Given the side effects and possible complications of current clinical treatments for severe pathological tremor, many researchers pursue the less invasive alternative of suppression at the musculo-skeletal level. In wearable robotics applications, the high strength-to-weight ratio, the low power consumption, and their adaptability allow magnetorheological (MR) fluid dampers and actuators to be personalized to the individual needs of a patient. Moreover, their rapid dynamic response makes them suitable for use in real time active devices. This paper presents the theoretical development and experimental validation of a tremor suppression control algorithm based on a dynamic lump parameter model of the MR damper and a real time adaptive tremor frequency estimator. The control strategy was experimentally evaluated using a one-DOF joint simulator. Experiments with ten datasets from patients with severe Parkinsonian and Essential Tremor show suppressions for the first and second harmonics of 28.7±2.2 dB and 11.8±4.8 dB, respectively. These results compare favorably with the suppression levels obtained by other researchers using constant current approach. In addition, the resistance that the orthotic device imposes to the voluntary motion was reduced by 33% under the active control strategy, while the power usage was dramatically reduced by nearly an order of magnitude.

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