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CC-BY Dit artikel is overgenomen van https://www.frontiersin.org/journals/neurorobotics There is a growing international interest in developing soft wearable robotic devices to improve mobility and daily life autonomy as well as for rehabilitation purposes. Usability, comfort and acceptance of such devices will affect their uptakes in mainstream daily life. The XoSoft EU project developed a modular soft lower-limb exoskeleton to assist people with low mobility impairments. This paper presents the bio-inspired design of a soft, modular exoskeleton for lower limb assistance based on pneumatic quasi-passive actuation. The design of a modular reconfigurable prototype and its performance are presented. This actuation centers on an active mechanical element to modulate the assistance generated by a traditional passive component, in this case an elastic belt. This study assesses the feasibility of this type of assistive device by evaluating the energetic outcomes on a healthy subject during a walking task. Human-exoskeleton interaction in relation to task-based biological power assistance and kinematics variations of the gait are evaluated. The resultant assistance, in terms of overall power ratio (Λ) between the exoskeleton and the assisted joint, was 26.6% for hip actuation, 9.3% for the knee and 12.6% for the ankle. The released maximum power supplied on each articulation, was 113.6% for the hip, 93.2% for the knee, and 150.8% for the ankle.

1. INTRODUCTION Robotic gait training is a promising tool to improve walking ability after stroke, however, therapeutic effect might largely depend on the type of robotic gait trainer and control algorithm that is used [1]. Therapy should be task-specific and promote active participation as this is crucial for motor learning. Some algorithms have been proposed to encourage patients to actively participate [2], however, the amount of assistance for different subtasks of the gait cycle is chosen by the therapist, and therefore, therapy could be affected by subjective decisions. This contribution presents a novel controller that is able to automatically adapt the assistance for specific subtasks of gait based on patients’ performance. This approach is expected to improve robotic gait therapy. 2. MATERIALS AND METHODS 2.1 Robotic platform LOPES II was used to develop and test the algorithm. LOPES II is a treadmill-based, admittance controlled robotic gait trainer that has eight actuated degrees of freedom to control the motion of the lower limbs and pelvis [2]. Currently, LOPES II is 'manually' controlled through a graphical user interface in which the operator can adjust the level of support for several gait subtasks (i.e. step length, step height, stance, prepositioning and weight shift). 2.2 Adaptive controller A new adaptive controller that is able to automatically change the assistance provided to the patient has been developed. The controller evaluates patients’ performance with respect to reference values in a defined number of preceding steps. This evaluation is done for each subtask of walking separately. Based on this, the assistance is automatically adjusted for each subtask of gait and might be affected in different ways: first, if performance is within a specific range around a previously selected threshold, amount of assistance in the particular subtask will remain constant; and second, assistance will be increased or decreased depending on whether the performance is below or above the specified range respectively. 3. RESULTS AND DISCUSSION A preliminary evaluation of the algorithm in healthy users showed the potential of the controller to adjust the assistance for different subtasks of gait depending on the user's needs. (e.g. simulated stiff knee gait resulted in increased assistance in step height or knee flexion; meanwhile simulated crouch gait was corrected providing assistance in knee and hip extension). In coming studies, authors plan to validate the algorithm in stroke survivors in two ways: First, to evaluate its benefits compared to the current 'manual' assistance where the operator selects the amount of support; and second, to study the effect of different amounts of body weight support and walking speed on the amount of assistance provided by the algorithm. In the future, an optimized version of this algorithm might not only improve robotic gait training for stroke survivors, but could also be used for assessment of patients’ abilities and to monitor progress during rehabilitation.

Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson's disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson's disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.