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
Previous work from our team, has proposed a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes, leading to the creation of a proof-of-concept system integrated in a toilet seat. However, for this approach to be industrially feasible, further optimization is needed, in particular in what concerns electrode materials compatible with injection moulding processes. In this paper we explore the use of different types of conductive materials as electrodes, aiming at industrial-scale production of a toilet seat capable of recording ECG data, without the need for bodyworn devices. In addition, the effect of cleaning agents applied to the materials over time. Our approach has been evaluated comparatively with a gold standard device, for a population of 15 healthy subjects. While some of the materials did not allow adequate signal acquisition in all users, one electrically conductive compound showed the best results as per heart rate and ECG waveform morphology analysis. For the best performing compound we were able to acquire signals in 100% of the sessions, with an average heart rate deviation between the reference and experimental systems of -3.67 +/- 5.05 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.99.

Abstract
This study presents non-invasive subject specific analysis using innovative tools from dynamic systems theory and image processing for sagittal plane anatomical marker tracking and digital filtering for detection of normalized phase differences of lower limb joint angular displacement and angular velocity coordination during long and short countermovement (CM) and muscle stretch-shortening cycle. Applied metrics captured at low-dimensional level (one variable - the phase) differences of CM neuromuscular control of lower limb joint coordination with greater dissimilarity between long and short CM, whereas no CM condition shares higher phase coordination at the hip, knee, ankle.