Live Demonstration: Upper Limb Prosthetic Control Using Toe Gesture Sensors and Various Touch Interfaces

Orienting technology, through new techniques, to rehabilitate the disabled and physically challenged and to help elderly is a noble cause, which will improve the quality of life of millions. Prosthetics is one of the important research area where robotic technology serves its best for medical sciences. Conventionally various prosthetic control techniques exists and each has their own advantages and disadvantages. These control techniques ranges from simply implementable, cable-controlled, body-powered prosthetics to technologically challenging Targeted Muscle Reinnervation (TMR) based prosthetics control system. The effectiveness of a upper-limb prosthetic control method for wide-spread application depends on various factors such as cost-effectiveness, ability for easy fabrication, ruggedness, repeatability on daily use, easily installable, usability, ability to bring dexterity and accuracy to perform various gestures/associated activities etc. The current techniques have major drawbacks, namely: with EMG there is a need of relatively complex electronics/sensor electrodes and classification algorithms for implementing large gesture range; EEG suffers from reliability issue and cost; sweating and placement positions of electrodes may interfere with reliable working in case of both EEG and EMG techniques; need of invasive surgery and associated high cost in case of TMR or implanted myo/neural interface etc. We will present demo of a robust, non-invasive, simple, touch-based toe gesture sensor system for upper limb prosthetic control

Emergence of a stable cortical map for neuroprosthetic control.

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram

New developments in prosthetic arm systems

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold - insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods

Pattern recognition methods for EMG prosthetic control

In this work we focus on pattern recognition methods related to EMG upper-limb prosthetic control. After giving a detailed review of the most widely used classification methods, we propose a new classification approach. It comes as a result of comparison in the Fourier analysis between able-bodied and trans-radial amputee subjects. We thus suggest a different classification method which considers each surface electrodes contribute separately, together with five time domain features, obtaining an average classification accuracy equals to 75% on a sample of trans-radial amputees. We propose an automatic feature selection procedure as a minimization problem in order to improve the method and its robustness

A Rehabilitation Engineering Course for Biomedical Engineers

This paper describes an upper division elective course in rehabilitation engineering that addresses prosthetics and orthotics, wheelchair design, seating and positioning, and automobile modifications for individuals with disabilities. Faculty lectures are enhanced by guest lectures and class field trips. Guest lecturers include a prosthetist and a lower extremity amputee client, an engineer/prosthetist specializing in the upper extremity, and a rehabilitation engineer. The lower extremity prosthetist and his client present a case study for prosthetic prescription, fabrication, fitting, alignment, and evaluation. The engineer/prosthetist contrasts body-powered versus externally powered upper extremity prostheses and associated design, fitting, and functional considerations; he also discusses myoelectric signal conditioning, signal processing, and associated control strategies for upper extremity prosthetic control. Finally, the rehabilitation engineer presents case studies related to assessment and prescription of mobility aids, environmental control systems, and children\u27s toys. The course also includes visits to a local prosthetic and orthotic facility to observe typical fabrication, fitting, and alignment procedures and a driver rehabilitation program for exposure to driver assessment, training, and common vehicle modifications. These applications of biomedical engineering to persons with disabilities have been well received by the students and have furthered interdisciplinary design and research projects

Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know

Domain adaptation methods have been proposed to reduce the training efforts needed to control an upper-limb prosthesis by adapting well performing models from previous subjects to the new subject. These studies generally reported impressive reductions in the required number of training samples to achieve a certain level of accuracy for intact subjects. We further investigate two popular methods in this field to verify whether this result also applies to amputees. Our findings show instead that this improvement can largely be attributed to a suboptimal hyperparameter configuration. When hyperparameters are appropriately tuned, the standard approach that does not exploit prior information performs on par with the more complicated transfer learning algorithms. Additionally, earlier studies erroneously assumed that the number of training samples relates proportionally to the efforts required from the subject. However, a repetition of a movement is the atomic unit for subjects and the total number of repetitions should therefore be used as reliable measure for training efforts. Also when correcting for this mistake, we do not find any performance increase due to the use of prior models

Engineering News, Summer 2018

https://scholarcommons.scu.edu/eng_news/1042/thumbnail.jp