Soft Robotics to Enhance Upper Limb Endurance in Individuals with Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disorder that affects the central nervous system and can result in various symptoms, including muscle weakness, spasticity, and fatigue, ultimately leading to the deterioration of the musculoskeletal system. However, in recent years, exosuits have emerged as a game-changing solution to assist individuals with MS during their daily activities. These lightweight and affordable wearable robotic devices have gained immense popularity. In our study, we assessed the performance of an elbow exosuit on eight individuals with MS using high-density electromyography to measure biceps muscle activity. The results demonstrated that our prototype significantly reduced muscle effort during both dynamic and isometric tasks while increasing the elbow range of motion. In addition, the exosuit effectively delayed the onset of muscle fatigue, enhancing endurance for people with MS and enabling them to perform heavy duty tasks for a longer period.

Improving Walking Assistance Efficiency in Real-World Scenarios with Soft Exosuits Using Locomotion Mode Detection.

The use of portable and lightweight wearable assistive devices can improve wearer locomotion efficiency by reducing the metabolic cost of walking. To achieve this goal, assistive technologies must adapt to different locomotion modes to optimize walking assistance. In this work, we developed a novel control strategy for an underactuated soft exosuit featuring a single actuator to assist bilateral hip flexion, which utilized inertial measurement units (IMUs) to discriminate between three different locomotion modes: walking up/down stairs or on level ground. Walking assistance was adjusted in real-time to maximize the assistance provided to the user. In order to preliminary test the effectiveness of this control strategy, four healthy subjects performed a walking task with the exosuit disabled (Exo Off) and enabled (Exo On). Results showed that the kinematics-based IMU classification strategy achieved an overall accuracy exceeding 95% across the three-movement patterns. Subjects were able to save an average of 10.1% on walking energy expenditure with assistance from the wearable device. This work contributes to the development of compact, high-performance lower limb assistive technologies and their development in practical applications.

Bimanual Motor Strategies and Handedness Role in Human-Robot Haptic Interaction.

Bimanual object manipulation involves using both hands to interact with objects in the environment, and the process requires the central nervous system to process sensory feedback and translate it into motor commands. Although there have been significant advancements in haptics and robotics, the kinematic strategies involved in bimanual coupled tasks are still not fully understood. This study aimed to investigate the dynamic interaction between hands during the manipulation of a shared object using two impedance-controlled exoskeletons programmed to simulate bimanual coupled manipulation of virtual objects. Twenty-six participants (right-handed and left-handed) were asked to use both hands to grab and place simulated objects in specific locations. The virtual objects were rendered with four different dynamic properties, affecting the manipulation strategies used to complete the tasks. The results showed that force asymmetries were related to movement direction and handedness preference, with right-handers exhibiting asymmetries related to movement direction and left-handers showing better control of the force applied between their hands. This is possibly due to their constant exposure to objects designed for right-handed use. Additionally, the haptic properties of the virtual objects influenced task performance in terms of timing and failure for all participants. This study demonstrates the potential of advanced technologies to provide realistic simulations of multi-joint movements involving the entire upper extremities. The findings have implications for the development of training programs for bimanual object manipulation tasks and the design of virtual environments that can enhance the learning process.

Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches.

Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R2 = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R2 = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.

A Comprehensive Framework for the Modelling of Cartesian Force Output in Human Limbs.

Neuromuscular functional electrical stimulation represents a valid technique for functional rehabilitation or, in the form of a neuroprosthesis, for the assistance of neurological patients. However, the selected stimulation of single muscles through surface electrodes remains challenging particularly for the upper extremity. In this paper, we present the MyoCeption, a comprehensive setup, which enables intuitive modeling of the user's musculoskeletal system, as well as proportional stimulation of the muscles with 16-bit resolution through up to 10 channels. The system can be used to provide open-loop force control, which, if coupled with an adequate body tracking system, can be used to implement an impedance control where the control loop is closed around the body posture. The system is completely self-contained and can be used in a wide array of scenarios, from rehabilitation to VR to teleoperation. Here, the MyoCeption's control environment has been experimentally validated through comparison with a third-party simulation suite. The results indicate that the musculoskeletal model used for the MyoCeption provides muscle geometries that are qualitatively similar to those computed in the baseline model.

Adaptive Hybrid FES-Force Controller for Arm Exosuit.

Patients suffering from neuromuscular diseases experience motor disabilities which hinder their independence during activities of daily living (ADLs). For such impaired subjects, robotic devices and Functional Electrical Stimulation (FES) are technologies commonly used to rehabilitate lost functions. Nevertheless, both systems present some limitations, and merging FES and robots in Hybrid Robotic Rehabilitation Systems allows to overcome these boundaries. Here we propose for the first time a hybrid cooperative controller involving FES and a soft wearable upper arm exosuit to rehabilitate elbow movements. We tested the designed hybrid controller on six healthy participants. The results showed how the proposed hybrid controller allowed the wearers to perform flexion movements with no significant decrease in accuracy and precision with respect to the exosuit alone, while significantly decreasing the fatigue level by about 63% and delaying its onset with respect to the FES action alone.

Rendering Immersive Haptic Force Feedback via Neuromuscular Electrical Stimulation.

Haptic feedback is the sensory modality to enhance the so-called "immersion", meant as the extent to which senses are engaged by the mediated environment during virtual reality applications. However, it can be challenging to meet this requirement using conventional robotic design approaches that rely on rigid mechanical systems with limited workspace and bandwidth. An alternative solution can be seen in the adoption of lightweight wearable systems equipped with Neuromuscular Electrical Stimulation (NMES): in fact, NMES offers a wide range of different forces and qualities of haptic feedback. In this study, we present an experimental setup able to enrich the virtual reality experience by employing NMES to create in the antagonists' muscles the haptic sensation of being loaded. We developed a subject-specific biomechanical model that estimated elbow torque during object lifting to deliver suitable electrical muscle stimulations. We experimentally tested our system by exploring the differences between the implemented NMES-based haptic feedback (NMES condition), a physical lifted object (Physical condition), and a condition without haptic feedback (Visual condition) in terms of kinematic response, metabolic effort, and participants' perception of fatigue. Our results showed that both in terms of metabolic consumption and user fatigue perception, the condition with electrical stimulation and the condition with the real weight differed significantly from the condition without any load: the implemented feedback was able to faithfully reproduce interactions with objects, suggesting its possible application in different areas such as gaming, work risk assessment simulation, and education.

Relationship Between Muscular Activity and Assistance Magnitude for a Myoelectric Model Based Controlled Exosuit.

The growing field of soft wearable exosuits, is gradually gaining terrain and proposing new complementary solutions in assistive technology, with several advantages in terms of portability, kinematic transparency, ergonomics, and metabolic efficiency. Those are palatable benefits that can be exploited in several applications, ranging from strength and resistance augmentation in industrial scenarios, to assistance or rehabilitation for people with motor impairments. To be effective, however, an exosuit needs to synergistically work with the human and matching specific requirements in terms of both movements kinematics and dynamics: an accurate and timely intention-detection strategy is the paramount aspect which assume a fundamental importance for acceptance and usability of such technology. We previously proposed to tackle this challenge by means of a model-based myoelectric controller, treating the exosuit as an external muscular layer in parallel to the human biomechanics and as such, controlled by the same efferent motor commands of biological muscles. However, previous studies that used classical control methods, demonstrated that the level of device's intervention and effectiveness of task completion are not linearly related: therefore, using a newly implemented EMG-driven controller, we isolated and characterized the relationship between assistance magnitude and muscular benefits, with the goal to find a range of assistance which could make the controller versatile for both dynamic and static tasks. Ten healthy participants performed the experiment resembling functional daily activities living in separate assistance conditions: without the device's active support and with different levels of intervention by the exosuit. Higher assistance levels resulted in larger reductions in the activity of the muscles augmented by the suit actuation and a good performance in motion accuracy, despite involving a decrease of the movement velocities, with respect to the no assistance condition. Moreover, increasing torque magnitude by the exosuit resulted in a significant reduction in the biological torque at the elbow joint and in a progressive effective delay in the onset of muscular fatigue. Thus, contrarily to classical force and proportional myoelectric schemes, the implementation of an opportunely tailored EMG-driven model based controller affords to naturally match user's intention detection and provide an assistance level working symbiotically with the human biomechanics.

On the edge between soft and rigid: an assistive shoulder exoskeleton with hyper-redundant kinematics.

In this paper, we present a prototype of an innovative portable shoulder exoskeleton for human assistance and augmentation. The device provides torques to flexion/extension movements of the shoulder, compensating for gravitational forces, and is passively compliant along the remaining degrees of freedom letting the shoulder moving along them. The novelty of our system is a flexible link, made of a hyper-redundant passive structure, that avoids joint misalignment by adapting to the complex movements of the humerus head, similarly to a soft component. The flexible link is compliant to rotations around one axis but rigid around the other two axes, allowing transmission of flexion/extension torque but kinematically transparent along the remaining degrees of freedom. The device is light weight and allows to cover around the 82% of the shoulder flexion/extension range of motion. The exoskeleton was tested on a cohort of 5 healthy subjects, monitoring shoulder kinematics, interaction forces and acquiring the electromyography of three major muscles contributing to shoulder flexion. During both static postures and dynamic movements, assistance from the exoskeleton resulted in a significant reduction of muscular effort in the anterior (-32.2% in static, -25.3% in dynamic) and medial deltoid (56.9% in static, -49.6% in dynamic) and an average reduction of the biceps brachii.

Toward EMG-controlled force field generation for training and rehabilitation: From movement data to muscle geometry.

EMG signals are often used to control prostheses or assistive devices, but have been rarely used in rehabilitation. We propose a novel approach to personalised rehabilitation, based on EMG-driven force field adaptation. As a step toward this direction, here we show how EMG activity and movement data during a robot-assisted motor task can be used to estimate muscle geometry. We compare three different models of muscle geometry, characterised by (i) constant moment arms (CM); (ii) a normative model, based on polynomial functions of joint angles with fixed coefficients (normative polynomial, NP); and (iii) a person-adaptive model, in which the same polynomial model is fitted to individual subjects data (fitted polynomial, FP). We found that the FP model has the best performance, specially for subjects whose size is farther from 'average'. The fitting results also emphasise the adverse effect of muscles co-contraction.