The continuous, accurate and reliable estimation of gait parameters as a measure of mobility is essential to assess the loss of functional capacity related to the progression of disease. Connected insoles are suitable wearable devices which allow precise, continuous, remote and passive gait assessment. The data of 25 healthy volunteers aged 20 to 77 years were analysed in the study to validate gait parameters (stride length, velocity, stance, swing, step and single support durations and cadence) measured by FeetMe^® insoles against the GAITRite^® mat reference. The mean values and the values of variability were calculated per subject for GAITRite^® and insoles. A t-test and Levene’s test were used to compare the gait parameters for means and variances, respectively, obtained for both devices. Additionally, measures of bias, standard deviation of differences, Pearson’s correlation and intraclass correlation were analysed to explore overall agreement between the two devices. No significant differences in mean and variance between the two devices were detected. Pearson’s correlation coefficients of averaged gait estimates were higher than 0.98 and 0.8, respectively, for unipedal and bipedal gait parameters, supporting a high level of agreement between the two devices. The connected insoles are therefore a device equivalent to GAITRite^® to estimate the mean and variability of gait parameters.

When exploring changes in upper limb kinematics and motor impairment associated with motor recovery in subacute post stroke during intensive therapies involving robot-assisted training, it is not known whether trained joints improve before non-trained joints and whether target reaching capacity improves before movement accuracy. Twenty-two subacute stroke patients (mean delay post-stroke at program onset 63 ± 29 days, M2) underwent 50 ± 17 (mean ± SD) 45-min sessions of robot-assisted (InMotion™) shoulder/elbow training over 3 months, in addition to conventional occupational therapy. Monthly evaluations (M2 to M5) included Fugl-Meyer Assessment (FM), with subscores per joint, and four robot-based kinematic measures: mean target distance covered, mean velocity, direction accuracy (inverse of root mean square error from straight line) and movement smoothness (inverse of mean number of zero-crossings in the velocity profile). We assessed delays to reach statistically significant improvement for each outcome measure. At M5, all clinical and kinematic parameters had markedly improved: Fugl-Meyer, +65% (median); distance covered, +87%; mean velocity, +101%; accuracy, +134%; and smoothness, +96%. Delays to reach statistical significance were M3 for the shoulder/elbow Fugl-Meyer subscore (+43%), M4 for the hand (+80%) and M5 for the wrist (+133%) subscores. For kinematic parameters, delays to significant improvements were M3 for distance (+68%), velocity (+65%) and smoothness (+50%), and M5 for accuracy (+134%). An intensive rehabilitation program combining robot-assisted shoulder/elbow training and conventional occupational therapy was associated with improvement in shoulder and elbow movements first, which suggests focal behavior-related brain plasticity. Findings also suggested that recovery of movement quantity related parameters (range of motion, velocity and smoothness) might precede that of movement quality (accuracy). EudraCT 2016–005121-36 . Date of Registration: 2016–12-20. Date of enrolment of the first participant to the trial: 2009–11-24 (retrospective data).

Backgound: Metrics for movement smoothness include the number of zero-crossings on the acceleration profile (N0C), the log dimensionless jerk (LDLJ), the normalized averaged rectified jerk (NARJ) and the spectral arc length (SPARC). Sensitivity to the handedness and movement type of these four metrics was compared and correlations with other kinematic parameters were explored in healthy subjects. Methods: Thirty-two healthy participants underwent 3D upper limb motion analysis during two sets of pointing movements on each side. They performed forward- and backward-pointing movements at a self-selected speed to a target located ahead at shoulder height and at 90% arm length, with and without a three-second pause between forward and backward movements. Kinematics were collected, and smoothness metrics were computed. Results: LDLJ, NARJ and N0C found backward movements to be smoother, while SPARC found the opposite. Inter- and intra-subject coefficients of variation were lowest for SPARC. LDLJ, NARJ and N0C were correlated with each other and with movement time, unlike SPARC. Conclusion: There are major differences between smoothness metrics measured in the temporal domain (N0C, LDLJ, NARJ), which depend on movement time, and those measured in the frequency domain, the SPARC, which gave results opposite to the other metrics when comparing backward and forward movements.

After discharge from hospital following a stroke, prescriptions of community-based rehabilitation are often downgraded to “maintenance” rehabilitation or discontinued. This classic therapeutic behavior stems from persistent confusion between lesion-induced plasticity, which lasts for the first 6 months essentially, and behavior-induced plasticity, of indefinite duration, through which intense rehabilitation might remain effective. This prospective, randomized, multicenter, single-blind study in subjects with chronic stroke-induced hemiparesis evaluates changes in active function with a Guided Self-rehabilitation Contract vs conventional therapy alone, pursued for a year. One hundred and twenty four adult subjects with chronic hemiparesis (> 1 year since first stroke) will be included in six tertiary rehabilitation centers. For each patient, two treatments will be compared over a 1-year period, preceded and followed by an observational 6-month phase of conventional rehabilitation. In the experimental group, the therapist will implement the diary-based and antagonist-targeting Guided Self-rehabilitation Contract method using two monthly home visits. The method involves: i) prescribing a daily antagonist-targeting self-rehabilitation program, ii) teaching the techniques involved in the program, iii) motivating and guiding the patient over time, by requesting a diary of the work achieved to be brought back by the patient at each visit. In the control group, participants will benefit from conventional therapy only, as per their physician’s prescription. The two co-primary outcome measures are the maximal ambulation speed barefoot over 10 m for the lower limb, and the Modified Frenchay Scale for the upper limb. Secondary outcome measures include total cost of care from the medical insurance point of view, physiological cost index in the 2-min walking test, quality of life (SF 36) and measures of the psychological impact of the two treatment modalities. Participants will be evaluated every 6 months (D1/M6/M12/M18/M24) by a blinded investigator, the experimental period being between M6 and M18. Each patient will be allowed to receive any medications deemed necessary to their attending physician, including botulinum toxin injections. This study will increase the level of knowledge on the effects of Guided Self-rehabilitation Contracts in patients with chronic stroke-induced hemiparesis. ClinicalTrials.gov: NCT02202954 , July 29, 2014.

There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.