Estimation of Joint Angle Based on Surface Electromyogram Signals Recorded at Different Load Levels

To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30, 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy

Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking

A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, neuroscience and prosthetics. Many empirical studies have uncovered neural, mechanical and energetic aspects of how humans resolve these degrees of freedom to actuate leg joints for common tasks like walking. However, a unifying theoretical framework that explains the many independent empirical observations and predicts individual muscle and tendon contributions to joint actuation is yet to be established. Here we develop a computational framework to address how the ankle joint actuation problem is resolved by the neuromuscular system in walking. Our framework is founded upon the proposal that a consideration of both neural control and leg muscle-tendon morphology is critical to obtain predictive, mechanistic insight into individual muscle and tendon contributions to joint actuation. We examine kinetic, kinematic and electromyographic data from healthy walking subjects to find that human leg muscle-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics in walking. This optimal realization (a) corresponds to independent empirical observations of operation and performance of the soleus and gastrocnemius muscles, (b) gives rise to an efficient load-sharing amongst ankle muscle-tendon units and (c) causes soleus and gastrocnemius muscle fibers to take on distinct mechanical roles of force generation and power production at the end of stance phase in walking. The framework outlined here suggests that the dynamical interplay between leg structure and neural control may be key to the high walking economy of humans, and has implications as a means to obtain insight into empirically inaccessible features of individual muscle and tendons in biomechanical tasks.National Institutes of Health (U.S.) (NIH Pioneer Award DP1 OD003646)Massachusetts Institute of Technology. Media Laboratory (Consortia Account 2736448)Massachusetts Institute of Technology. Media Laboratory (Consortia Account 6895867

Enhancing Biomechanical Function through Development and Testing of Assistive Devices for Shoulder Impairment and Total Limb Amputation

Assistive devices serve as a potential for restoring sensorimotor function to impaired individuals. My research focuses on two assistive devices: a passive shoulder exoskeleton and a muscle-driven endoprosthesis (MDE). Previous passive shoulder exoskeletons have focused on testing during static loading conditions in the shoulder. However, activities of daily living are based on dynamic tasks. My research for passive shoulder exoskeletons analyzes the effect that a continuous passive assistance has on shoulder biomechanics. In my research I showed that passive assistance decreases the muscular activation in muscles responsible for positive shoulder exoskeleton. An MDE has the potential to have accurate and precise control of movement as well as restore a sense of proprioception to the user. Such a transformative and invasive device has never previously been tested. Therefore, my research focused on analyzing fundamental principles of the MDE in an in-vivo rabbit model. The two concepts I tested in my research were the feasibility of implanting an orthopedic device underneath the skin at the distal end of a limb following amputation and the locomotor restorative capabilities of an artificial tendon used for muscle-device connection. In my work I proved the feasibility of implanting fully-footed rigid endoprostheses underneath the skin and isolated the primary factors for a successful surgery and recovery. In addition, my research showed that although artificial tendons have the potential to restore locomotor function, proper in-situ tendon lengths must be achieved for optimal movement. This research informed the design and testing of a fully jointed muscle-driven endoprosthesis prototype

Optimizing User Integration for Individualized Rehabilitation

User integration with assistive devices or rehabilitation protocols to improve movement function is a key principle to consider for developers to truly optimize performance gains. Better integration may entail customizing operation of devices and training programs according to several user characteristics during execution of functional tasks. These characteristics may be physical dimensions, residual capabilities, restored sensory feedback, cognitive perception, or stereotypical actions

Nonlinearity Compensation in a Multi-DoF Shoulder Sensing Exosuit for Real-Time Teleoperation

The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi-DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.Comment: 8 pages, 7 figures, 3 tables. Accepted to be published in IEEE RoboSoft 202