Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals.

BACKGROUND: Although model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces. METHODS: Three-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization. FINDINGS: The electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks. INTERPRETATION: Quantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.

Center of mass-based posturography for free living environment applications.

BACKGROUND: Postural assessment is crucial as risk of falling is a major health problem for the elderly. The most widely used devices are force and balance plates, while center of pressure is the most studied parameter as measure of neuromuscular imbalances of the body sway. In out-of-laboratory conditions, where the use of plates is unattainable, the center of mass can serve as an alternative. This work proposes a center of mass-based posturographic measurement for free living applications. METHODS: Ten healthy and ten Parkinson's disease individuals (age = 26.1 ± 1.5, 70.4 ± 6.2 years, body mass index = 21.7 ± 2.2, 27.6 ± 2.8 kg/m2, respectively) participated in the study. A stereophotogrammetric system and a force plate were used to acquire the center of pressure and the 5th lumbar vertebra displacements during the Romberg test. The center of mass was estimated using anthropometric measures. Posturographic parameters were extracted from center of pressure, center of mass and 5th lumbar vertebra trajectories. Normalized root mean squared difference was used as metric to compare the trajectories; Spearman's correlation coefficient was computed among the posturographic parameters. FINDINGS: Low values of the metric indicated a good agreement between 5th lumbar vertebra trajectory and both center of pressure and center of mass trajectories. Statistically significant correlations were found among the postural variables. INTERPRETATION: A method to perform posturography tracking the movement of the 5th lumbar vertebra as an approximation of center of mass has been presented and validated. The method requires the solely kinematic tracking of one anatomical landmark with no need of plates for free living applications.

Quantitative assessment of training effects using EksoGT® exoskeleton in Parkinson's disease patients: A randomized single blind clinical trial.

Background: Gait alterations are among the most disabling motor-symptoms associated with Parkinson's Disease (PD): reduced stride length, stride velocity and lower limb joint range of motion are hallmarks of parkinsonian gait. Research focusing on optimal functional rehabilitation methods has been directed towards powered lower-limb exoskeletons which combines the advantages delivered from the grounded robotic devices with the ability to train the patient in a real-world environment. As gait involves both central (CNS) and peripheral nervous systems (PNS), targeted rehabilitation must restore not only mechanics but also neurophysiological gait patterns. Methods: Two cohorts of subjects will be enrolled and equally distributed between one group (n = 25) who will undergo a functional kinematic therapy, and one group (n = 25) who will undergo an overground wearable-exoskeleton training. Participants are evaluated at three time points: before the therapy (T0), after the therapy (T1), 4-weeks after T1 (T2). Comprehensive gait analysis and surface electromyography will be combined into neuromusculoskeletal modelling to determine modifications at the PNS level. Functional magnetic resonance imaging coupled with electroencephalography will be used to determine modifications at the CNS level. Conclusion: The findings of the proposed trial will likely give substantial solutions for the management of gait and postural disorders in PD where valid interventions are lacking. The coupling of movement evaluation, which assesses neuromuscular and biomechanical features, with neurological data, will better define the impact of the therapy on the relationship between PD motor alterations and brain activity. This will provide an active treatment that is personalized and shared to large populations.

Different perspectives in understanding muscle functions in Parkinson's disease through surface electromyography: Exploring multiple activation patterns.

Gait disorders are one of the cardinal features of Parkinson's Disease (PD) and might be affected by a modified pattern of motor unit activation. This work explores how PD affects the lower limb muscle control and how muscle activity contributes to gait impairment. Using clinical gait analysis data, the onset and the offset of the surface electromyographic (sEMG) signal of four lower limb muscles were determined in 18 people with PD and compared with 10 heathy controls. Different motor patterns were identified in both the populations through a statistical detector algorithm and described in terms of linear envelope, local maxima activation magnitude and occurrence, co-contractions, and bursts duration. Statistical analysis was performed using statistical parametric mapping for the sEMG envelope and linear mixed effects models for the sEMG parameters. An equivalent number of sEMG patterns was detected in PD with respect to controls. Significant differences were highlighted between the two cohorts within the same activation modality. Plantarflexors muscles activation was delayed on time and had different durations and activations peaks, while Biceps Femoris revealed a higher local maximum. These results suggested that functional tibiotarsus joint reeducation coupled with postural rehabilitation might be beneficial for people with PD.

Muscular activation changes in lower limbs after underwater gait training in Parkinson's disease: A surface emg pilot study.

BACKGROUND: Under water gait training (UT) has been proposed as an innovative rehabilitative strategy for the treatment of axial disorders in Parkinson Disease (PD) patients, in particular for balance and gait impairment. However, the basis for the improvement is unclear. RESEARCH QUESTION: The aim of this study was to evaluate improvements in the muscular activation in the lower limbs in a cohort of PD patients after UT. METHODS: Ten PD participants in the "off" state and 10 controls (mean ± standard deviation of age and BMI were respectively: 71 ± 6 years, 28 ± 3 kg/m2; 65.5 ± 7 years, 28 ± 3 kg/m2) were enrolled in the study. After signing informed consent, they walked barefoot at their preferred speed on a 10 m walkway, before and after UT. The electrical activity of four muscles were collected bilaterally by means of a surface electromyography system (sEMG), two force plates and a motion capture system. All signals were synchronized in time with the gait cycle. The sEMG activity of Rectus Femoris (RF), Tibialis Anterior (TA), Biceps Femoris (BF) and Gastrocnemius Lateralis (GL) were acquired. The average from each signal was used to extract the peak of the Envelope (PoE) and its occurrence with respect to the gait cycle (PoPE%). Time and space parameters were determined. RESULTS: Our results showed that UT in PD patients improved the muscle's recruitment pattern towards normal. The PD patients POPE% was comparable with the one of the controls (TA: 20-35 %, 75-80 % of gait cycle; GL: 0-15 %, 25-45 %, 85-100 % of gait cycle) after UT on each muscle with the exception of BF. The muscle co-activation plots failed to show improvement in line with the muscle activation. SIGNIFICANCE: These results suggest that the muscle activation improvement with UT in PD participants might be due to a reorganisation at the executive rather than at the command level.

Validation of plantar pressure simulations using finite and discrete element modelling in healthy and diabetic subjects.

Plantar pressure simulation driven by integrated 3D motion capture data, using both a finite element and a discrete element model, is compared for ten healthy and ten diabetic neuropathic subjects. The simulated peak pressure deviated on average between 16.7 and 34.2% from the measured peak pressure. The error in the position of the peak pressure was on average smaller than 4.2 cm. No method was more accurate than the other although statistical differences were found between them. Both techniques are thus complementary and useful tools to better understand the alteration of diabetic foot biomechanics during gait.

Closed-loop EMG-informed model-based analysis of human musculoskeletal mechanics on rough terrains.

This work aims at estimating the musculoskeletal forces acting in the human lower extremity during locomotion on rough terrains. We employ computational models of the human neuro-musculoskeletal system that are informed by multi-modal movement data including foot-ground reaction forces, 3D marker trajectories and lower extremity electromyograms (EMG). Data were recorded from one healthy subject locomoting on rough grounds realized using foam rubber blocks of different heights. Blocks arrangement was randomized across all locomotion trials to prevent adaptation to specific ground morphology. Data were used to generate subject-specific models that matched an individual's anthropometry and force-generating capacity. EMGs enabled capturing subject- and ground-specific muscle activation patterns employed for walking on the rough grounds. This allowed integrating realistic activation patterns in the forward dynamic simulations of the musculoskeletal system. The ability to accurately predict the joint mechanical forces necessary to walk on different terrains have implications for our understanding of human movement but also for developing intuitive human machine interfaces for wearable exoskeletons or prosthetic limbs that can seamlessly adapt to different mechanical demands matching biological limb performance.