Tremor is the most prevalent movement disorder, and its incidence is increasing with aging. In spite of the numerous therapeutic solutions available, 65% of those suffering from upper limb tremor report serious difficulties during their daily living. This gives rise to research on different treatment alternatives, amongst which wearable robots that apply selective mechanical loads constitute an appealing approach. In this context, the current work presents a multimodal human-robot interface to drive a neuroprosthesis for tremor management. Our approach relies on the precise characterization of the tremor to modulate a functional electrical stimulation system that compensates for it. The neuroprosthesis is triggered by the detection of the intention to move derived from the analysis of electroencephalographic activity, which provides a natural interface with the user. When a prediction is delivered, surface electromyography serves to detect the actual onset of the tremor in the presence of volitional activity. This information in turn triggers the stimulation, which relies on tremor parameters-amplitude and frequency-derived from a pair of inertial sensors that record the kinematics of the affected joint. Surface electromyography also yields a first characterization of the tremor, together with precise information on the preferred stimulation site. Apart from allowing for an optimized performance of the system, our multimodal approach permits the implementation of redundant methods to both enhance the reliability of the system and adapt to the specific needs of different users. Results with a representative group of patients illustrate the performance of the interface presented here and demonstrate its feasibility.

Tremor constitutes the most common motor disorder, and poses a functional problem to a large number of patients. Despite of the considerable experience in tremor management, current treatment based on drugs or surgery does not attain an effective attenuation in 25 % of patients, motivating the need for research in new therapeutic alternatives. In this context, this paper presents the concept design, development, and preliminary validation of a soft wearable robot for tremor assessment and suppression. The TREMOR neurorobot comprises a Brain Neural Computer Interface that monitors the whole neuromusculoskeletal system, aiming at characterizing both voluntary movement and tremor, and a Functional Electrical Stimulation system that compensates for tremulous movements without impeding the user perform functional tasks. First results demonstrate the performance of the TREMOR neurorobot as a novel means of assessing and attenuating pathological tremors.

Pathological tremor constitutes the most common movement disorder, and is increasing its prevalence with ageing. Treatment forms range from drugs to surgery in those patients refractory to drugs, however, tremor is not effectively managed in about 25% of patients. According to this, new management techniques such as wearable robots that take advantage of selective biomechanical loading seem an interesting alternative. Our objective is to design robotic exoskeletons which suppress tremor, letting the user perform a voluntary movement, by means of intelligent control approaches that include accurate tremor models. In this context, we propose a two-stage algorithm for real-time estimation of time varying tremor amplitude and frequency. It is based on the assumption that tremor alters voluntary motion in an additive manner, and happens in a higher frequency band. The two-stage algorithm first generates an estimation of voluntary movement based on its inherent slower dynamics, and then removes it from the total motion, directly providing an estimate of tremor. This tremor estimation is then fed into an adaptive filter, which provides instantaneous tremor characteristics. Accurate and robust tremor amplitude and frequency estimates are obtained.