BACKGROUND: Foot drop is common gait impairment after stroke. Functional electrical stimulation (FES) of the ankle dorsiflexor muscles during the swing phase of gait can help correcting foot drop. OBJECTIVE: To evaluate efficacy of additional novel F

Transcutaneous activation of muscles with electrical stimulation has limited selectivity in recruiting paralyzed muscles in stroke patients. However, the selectivity could be increased by the application of smaller electrodes and their appropriate positioning on the skin. We developed a method for selecting the appropriate positions of the stimulating electrodes based on electromyography (EMG). The EMG activity maps were estimated from signals recorded with two electrode arrays and two 24-channel wearable amplifiers positioned on the nonparetic and paretic forearms. The areas where the difference between the EMG maps obtained from the nonparetic and paretic arms was significant were identified as the stimulation sites. The stimulation was applied through array electrodes with magnetic holders and two wearable stimulators with four output channels each. The measures of functionality included joint angles measured with goniometers (hand opening) and grasp force measured with a multi-contact dynamometer (grasping). The stimulation protocol comprised co-activation of flexors and extensors to stabilize the wrist joint and prevent pronation/supination.

Functional electrical stimulation (FES) can be applied as an assistive and therapeutic aid in the rehabilitation of foot drop. Transcutaneous multi-pad electrodes can increase the selectivity of stimulation; however, shaping the stimulation electrode becomes increasingly complex with an increasing number of possible stimulation sites. We described and tested a novel decision support system (DSS) to facilitate the process of multi-pad stimulation electrode shaping. The DSS is part of a system for drop foot treatment that comprises a custom-designed multi-pad electrode, an electrical stimulator, and an inertial measurement unit. The system was tested in ten stroke survivors (3–96 months post stroke) with foot drop over 20 daily sessions. The DSS output suggested stimulation pads and parameters based on muscle twitch responses to short stimulus trains. The DSS ranked combinations of pads and current amplitudes based on a novel measurement of the quality of the induced movement and classified them based on the movement direction (dorsiflexion, plantar flexion, eversion and inversion) of the paretic foot. The efficacy of the DSS in providing satisfactory pad-current amplitude choices for shaping the stimulation electrode was evaluated by trained clinicians. The range of paretic foot motion was used as a quality indicator for the chosen patterns. The results suggest that the DSS output was highly effective in creating optimized FES patterns. The position and number of pads included showed pronounced inter-patient and inter-session variability; however, zones for inducing dorsiflexion and plantar flexion within the multi-pad electrode were clearly separated. The range of motion achieved with FES was significantly greater than the corresponding active range of motion (p < 0.05) during the first three weeks of therapy. The proposed DSS in combination with a custom multi-pad electrode design covering the branches of peroneal and tibial nerves proved to be an effective tool for producing both the dorsiflexion and plantar flexion of a paretic foot. The results support the use of multi-pad electrode technology in combination with automatic electrode shaping algorithms for the rehabilitation of foot drop. This study was registered at the Current Controlled Trials website with ClinicalTrials.gov ID NCT02729636 on March 29, 2016.

The purpose of this study was to examine surface motor activation zones for wrist, fingers and thumb extension movements and their temporal change during 20 therapy sessions using advanced multi-pad functional electrical stimulation system. Results from four hemiplegic patients indicate that certain zones have higher probability of eliciting each of the target movements. However, mutual overlap and variations of the zones are present not just between the subjects, but also on the intrasubject level, reflected through these session to session transformations of the selected virtual electrodes. The obtained results could be used as a priori knowledge for semi-automated optimization algorithm and could shorten the time required for calibration of the multi-pad electrode.

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.

Background: Stroke recovery is influenced not only by clinical but also sociodemographic factors (SDFs). However, data on how variables such as age, sex, marital status, education, and employment status affect rehabilitation outcomes remain limited, particularly in structured inpatient settings. This study aimed to analyze the impact of key SDFs on functional recovery after stroke. Methods: A retrospective cohort of 289 stroke patients undergoing structured inpatient rehabilitation was analyzed. Functional status was assessed at admission, after three weeks, and at discharge using five standardized outcomes: gait speed (primary outcome), Barthel Index, Berg Balance Scale, Action Research Arm Test, and Ashworth scale. Repeated measures ANOVA and multivariable logistic regression were used to evaluate within-subject changes and associations with SDFs. Results: The cohort consisted predominantly of middle-aged to older adults (58% female, 62% married, 60% retired, 60% with primary education or less). Most patients (88%) had ischemic strokes of moderate severity. Significant improvements were observed across all functional measures. Employed, married, younger, and male patients achieved better outcomes. Interaction models indicated that older and female patients with moderate stroke severity demonstrated greater improvement than younger and male counterparts with milder strokes. Mean gait speed increased by +0.32 m/s, exceeding the minimal clinically important difference (MCID) of 0.16 m/s. Conclusions: Age, sex, marital status, education, and employment status are relevant predictors of stroke rehabilitation outcomes. These findings emphasize the importance of incorporating sociodemographic profiles into individualized rehabilitation planning to optimize functional recovery and reduce disparities among stroke survivors.