Abstract
This study presents lower limb joint load comparison from subject specific musculoskeletal model simulation (MSK-MS) and direct measurements from instrumented implants on post-operative (PO) patients. A case study was considered for MSK-MS gait analysis of a 40-year-old healthy male with 70 kg and 1.86 m height. Reflective adhesive markers were applied on skin surface of selected anatomical points at right and left lower limbs. Orthostatic and dynamic acquisition on normal gait (NG), stiff-knee gait (SKG) and slow running (SR) was performed from ground reaction forces with two force plates at 2 kHz and trajectories of skin markers with eight-camera system at 100 Hz. Subject specific MSK-MS was performed using AnyGait and morphed Twente Lower Extremity Model (TLEM), matching the size and joint morphology of the stick-figure model. Over-determinate kinematic analysiswas performed, and motion equations solved with hard and soft constraints. Representative MSK-MS gait cycles were selected at NG, SKG and SR lower limb joint vertical force components at the hip, the knee, and the ankle normalized to body weight (JFz/BW). Internal joint direct measurements of four PO patients', 61-83 years, average weight 808 N and 1.71 m height, with telemetric Hip I (4-channel), Hip II (8-channel) and knee (9-channel) instrumented implants were selected from Orthoload database with comparable gait to NG, SKG and SR. Statistical measurements presented similar mean JFz/BW at right/left hip, knee, ankle MSK-MS and asymmetric peak values with dominant NG, SKG and SR different variances (p < 0.05). Direct JFz/BW measures contrasted NG with similar hip and knee mean and variance from SKG and SR with different mean and variance. Peak JFz/BW direct measurements presented higher hip and knee values on SR and NG than SKG, with higher values at the knee than the hip on NG and SKG, and the opposite on SR. Direct JFz/BW measurements presented at the hip and the knee lower values than their corresponding MSK-MS on NG, SKG and SR.

Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.

Abstract
BackgroundCognitive impairment is a critical aspect of our aging society. Yet, it receives inadequate intervention due to delayed or missed detection. Dual-task gait analysis is currently considered a solution to improve the early detection of cognitive impairment in clinical settings. Recently, our group proposed a new approach for the gait analysis resorting to inertial sensors placed on the shoes. This pilot study aimed to investigate the potential of this system to capture and differentiate gait performance in the presence of cognitive impairment based on single- and dual-task gait assessments.MethodsWe analyzed demographic and medical data, cognitive tests scores, physical tests scores, and gait metrics acquired from 29 older adults with mobility limitations. Gait metrics were extracted using the newly developed gait analysis approach and recorded in single- and dual-task conditions. Participants were stratified into two groups based on their Montreal Cognitive Assessment (MoCA) global cognitive scores. Statistical analysis was performed to assess differences between groups, discrimination ability, and association of gait metrics with cognitive performance.ResultsThe addition of the cognitive task influenced gait performance of both groups, but the effect was higher in the group with cognitive impairment. Multiple dual-task costs, dual-task variability, and dual-task asymmetry metrics presented significant differences between groups. Also, several of these metrics provided acceptable discrimination ability and had a significant association with MoCA scores. The dual-task effect on gait speed explained the highest percentage of the variance in MoCA scores. None of the single-task gait metrics presented significant differences between groups.ConclusionsOur preliminary results show that the newly developed gait analysis solution based on foot-worn inertial sensors is a pertinent tool to evaluate gait metrics affected by the cognitive status of older adults relying on single- and dual-task gait assessments. Further evaluation with a larger and more diverse group is required to establish system feasibility and reliability in clinical practice.

Abstract
This study presents principal component analysis (PCA) intra-subject variability of lower limb surface electromyography (sEMG) at different muscle stretch-shortening cycle (SSC). Several key steps are presented on the research of muscle force production for human in-vivo and noninvasive studies as well as on SSC contribution at gait, run, and jump with the need for separation of muscle and tendon behavior. Complexity and unpredicted multiple muscle actuation are highlighted with the need for extraction of PCA components from muscle stretch-shortening cycle sEMG, namely on lower limb stereotyped muscle patterns assessed on standard maximum vertical jump (MVJ). The purpose of this study is to apply PCA to sEMG linear envelopes of lower limb selected muscles at different MVJ, to detect lower number of components explaining maximum sEMG variability, representative of low dimensional signal control on muscles synergies. Different MVJ were assessed with subject specific PCA of lower limb sEMG during Counter Movement Jump (CMJ), Drop Jump (DJ), and Squat Jump (SJ). Intra-subject variability of sEMG PCA allowed the detection of two components explaining maximum variability with different profiles and muscle grouping at CMJ, DJ, and SJ. First component (PC1), representing larger signal variability, presented higher value at SJ and DJ than CMJ, with the need for a higher number of PC's to explain the same cumulative percentual variance at CMJ than DJ and SJ. Comparison with intra-subject linear (r) and cross-correlation (CCr) presented higher r and CCr at SJ and DJ than CMJ, with higher paired correlations at the muscles grouped on the same component. Comparison of intra-subject analysis with previous study on same subject single trial allowed subject-specific generalization of the preceding results.

Abstract
This study presents and applies in vivo lower limb frequency response analysis during standard maximum vertical jump (MVJ) with long and short counter movement (CM) and corresponding muscle stretch shortening cycle (SSC) for comparison without CM and SSC condition. The study makes use of algebraic relation at the frequency domain to obtain the response function from the input and output signals. Single-input/single-output (SI/SO) constant parameter linear system (CPLS) was applied with vertical ground reaction force (GRFz) input and center of gravity (CG) vertical displacement (Delta z) output, obtaining lower limb frequency response function during MVJ impulse phase. Piecewise linearity and limited input-output range of experimentally acquired GRFz and CG Delta z during MVJ impulse phase were assessed to confirm assumptions for CPLS application. Piecewise stationarity of the input and output signal was ensured by acquiring those signals on each MVJ type at similar conditions, guaranteeing experimental repetitions under statistical similar conditions on each CM. Different CM condition on each MVJ type were compared as regards to maximum vertical height, time period of the impulse phase, fundamental harmonic frequencies, convergence of the GRFz input and CG Delta z output Fourier series, their autospectral and cross-spectral density, as well as its input-output coherence, cross-spectrum gain factor, and phase of the frequency response function. Several differences were detected among CM condition, potentially contributing to explain differences on achieved performances at each CM and SSC.