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 neuromusculo-skeletal system, aiming at characteri-zing 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.

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In recent years, gait robots have become increasingly common for gait rehabilitation in non-ambulatory stroke patients. Cardiovascular treadmill training, which has been shown to provide great benefit to stroke survivors, cannot be performed with non-ambulatory patients. We there-fore integrated cardiovascular training in robot-assisted gait therapy to combine the benefits of both training modi. We developed a model of human heart rate as a function of exercise parameters during robot-assisted gait training and applied it for automatic control purposes. This structural model of the physio-logical processes describes the change in heart rate caused by treadmill speed and the power exchanged bet-ween robot and subject. We performed physiological parameter estimation for each tested individual and de-signed a model-based feedback controller to guide heart rate to a desired time profile. Five healthy sub-jects and eight stroke patients were recorded for model parameter identification, which was successfully used for heart rate control of three healthy subjects.We showed that a model-based control approach can take into account patient-specific limitations of treadmill speed as well as individual power expenditure.

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