Elena Ceseracciu

CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks

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.

Motion analysis of front crawl swimming applying CAST technique by means of automatic tracking

Abstract 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.

SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patients 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.

Symbiotic Wearable Robotic Exoskeletons: The Concept of the BioMot Project

Wearable robots (WR) are person-oriented devices, usually in the form of exoskeletons. These devices are worn by human operators to enhance or support a daily function, such as walking. Most advanced WRs for human locomotion still fail to provide the real-time adaptability and flexibility presented by humans when confronted with natural perturbations, due to voluntary control or environmental constraints. Current WRs are extra body structures inducing fixed motion patterns on its user. The main objective of the European Project BioMot is to improve existing wearable robotic exoskeletons exploiting dynamic sensory-motor interactions and developing cognitive capabilities that may lead to symbiotic gait behavior in the interaction of a human with a wearable robot. BioMot proposes a cognitive architecture for WRs exploiting neuronal control and learning mechanisms the main goal of which is to enable positive co-adaptation and seamless interaction with humans. In this paper we present the research that is conducted to enable positive co-adaptation and more seamless interaction of humans and WRs.

Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

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 paper, 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 five 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 three 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 paper focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body.

An EMG-informed Model to Evaluate Assistance of the Biomot Compliant Ankle Actuator

A main concern that rises when developing active orthoses is how to actively engage the users and monitor how they are affected by the devices. Through EMG-informed neuromusculoskeletal modeling, it is possible to estimate users’ muscle contributions to joint moments generation. We present preliminary results about the application of such models to a subject wearing the BioMot ankle actuator.

Experimental architecture for synchronized recordings of cerebral, muscular and biomechanical data during lower limb activities

In this paper, an architecture that allows the synchronized recording of cerebral, muscular and biomechanical data during lower limb activities has been designed. The synchronization issue has been addressed. The goal is to analyze the relationship between the different signals, first during simple lower limbs movements, then extending the analysis to gait. Five incomplete spinal cord injury patients and four healthy users participated in experiments to validate the architecture. The users were asked to perform simple movements that involve only one or two joints, particularly knee and ankle. Future studies with the recorded data will address several issues, such as creating neuromusculoskeletal models that relate kinematics data with EMG information, improving the decoding of the angles of the lower limb through EEG signals, or analyzing the coherence between the EEG signals and the EMG information.