A Biomimetic Approach to Controlling Restorative Robotics

Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control. Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands. Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques. Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury. Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control.Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands.Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques.Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury

Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

abstract: The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands.View the article as published at https://www.frontiersin.org/articles/10.3389/fnbot.2017.00071/ful

Decoding motor neuron behavior for advanced control of upper limb prostheses

One of the main challenges in upper limb prosthesis control to date is to provide devices intuitive to use and capable to reproduce the natural movements of the arm and hand. One approach to solve this challenge is to use the same control signals for prosthesis control that our nervous system uses to control its muscles. This thesis aims to investigate the possibility of natural, intuitive prosthesis control using neural information obtained with available surface EMG decomposition methods. In order to explore all aspects of such a novel approach, a series of five studies were performed with the final goal of implementing a proof of concept and comparing its performance with state of the art myoelectric control. The performed investigations revealed important insights in motor unit physiology after targeted muscle reinnervation, EMG decomposition in dynamic voluntary contractions of the forearm, and the properties and challenges of neural information based prosthesis control. The main outcome of the thesis is that neural information based prosthesis control is capable to outperform myoelectric approaches in pattern recognition, linear regression and nonlinear regression, as determined by offline performance comparisons. The final proof of concept for this novel approach was a robust regression method based on neuromusculoskeletal modeling. The kinematics estimation of the proposed approach outperformed EMG-based nonlinear regression in both able-bodied subjects and patients with limb deficiency, indicating that using neural information is a promising avenue for advanced myoelectric control.2017-11-3

Design of a cybernetic hand for perception and action

Strong motivation for developing new prosthetic hand devices is provided by the fact that low functionality and controllability—in addition to poor cosmetic appearance—are the most important reasons why amputees do not regularly use their prosthetic hands. This paper presents the design of the CyberHand, a cybernetic anthropomorphic hand intended to provide amputees with functional hand replacement. Its design was bio-inspired in terms of its modular architecture, its physical appearance, kinematics, sensorization, and actuation, and its multilevel control system. Its underactuated mechanisms allow separate control of each digit as well as thumb–finger opposition and, accordingly, can generate a multitude of grasps. Its sensory system was designed to provide proprioceptive information as well as to emulate fundamental functional properties of human tactile mechanoreceptors of specific importance for grasp-and-hold tasks. The CyberHand control system presumes just a few efferent and afferent channels and was divided in two main layers: a high-level control that interprets the user’s intention (grasp selection and required force level) and can provide pertinent sensory feedback and a low-level control responsible for actuating specific grasps and applying the desired total force by taking advantage of the intelligent mechanics. The grasps made available by the high-level controller include those fundamental for activities of daily living: cylindrical, spherical, tridigital (tripod), and lateral grasps. The modular and flexible design of the CyberHand makes it suitable for incremental development of sensorization, interfacing, and control strategies and, as such, it will be a useful tool not only for clinical research but also for addressing neuroscientific hypotheses regarding sensorimotor control

Synopsis of an engineering solution for a painful problem Phantom Limb Pain

This paper is synopsis of a recently proposed solution for treating patients who suffer from Phantom Limb Pain (PLP). The underpinning approach of this research and development project is based on an extension of “mirror box” therapy which has had some promising results in pain reduction. An outline of an immersive individually tailored environment giving the patient a virtually realised limb presence, as a means to pain reduction is provided. The virtual 3D holographic environment is meant to produce immersive, engaging and creative environments and tasks to encourage and maintain patients’ interest, an important aspect in two of the more challenging populations under consideration (over-60s and war veterans). The system is hoped to reduce PLP by more than 3 points on an 11 point Visual Analog Scale (VAS), when a score less than 3 could be attributed to distraction alone

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies