniques seek a desired
closed-loop human-machine frequency response and require the development of open-loop hu-
man-machine models through system identification.
The design techniques were used to develop a tremor suppression system that was subse-
quently evaluated using human subjects. It is concluded that non-adaptive tremor suppression
systems that utilize impedance control to achieve a specified reduction in tremor power can be
successfully designed when accurate open-loop human-machine models are available.
Tremor is an involuntary, rhythmic, oscillatory movement of the body . Tremor
movements are typically categorized as being either physiological or pathological in origin.
Physiological tremor pervades all human movements, both voluntary and involuntary, and is
generally considered to exist as a consequence of the structure, function, and physical properties
of the neuromuscular and skeletal systems . Its frequency varies with time and lies between 8
and 12 Hz. Pathological tremor arises in cases of injury and disease and is typically of greater
amplitude and lower frequency than physiological tremor. In its mildest form, pathological
tremor impedes the activities of daily living and hinders social function. In more severe cases,
tremor occurs with sufficient amplitude to obscure all underlying voluntary activity [3, 4].
A number of digital filtering algorithms have been developed for the purpose of remov-
ing unwanted noise from signals of interest and have thus found application in tremor sup-
pression. Riviere and Thakor have investigated the application of adaptive notch filtering for the
purpose of suppressing pathological tremor noise during computer pen input [5, 6]. When a ref-
erence of the noise signal is available, adaptive finite impulse response (FIR) filters can produce
a closed-loop frequency response very similar to that of an adaptive notch filter . Gonzalez et
al. developed a digital filtering algorithm that utilized an optimal equalizer to equilibrate a
tremor contaminated input signal and a target signal that the subject attempted to follow on a
computer screen . Inherent human tracking characteristics, such as a relatively constant tem-
poral delay and over and undershoots at target trajectory extrema, were incorporated in a
“pulled-optimization” process designed to minimize a measure of performance similar to the
squared error of the tracking signal.
To improve an individual’s ability to perform manual tasks in a physical environment, it
is necessary to suppress tremor-related movements. This can be accomplished by applying re-
sistive forces to the user’s limb to attenuate movements that occur at or near tremor frequencies.
The mechanical impedance of the human-machine interface is altered due to the activity of a set
of actuators driven by a displacement feedback controller.
Several projects have investigated the application of viscous (velocity dependent) resis-
tive forces to the hand and wrist of tremor subjects for the purpose of suppressing tremor move-
ments [4, 7, 9, 10, 11]. Experimentation with varying levels of velocity dependent force feed-
back showed, qualitatively, that tremor movements could be increasingly suppressed with
increasing levels of viscous force feedback, but that concurrent resistance of voluntary move-
ment may occur.
Closed loop functional electrical stimulation (FES) has been shown to be effective in
suppressing tremor movements in patients with essential tremor, parkinsonian tremor, and cere-
bellar tremor [12, 13]. In this approach, tremorogenic muscles are stimulated out-of-phase to
cancel the tremor forces generated by affected muscles. Investigators were successful in deter-
mining closed loop configurations that attenuate 2 - 5 Hz tremor movements with minimal at-
tenuation applied to voluntary movements in the 0 – 1 Hz range.
Previous investigations into non-adaptive feedback tremor suppression systems have not
utilized quantitative performance criteria during the design of the feedback control system. They
addressed the question of whether or not velocity dependent resistive forces (damping) could ef-
fectively suppress tremor movements, but were not concerned with achieving a specified statisti-
cal reduction in the tremor.
The objective of this research was the development of a methodology that incorporates
quantitative performance criteria as well as position, rate, and acceleration feedback into the de-
sign of a non-adaptive tremor suppression system. The remainder of this paper is divided into
six sections. Section 2 presents the results of an analysis of pathological tremor movements.
The design process for the tremor suppression system is described in Section 3. Next, a method
of system identification for the human-machine system is discussed. Sections 5 and 6 present the
methods and results of an evaluation of the system. Finally, the paper is completed with a brief
discussion and concluding remarks.
2. Analysis of Tremor Movements
An investigation into the spatio-temporal characteristics of tremor movements was per-
formed to gain insight into the spatial distribution and time-frequency properties of pathological
tremor movements. Previous investigations into tremor frequency have typically applied the
Fast Fourier Transform (FFT) algorithm to a sampled data sequence to obtain information re-
garding the exact frequency content of the data. However, no information with respect to the
evolution of the frequency content over time is generated with the FFT. It is for this reason that
a time-frequency analysis of pathological tremor movements was undertaken. The spatial distri-
bution of tremor movements was also examined. A tremor suppression system could potentially
take advantage of unique temporal and spatial distributions in the tremor.
A broad set of experiments was developed to examine the pertinent tremor characteris-
tics. Five tremor subjects ages 18 to 91 participated in the study.
The tremor subjects were qualitatively categorized with respect to the severity of their
tremor. Two subjects possessed the ability to write in a somewhat legible manner and received a
low severity label. Relatively large tremor amplitude that prevented legible writing was ob-
served in two of the subjects. The remaining tremor subject exhibited high variability in tremor
amplitude and, as such, received a variable severity label. The origin of the tremor in subjects B,
D, and E was unknown because no medical diagnosis was available.
The subjects performed target-tracking tasks while seated in front of a 17” computer dis-
play. The position of an on-screen cursor was controlled by manipulating a stylus attached to the
end-effector of the PHANToM, a small robotic arm used in haptic interfaces. The PHANToM is
an excellent platform for data collection because it has less than 0.1 N of static backdrive fric-
tion, an end-effector inertia of at most 100 grams with the motors disabled, a workspace of 13
cm x 18 cm x 25 cm, and a nominal end-effector position resolution of 0.03 mm.
Table 1. Subject Information.
Figure 1. Experimental Setup.