Article

Tremor suppression through impedance control

Department of Electrical and Computer Engineering, University of Delaware, Ньюарк, Delaware, United States
IEEE Transactions on Rehabilitation Engineering 04/2000; 8(1):53-9. DOI: 10.1109/86.830949
Source: PubMed

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.

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    • "The fact that the patient's desired motion is unknown makes the control problem different, and more difficult, than the traditional stabilization or tracking control. Pledgie et al. employed impedance control and a PHAN- ToM manipulator to change the frequency response of the human–machine system such that the closed-loop system has a higher impedance in the high-frequency region [16]. The performance of the suppression algorithm relies highly on an accurate human–machine dynamic model and requires position, velocity, and acceleration feedback. "
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    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; 20(2). DOI:10.1109/TMECH.2014.2317948 · 3.65 Impact Factor
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    • "Yano [11] and Riviere [12] apply notch filter and eliminate the frequency band of the tremors from the command value so as to move the systems smoothly. Pledgie applied impedance control to suppress tremors [13]. Gonzalez applied equalizer as a tremor suppressing filter [14]. "
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    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.
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    • "Several methods for musculoskeletal tremor attenuation have been explored in the recent years. Impedance control was employed to change the frequency response of the human–machine system such that the closed-loop system has a higher impedance in the high frequency region [14], [15]. The performance of the suppression algorithm relies highly on an accurate human–machine dynamic model and requires position, velocity, and acceleration feedback. "
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    ABSTRACT: Tremor is a rhythmical and involuntary oscillatory movement of a body part and it is one of the most common movement disorders. Orthotic devices have been under investigation as a noninvasive tremor suppression alternative to medication or surgery. The challenge in musculoskeletal tremor suppression is estimating and attenuating the tremor motion without impeding the patient's intentional motion. In this research a robust tremor suppression algorithm was derived for patients with pathological tremor in the upper limbs. First the motion in the tremor frequency range is estimated using a high-pass filter. Then, by applying the backstepping method the appropriate amount of torque is calculated to drive the output of the estimator toward zero. This is equivalent to an estimation of the tremor torque. It is shown that the arm/orthotic device control system is stable and the algorithm is robust despite inherent uncertainties in the open-loop human arm joint model. A human arm joint simulator, capable of emulating tremorous motion of a human arm joint was used to evaluate the proposed suppression algorithm experimentally for two types of tremor, Parkinson and essential. Experimental results show 30–42 dB (97.5–99.2%) suppression of tremor with minimal effect on the intentional motion.
    IEEE Transactions on Neural Systems and Rehabilitation Engineering 12/2013; DOI:10.1109/TNSRE.2013.2295034 · 2.82 Impact Factor
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