Facilitated movement of inertial Brownian motors driven by a load under an asymmetric potential

Based on recent work [L. Machura, M. Kostur, P. Talkner, J. Luczka, and P. Hanggi, Phys. Rev. Lett. 98, 040601 (2007)], we extend the study of inertial Brownian motors to the case of an asymmetric potential. It is found that some transport phenomena appear in the presence of an asymmetric potential. Within tailored parameter regimes, there exists two optimal values of the load at which the mean velocity takes its maximum, which means that a load can facilitate the transport in the two parameter regimes. In addition, the phenomenon of multiple current reversals can be observed when the load is increased.Comment: 7 pages, 3 figure

Helicity Observation of Weak and Strong Fields

We report in this letter our analysis of a large sample of photospheric vector magnetic field measurements. Our sample consists of 17200 vector magnetograms obtained from January 1997 to August 2004 by Huairou Solar Observing Station of the Chinese National Astronomical Observatory. Two physical quantities, $\alpha$ and current helicity, are calculated and their signs and amplitudes are studied in a search for solar cycle variations. Different from other studies of the same type, we calculate these quantities for weak ($100G1000G$) fields separately. For weak fields, we find that the signs of both $\alpha$ and current helicity are consistent with the established hemispheric rule during most years of the solar cycle and their magnitudes show a rough tendency of decreasing with the development of solar cycle. Analysis of strong fields gives an interesting result: Both $\alpha$ and current helicity present a sign opposite to that of weak fields. Implications of these observations on dynamo theory and helicity production are also briefly discussed.Comment: accepted for publication in ApJ Lette

A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.0130.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%11.58%±1.44%

Path Control of a Rehabilitation Robot Using Virtual Tunnel and Adaptive Impedance Controller

Interactive control strategies have been widely used in many rehabilitation robotic devices. The distinctive feature of these strategies is that the patient can be encouraged to actively participant in the therapy program. In this paper, a novel adaptive impedance control method, which allows the patient to actively influence the robot movement trajectory, is presented. The control algorithm developed in this paper is capable of regulating the desired impedance according to the patient's actual deviation from the desired path and the dynamic relationship between patients' motion intention and the reference trajectory. A virtual tunnel surrounding the reference trajectory is designed to ensure the patient's range of motion is always physiologically meaningful. The proposed rehabilitation strategy encourages participants to make contributions to rehabilitation training task as much as possible, which may facilitate provoking motor plasticity and motor recovery. Preliminary experiments with several healthy subjects were conducted to evaluate the feasibility and effectiveness of this strategy. Experimental results demonstrated that subjects could successfully finish the tracking task assisted by robot with the proposed control algorithm

Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model

Purpose – The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach – An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. Findings – Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. Originality/value – Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles

Efficiency optimization in a correlation ratchet with asymmetric unbiased fluctuations

The efficiency of a Brownian particle moving in periodic potential in the presence of asymmetric unbiased fluctuations is investigated. We found that there is a regime where the efficiency can be a peaked function of temperature, which proves that thermal fluctuations facilitate the efficiency of energy transformation, contradicting the earlier findings (H. kamegawa et al. Phys. Rev. Lett. 80 (1998) 5251). It is also found that the mutual interplay between asymmetry of fluctuation and asymmetry of the potential may induce optimized efficiency at finite temperature. The ratchet is not most efficiency when it gives maximum current.Comment: 10 pages, 7 figure

An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model

An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model

Compliance adaptation of an intrinsically soft ankle rehabilitation robot driven by pneumatic muscles

Pneumatic muscles (PMs)-driven robots become more and more popular in medical and rehabilitation field as the actuators are intrinsically complaint and thus are safer for patients than traditional rigid robots. This paper proposes a new compliance adaptation method of a soft ankle rehabilitation robot that is driven by four pneumatic muscles enabling three rotational movement degrees of freedom (DoFs). The stiffness of a PM is dominated by the nominal pressure. It is possible to control the robot joint compliance independently of the robot movement in task space. The controller is designed in joint space to regulate the compliance property of the soft robot by tuning the stiffness of each active link. Experiments in actual environment were conducted to verify the control scheme and results show that the robot compliance can be adjusted when provided changing nominal pressures and the robot assistance output can be regulated, which provides a feasible solution to implement the patient-cooperative training strategy