Personalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction.
Biofeedback assisted rehabilitation and intervention technologies have
the potential to modify clinically relevant biomechanics. Gait retraining has
been used to reduce the knee adduction moment, a surrogate of medial
tibiofemoral joint loading often used in knee osteoarthritis research. In this
study we present an electromyogram-driven neuromusculoskeletal model of the
lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model
included 34 musculotendon units spanning the hip, knee, and ankle joints.
Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics
were solved in real-time from motion capture and force plate data to estimate
the knee medial tibiofemoral contact force (MTFF). We analyzed 5 healthy
subjects while they were walking on an instrumented treadmill with visual
biofeedback of their MTFF. Each subject was asked to modify their gait in order
to vary the magnitude of their MTFF. All subjects were able to increase their
MTFF, whereas only 3 subjects could decrease it, and only after receiving verbal
suggestions about possible gait modification strategies. Results indicate the
important role of knee muscle activation patterns in modulating the MTFF. While
this study focused on the knee, the technology can be extended to examine the
musculoskeletal tissue loads at different sites of the human body.
During the last decade markerless motion capture techniques have gained an increasing interest in the biomechanics community. In the clinical field, however, the application of markerless techniques is still debated. This is mainly due to a limited number of papers dedicated to the comparison with the state of the art of marker based motion capture, in term of repeatability of the three dimensional joints' kinematics. In the present work the application of markerless technique to data acquired with a marker-based system was investigated. All videos and external data were recorded with the same motion capture system and included the possibility to use markerless and marker-based methods simultaneously. Three dimensional markerless joint kinematics was estimated and compared with the one determined with traditional marker based systems, through the evaluation of root mean square distance between joint rotations. In order to compare the performance of markerless and marker-based systems in terms of clinically relevant joint angles estimation, the same anatomical frames of reference were defined for both systems. Differences in calibration and synchronization of the cameras were excluded by applying the same wand calibration and lens distortion correction to both techniques. Best results were achieved for knee flexion-extension angle, with an average root mean square distance of 11.75 deg, corresponding to 18.35% of the range of motion. Sagittal plane kinematics was estimated better than on the other planes also for hip and ankle (root mean square distance of 17.62 deg e.g. 44.66%, and 7.17 deg e.g. 33.12%), meanwhile estimates for hip joint were the most incorrect. This technique enables users of markerless technology to compare differences with marker-based in order to define the degree of applicability of markerless technique.
The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phase-dependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMG-based exoskeleton for rehabilitation purposes
Kinematic analysis of swimming is of interest to improve swimming performances. Although the video recordings of underwater swimmers are commonly used, the available methodologies are rarely precise enough to adequately estimate the three dimensional (3D) joint kinematics. This is mainly due to difficulties in obtaining the required kinematic parameters (anatomical landmarks, joint centres and reference frames) in the swimming environment. In this paper we propose a procedure to investigate the right upper limb's 3D kinematics during front crawl swimming in terms of all elbow and shoulder degrees of freedom (three rotations of the shoulder, two of the elbow). The method is based upon the calibrated anatomical systems technique (CAST), a technique widely used in clinics, which allows estimation of anatomical landmarks of interest even when they are not directly visible. An automatic tracking technique was adopted. The intra-operator repeatability of the manual tracking was also assessed. The root mean squared difference of three anatomical landmarks, processed five times, is always lower than 8 mm. The mean of the root mean squared difference between trajectories obtained with the different methodologies was found to be lower than 20 mm. Results showed that complete 3D kinematics of at least twice as many frames than without CAST can be reconstructed faster and more precisely.
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