Identificación de intención de movimiento de marcha sobre diferentes terrenos -una aproximación bioinspirada-

En este documento se propone una aproximación al entendimiento del lenguaje neuromotor de una persona con una amputación por encima de rodilla, mediante la identificación de su intención de movimiento a partir de la percepción de las manifestaciones tanto internas como externas de los Patrones de Acción Fija (PAF) mediante el uso de una propiocepción artificial y la exterocepción de su prótesis durante el desarrollo de diferentes gestos (marcha, subir y bajar escaleras y subir y bajar rampas). Se presentan la formalización de una expresión General del Gesto Rítmico, los procedimientos para la generación de PAF artificiales y un Algoritmo de Respuesta ante el Desarrollo de Gestos. Mediante la identificación de la intención del usuario a través de la información propioceptiva y exteroceptiva, la prótesis discrimina entre un repertorio de PAF artificiales y selecciona el más adecuado para satisfacer las necesidades de movimiento del usuario. Los datos experimentales de las pruebas desarrolladas en individuos sanos y amputados mostraron un alto desempeño en la identificación de la intención del usuario (97.06% de identificaciones correctas) y un buen seguimiento de los gestos de movimiento independientemente de la velocidad con que fueron ejecutadosIn this paper we propose an approach to the neuromotor language of the transfemoral amputee user. We do this by identifying the user’s intention from the perception of both internal and external manifestations (to be explained into a next section) of Fixed Action Patterns (FAPs, by making use of artificial proprioception and exteroception of the prosthesis. The formalization of a General Expression of the Rhythmic Gesture, generation procedures for artificial FAPs, and a Response Algorithm for Gestures Development are presented. By identifying the user’s intention through proprioceptive and exteroceptive information, the prosthesis discriminates between repertories of artificial FAPs and chooses the most suitable one to meet the users’s requirements. Experimental data of tests carried out in healthy and amputee individuals showed high performance on identification (97.06 % of true identifications) of the user’s intention and good tracking of gestures such as gait, walking up stairs, down stairs, up hill and down hill, independently of the speed of execution of the gestur

Human-centered Electric Prosthetic (HELP) Hand

Through a partnership with Indian non-profit Bhagwan Mahaveer Viklang Sahayata Samiti, we designed a functional, robust, and and low cost electrically powered prosthetic hand that communicates with unilateral, transradial, urban Indian amputees through a biointerface. The device uses compliant tendon actuation, a small linear servo, and a wearable garment outfitted with flex sensors to produce a device that, once placed inside a prosthetic glove, is anthropomorphic in both look and feel. The prosthesis was developed such that future groups can design for manufacturing and distribution in India

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

EMG-driven control in lower limb prostheses: a topic-based systematic review

Background The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts. Objective and methodologies This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers. Results and conclusions The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important to improve the quality of the daily life of the patients with motion disorders. Although neuroprosthetic devices restore partially the lost functionality, the user acceptance is low, possibly due to the artificial and unnatural operation of the devices. This thesis addresses this problem in reach-to-grasp motions with the development of shared control approaches that enable a seamless and more natural operation of hand prosthesis. In the first part, we focus on the identification of the grasping intention during the reach-to-grasp motion with able-bodied individuals. We propose an Electromyographic (EMG)-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. In this approach, the utilization of Echo State Networks encloses efficiently the dynamics of the muscle activation enabling a fast identification of the grasp type in real-time. We also examine the impact of different object distance and speed on the detection time and accuracy of the classifier. Although the distance from the object has no significant effect, fast motions influence significantly the performance. In the second part, we evaluate and extend our approach on four real end-users, i.e. individuals with below the elbow amputation. For addressing the variability of the EMG signals, we separate the reach-to-grasp motion into three phases, with respect to the arm extension. A multivariate analysis of variance on the muscle activity reveals significant differences among the motion phases. Additionally, we examine the classification performance on these phases and compare the performance of different pattern recognition methods. An on-line evaluation with an upper-limb prosthesis shows that the inclusion of the reaching motion in the training of the classifier improves importantly classification accuracy. In the last part of the thesis, we explore further the concept of motion phases on the EMG signals and its potentials on addressing the variability of the signals. We model the dynamic muscle contractions of each class with Gaussian distributions over the different phases of the overall motion. We extend our previous analysis providing insights on the LDA projection and quantifying the similarity of the distributions of the classes (i.e grasp types) with the Hellinger distance. We notice larger values of the Helinger distance and, thus, smaller overlaps among the classes with the segmentation to motion phases. A Linear Discriminant Analysis classifier with phase segmentation affects positively the classification accuracy

A review on the usability,flexibility, affinity, and affordability of virtual technology for rehabilitation training of upper limb amputees

(1) Background: Prosthetic rehabilitation is essential for upper limb amputees to regain their ability to work. However, the abandonment rate of prosthetics is higher than 50% due to the high cost of rehabilitation. Virtual technology shows potential for improving the availability and cost-effectiveness of prosthetic rehabilitation. This article systematically reviews the application of virtual technology for the prosthetic rehabilitation of upper limb amputees.(2) Methods: We followed PRISMA review guidance, STROBE, and CASP to evaluate the included articles. Finally, 17 articles were screened from 22,609 articles.(3) Results: This study reviews the possible benefits of using virtual technology from four aspects: usability, flexibility, psychological affinity, and long-term affordability. Three significant challenges are also discussed: realism, closed-loop control, and multi-modality integration.(4) Conclusions: Virtual technology allows for flexible and configurable control rehabilitation, both during hospital admissions and after discharge, at a relatively low cost. The technology shows promise in addressing the critical barrier of current prosthetic training issues, potentially improving the practical availability of prosthesis techniques for upper limb amputees