In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real-life, as opposed to laboratory settings. Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge2 and Garmin Vivo Smart HR+) estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (TST and WASO) against gold-standard technologies in a cohort of healthy older adults in free-living environment. Methods: Overall, 20 subjects (> 65 years) took part in the study. The devices were worn by the subjects for 24 hours, and the results were compared against validated technology (ActiGraph and New Lifestyles NL-2000i). Mean error, MAPE, ICC and Bland-Altman plots were computed for all the parameters considered. Results: Regarding step-counting, all the trackers are highly correlated with each other (ICCs > 0.89). While Fitbit tends to over-count steps (MPE: 12.36%), Garmin and ActiGraph under-count with a MPE of 9.36% and 11.53%. Garmin had poor ICC values when considering energy expenditure compared against the criterion. Fitbit has moderate-to-good ICCs when compared against the other activity trackers, and showed the best results (MAPE: 12.25%) even if underestimated the amount of calories burnt. For MVPA levels estimation, the wristband trackers were highly correlated (ICC = 0.96), however, were moderately correlated against the criterion and over-estimated the minutes of MVPA activity. When analyzing the sleep parameters, the ICCs were poor for all cases, except when comparing Fitbit with the criterion, which showed a moderate agreement. TST is slightly over-estimated with the Fitbit, although providing good results with an average MAPE equal to 10.13%. On the other hand, WASO estimation was poorer and was over-estimated by Fitbit but under-estimated by Garmin. Again, Fitbit was the most accurate, with an average MAPE of 49.7%. Conclusions: Results suggest that the tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, while clinicians should be cautious in considering other parameters for clinical and research purposes.

Over the last years, the introduction of robotic technologies into Parkinson’s disease rehabilitation settings has progressed from concept to reality. However, the benefit of robotic training remains elusive. This pilot randomized controlled observer trial is aimed at investigating the feasibility, the effectiveness and the efficacy of new end-effector robot training in people with mild Parkinson’s disease. Design. Pilot randomized controlled trial. Setting. Robot assisted gait training (EG) compared to treadmill training (CG). Participants. Twenty cognitively intact participants with mild Parkinson’s disease and gait disturbance. Interventions. The EG underwent a rehabilitation programme of robot assisted walking for 40 minutes, 5 times a week for 4 weeks. The CG received a treadmill training programme for 40 minutes, 5 times a week for 4 weeks. Main outcome measures. The outcome measure of efficacy was recorded by gait analysis laboratory. The assessments were performed at the beginning (T0) and at the end of the treatment (T1). The main outcome was the change in velocity. The feasibility of the intervention was assessed by recording exercise adherence and acceptability by specific test. Robot training was feasible, acceptable, safe, and the participants completed 100% of the prescribed training sessions. A statistically significant improvement in gait index was found in favour of the EG (T0 versus T1). In particular, the statistical analysis of primary outcome (gait speed) using the Friedman test showed statistically significant improvements for the EG (p = 0,0195). The statistical analysis performed by Friedman test of Step length left (p = 0,0195) and right (p = 0,0195) and Stride length left (p = 0,0078) and right (p = 0,0195) showed a significant statistical gain. No statistically significant improvements on the CG were found. Robot training is a feasible and safe form of rehabilitative exercise for cognitively intact people with mild PD. This original approach can contribute to increase a short time lower limb motor recovery in idiopathic PD patients. The focus on the gait recovery is a further characteristic that makes this research relevant to clinical practice. On the whole, the simplicity of treatment, the lack of side effects, and the positive results from patients support the recommendation to extend the use of this treatment. Further investigation regarding the long-time effectiveness of robot training is warranted. ClinicalTrials.gov NCT01668407

The soft tissue artifact (STA) is a phenomenon occurring when the motion of bones or anatomical segments is measured by means of skin markers: the biological tissues between the markers and the bone produce a relative motion bone-markers that leads to inaccuracies in the estimation of rigid body poses or kinematics. The aim of this study was to quantify the STA by exploiting a recently published gait analysis dataset. The dataset was composed of six adult subjects with a total knee arthroplasty who underwent gait analysis trials. The motion of the knee was concurrently recorded by means of (i) fluoroscopy imaging and (ii) an optoelectronic system and redundant markers attached to the thigh and shank. The STA was studied by comparing the results calculated on the marker sets with the results obtained from the fluoroscopy data. The stance and swing phases were considered separately. Rigid STA motion and local STA deformation were studied separately. In addition to previous studies, the instantaneous helical axis (IHA) of the knee was calculated and the effect of the STA on its calculation was assessed.

Objective: To assess quantitatively the effects of Neuromuscular Taping (NMT) on the upper limb in a female child with left hemiplegia, due to Cerebral Palsy (CP). Methods: The patient underwent NMT on cervical level, shoulder and hand only of the plegic upper limb, followed by physical therapy. Kinematic data of upper limbs during reaching task were collected before (PRE) and after 2 weeks of treatment (POST). Results: After the intervention, the affected limb improved in terms of movement duration, Average Jerk and Number of Unit Movements indices, indicating a faster, smoother and less segmented movement. Improvements appeared at the ranges of motion of the upper limb joints, both at shoulder and elbow joints. No significant changes were globally displayed for the unaffected arm. Conclusion: NMT seems to be a promising intervention for improving upper limb movement in patients with CP. Further investigations are certainly needed to assess effectively the effects of the intervention in this pathological state.