Lower-limb prostheses have an important function to partially recover the leg movement after amputation. In order to improve the mechanical joint behavior towards a healthy human knee, compliant elements have been introduced to the active prostheses, comprised of the well-known Series Elastic Actuators (SEAs). SEAs are used in lower-limb assistive devices due to their ability to tolerate impacts and passive store mechanical energy during ground-walking. Based on the healthy human knee in the stance phase of walking, this paper brings the design, prototyping, and analysis of a customized planar torsional spring. To enhance the compliance of a rigid active knee prosthesis, the proposed spring will substitute a torque flange between the transmission and the output of the actuator, and this carries a series of constraints to the design. The finite element method (FEM) is applied to the development and exploration of the three initially proposed geometries and the material selection along with its heat treatment is based on the maximum stress obtained in the simulations. The proposed geometry, chosen by comparison of the three, is made of austempered AISI 4340 steel and using two springs in parallel and it has a torsional stiffness of 250 N.m/rad with maximum angular displacement of ± 2.5° and 0.153 kg. In future work, we intend to compare the results of the rigid actuator against the SEA one during walking over the ground.

Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain–computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high—85 rpm and low—30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly ($p<$ 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.

Stroke is a leading cause of disability, often resulting in motor, cognitive, and language deficits, with significant impact on upper-limb function. Robotic therapy (RT) has emerged as an effective strategy, providing intensive, repetitive, and adaptable practice to optimize functional recovery. This pilot study aimed to describe and evaluate the effects of robotic rehabilitation as a complement to conventional therapy, using a biomimetic activities-of-daily-living (ADL)-based protocol, on upper-limb function in post-stroke patients. Three participants (aged 30–80 years) undergoing occupational and/or physiotherapy received individualized robotic training with a lightweight cable-driven upper-limb exoskeleton, m-FLEX™, twice a week for ten weeks (30 min per session). Movements were designed to mimic natural upper-limb actions, including elbow flexion-extension, forearm pronation-supination, tripod pinch, and functional tasks such as grasping a cup. Assessments included the Fugl-Meyer (FM) scale, the Functional Independence Measure (FIM), and device satisfaction, performed at baseline, mid-intervention, and post-intervention. Descriptive analysis of the tabulated data revealed improvements in range of motion and functional outcomes. These findings suggest that biomimetic protocol of robotic rehabilitation, when combined with conventional therapy, can enhance motor and functional recovery in post-stroke patients.