Field Of Rehabilitation Engineering Research Articles

Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs.

The development of advanced prosthetic devices that can be seamlessly used during an individual's daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including a feedforward neural network with one hidden layer, a feedforward neural network with multiple hidden layers, a temporal convolutional network, and a convolutional neural network with squeeze-and-excitation operations were evaluated in real-time, human-in-the-loop experiments with able-bodied participants and an individual with an amputation. Our results demonstrate that deep learning architectures outperform shallow networks in decoding motor intent, with representation learning effectively extracting underlying motor control information from EMG signals. Furthermore, the observed performance improvements by using deep neural networks were consistent across both able-bodied and amputee participants. By employing deep neural networks instead of a shallow network, more reliable and precise control of a prosthesis can be achieved, which has the potential to significantly enhance prosthetic functionality and improve the quality of life for individuals with amputations.

Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals

Objective: To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements. Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %. Conclusions: An algorithm based on pattern recognition of EMG signals was used to carry out a comparative study of groups of features that allow for the differentiation of the angular position of the wrist in terms of flexion-extension movements. The method has the potential for application in the field of rehabilitation engineering to detect the user’s movement intent.

Design and Control of a Mechatronic Exoskeleton for Rehabilitation Purposes

The field of rehabilitation engineering has witnessed significant advancements in recent years, with the development of mechatronic exoskeletons playing a crucial role in enhancing the rehabilitation process. This paper presents the design and control of a novel mechatronic exoskeleton specifically designed for rehabilitation purposes. The primary objective of this research is to provide a comprehensive framework that enables effective and safe assistance during the rehabilitation process.The design phase of the mechatronic exoskeleton focuses on achieving a balance between functionality, comfort, and ergonomics. Extensive biomechanical analysis was conducted to ensure compatibility between the exoskeleton and human anatomy. The exoskeleton's structural components were designed using lightweight and durable materials to optimize portability and minimize user fatigue. Additionally, the design incorporates adjustable joint mechanisms to accommodate a wide range of motion and to adapt to individual user requirements. The design and control of mechatronic exoskeletons for rehabilitation purposes hold great potential to revolutionize the field of physical therapy. These wearable robotic systems offer personalized assistance and targeted therapy to individuals with physical impairments, aiding in their recovery and improving their quality of life. Through careful consideration of comfort, ergonomics, actuation, sensing, control strategies, and safety measures, mechatronic exoskeletons can provide effective and safe rehabilitation solutions. Continued research, technological advancements, and collaboration between various disciplines will further enhance the capabilities and impact of these innovative devices in the future.

A Novel HD-sEMG Preprocessing Method Integrating Muscle Activation Heterogeneity Analysis and Kurtosis-Guided Filtering for High-Accuracy Joint Force Estimation.

This study proposes a novel preprocessing method integrating muscle activation heterogeneity analysis and kurtosis-guided filtering to realize high-accuracy surface electromyogr-aphy (sEMG)-based force estimation. A total of 10 subjects were recruited. Each subject performed isometric elbow flexion tasks at 20%, 40%, and 60% maximum voluntary contraction (MVC) target force levels, and the joint force and high-density sEMG (HD-sEMG) signals from biceps brachii and brachialis were collected synchronously. The force estimation model was built using three-order polynomial fitting technique. The input signal extraction of the force model, also named as the preprocessing of HD-sEMG signal, was carried out in the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas was selected; and last, a kurtosis-guided filter was designed to process the selected principal component to obtain the input signal. For the sake of comparison, the joint force estimation experiments based ON five preprocessing methods were conducted. The experimental results demonstrated that the proposed method obtained 52%, 53%, and 59% reduction in the mean root mean square difference at 20% MVC, 40% MVC, and 60% MVC force-level tasks, respectively, compared to the preprocessing method with the first principal component plus fixed parameter filtering. This proposed HD-sEMG pre-processing method has reliable neuromuscular electro-physiological foundation, and has good application value for realizing high-accuracy muscle/joint force estimation in the fields of rehabilitation engineering, sports biomechanics, and muscle disease diagnosis etc.

Robust feature sets for contraction level invariant control of upper limb myoelectric prosthesis

In spite of the tremendous progress of upper limb myoelectric prosthetic control in the field of rehabilitation engineering, there still exist several real world challenges to be met, before realizing it as a good substitute for a natural arm. Incompetence of the system to accommodate variations in contraction levels of muscle movements has been identified as one of the significant challenges, as these variations have a subsequent impact on the performance of pattern recognition based myoelectric control. Non-linear techniques are more suited to characterize myoelectric signals since one of their major properties is nonlinearity. Based on this we propose two feature combinations which can lead to a reliable control scheme that is robust against contraction level variations. The performance of our proposed features when tested on nine transradial amputees for six motion classes at three different force levels outweighed other established feature extraction methods meant for contraction variation independent control. Significant improvement of around 8% in average classification performance was achieved across all subjects and force levels, subjected to training, both with all force levels and with unseen force levels. Moreover, these features achieved superior performance in classifying flexion as well as grip movements.

The American Student Placements in Rehabilitation Engineering Program (ASPIRE)

Background: American Student Placements and Internships in Rehabilitation Engineering is founded on the principal of sparking the interest in a new generation of rehabilitation engineering scientists to transform the lives of older adults and people with disabilities. Each year a minimum of 10 students were enrolled.Methods: The internship runs for 10 weeks and activities center on developing excitement about technology and engineering and understanding the principles and processes of conducting rehabilitation engineering research. This was accomplished by student participation in 1) rehabilitation engineering research projects; 2) educational research training, and 3) professional activities with clinical and engineering faculty, staff, and graduate students. Of the 162 participants, 53% were women and 47% men. 23% of participants were from minority or underrepresented groups, and 18% identified as having a disability.Results: From post-internship follow-up efforts, 65% of program participants went on to pursue or have graduated with advanced degrees, 27% of whom are engineers from underrepresented groups. Of those students that are working post-baccalaureate, 70% are engineers working in a biomedical/technology field. In the past 3 years, 92% of students stated that the program met or exceeded their expectations, while 100% of the 2018 group felt that their expectations were met.Conclusion: The American Student Placements and Internships in Rehabilitation Engineering program has demonstrated efficacy in preparing undergraduate students for future academic work and employment.Implications for RehabilitationCreative researchers and designers will lead the way in advancing accessibility standards and engineering for people with disabilities.The ASPIRE program increases excitement for and interest in the fields of rehabilitation engineering among undergraduate students to positively influence academic and professional careers.Students enrolled in the ASPIRE program actively participated in a real multidisciplinary project supervised by professor mentors. This led students to take a problem-based approach in their professional development.The ASPIRE program stimulates rehabilitation engineering in students’ mindset and promotes inclusive academic environments and communities.Rehabilitation engineering is not a mainstream discipline, but the ASPIRE program indicates that there are benefits to student education, including participatory action engineering, that need to continue gain momentum until rehabilitation engineering is a mainstream discipline or a core component of engineering education.We must move beyond a “survival of the fittest” mentality. A “survival of the fittest” model places a disproportionate burden on groups that are underrepresented in science and engineering, and thereby postpones the day on which the demographics of science and engineering are comparable to that of society as a whole.The ASPIRE program needs to be replicated at various institutions around the world.

High-speed gesture modelling through boundary analysis of active signals from wearable data glove

Assistive technology utilises communication by means of gestures with high appreciation in the fields of rehabilitation engineering and security. The necessity for such technique is to extract the communications in terms of intentions from the disabled community. Such systems play a very crucial role during emergency as well as to perform regular communication during normal course of life. To interpret and communicate most distinctive requirements of the person with disability, the caregivers and medical support agents need a well- defined and distinguishable gesture paradigm with its recognition system. Conversion of such communicative gestures is to be made precise and easy. In this proposed work, the feature construction is made with a simple modelling of the gesture signals along the time zone considerations created during the gesture activity. The identification of most active channels for every subject involved in this experiment for different gestures contributes to reduction of complexity in processing and hardware cost.

Decoding Arm Movements by Myoelectric Signal and Artificial Neural Networks

The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (sEMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68% - 88%.

Electrical stimulation of mechanoreceptors

Within the field of Rehabilitation Engineering, this work is aimed at identifying the optimal parameters of electric current stimulus which activate the nervous axons of mecanoreceptors found in the fingertip, allowing, this way, to resemble tactile senses. These sensorial feelings can be used by aiding technological means, namely, the sensorial substitution technology, in an attempt to render information to blind people through the tactile sense. The physical pressure on sensorial areas (fingertips) used for reading activities through the Braille System is the main effect that is imitated and studied in this research work. An experimental aiding prototype for Braille reading research has been developed and tested with blinds and reduced vision people, with highly satisfactory results.

GCUA Humanoid Robotic Hand with Tendon Mechanisms and Its Upper Limb

Upper limbs of human beings are extremely special and significant, which obtain crucial function to achieve manipulations intelligently and dexterously. Gesture-Changeable Under-Actuated (GCUA) grasping function is presented to improve the capability of robotic hands to achieve humanoid manipulations with low dependence on control and sensor feedback systems, which includes traditional under-actuated (UA) grasping motion and special pre-bending (PB) motion. Based on GCUA function, GCUA Hand II is developed, which has 5 fingers and 14 DOF. All the fingers use similar tendon mechanisms and motors to achieve GCUA function. With GCUA Hand II, a humanoid robot upper limb system is designed, which has two 3-DOF arms actuated by stepper motors and two 14-DOF hands actuated by DC motors. The control system includes four parts: a computer, a FPGA motion controller, a driver module, and a user module, which can control the upper limb system to do various movements dexterously and exactly. With C++ language, a spatial motion program is designed to assist researchers to determine spatial motions of the upper limb system. This system has a great prospect in the field of rehabilitation engineering, extremely environmental manipulation, humanoid robotics and social services.

State of the Science for Pediatric Rehabilitation Engineering

Over the past 25 years the field of rehabilitation engineering has recognized the need for a specific focus on the technological needs of children with disabilities as those needs often differ from those of adults [2]. Among the important efforts in a number of nations has been the support of a Rehabilitation Engineering Research Center (RERC) on technology for children with orthopedic disabilities by the United States Department of Education. The National Institute on Disability and Rehabilitation Research (NIDRR) earliest RERC on pediatrics influenced national rehabilitation policy and had a significant impact on the ‘Tech Act’ legislation of the early 1990s [13]. The NIDRR shifted the focus of this RERC in 1990 from leading the creation of technology service infrastructure to conducting research and development in the area of prosthetics and orthotics for children with orthopedic disabilities. The legacy of the Rancho Los Amigos RERC over its three funding cycles from 1990 to 2005, is now seen in a large number of commercially available products that meet the needs of children. Later, the NIDRR again shifted the focus of its pediatric RERC to newly emerging issues, and awarded this RERC on Technology for Children with Disabilities to our partnership between New Jersey Institute of Technology and the Childrens’ Specialized Hospital. We introduced the blending of emerging biomechanics, neuroscience and cognitive science with new therapeu-

RESIDUAL CAPABILITIES OF HEMIPLEGIC PATIENTS TO RESTORE HAND FUNCTIONS VIA A NON-INVASIVE FUNCTIONAL ELECTRICAL STIMULATION SYSTEM

Control strategies are the chief attraction in the field of rehabilitation engineering, and especially in a functional electrical stimulation (FES) system, a reliable control method is important for paralyzed patients to restore lost their functions. In this paper, we have presented a demonstration of the control strategy, which is based on the patient-driven loop, used in a non-invasive FES system for hand function restoration. With the patient-driven loop control, hemiplegic patients could use their residual capabilities, such as shoulder movements in their sound extremities, the myoelectric signals generated from different muscles, etc, to operate the FES system. Here we have chosen the most common and acceptable signals as the input sources, i.e. electromyographic (EMG) signals, to control a non-invasive FES system, generating the electrical stimuli to excite the paralyzed muscles. In addition, EMG signals recorded by the sensors in the electrical stimulator can serve both as the trigger of the system and as the adjustment of the electrical stimulation parameters, thereby improving the system's performance and reliability. From the experimental results, subjects can successfully use their residual capabilities to control the FES system and restore their lost hand functions as well. On the other hand, from the viewpoints of rehabilitation and psychology, hemiplegics will benefit greatly by using their residual capabilities to regain their lost functions. It is believed that the patient-driven loop control is very useful, not only for the FES system in this study, but also for other assistive devices. By the control strategy proposed in this paper, we deeply hope that patients will benefit greatly and regain their self-confidence.

A commentary: the impact of the IEEE TRANSACTIONS ON REHABILITATION ENGINEERING on the field of rehabilitation engineering and science.

The IEEE TRANSACTIONS ON REHABILITATION ENGINEERING, founded 8.5 years ago, has survived, thrived, been of high quality, attracted the best authors in its field and related fields, been the publication of choice for manuscript submission in the rehabilitation engineering and related science area, possessed a wide and international distribution and loyal readership, been oft-cited, been financially sound, and made a marked impact on the field of Rehabilitation Engineering.

Ideas to help break down barriers: the RESNA Student Scientific Paper/Paralyzed Veterans of America Student Design Competition winners

Each year, RESNA hosts two competitions at its annual conference. The RESNA Student Scientific Paper Competition promotes high-quality research related to the fields of rehabilitation engineering and assistive technology. The Paralyzed Veterans of America Student Design Competition recognizes exemplary projects developed by students from all disciplines related to assistive technology. Both competitions are intended to encourage students from a variety of disciplines to address issues in the field of assistive technology and submit papers for presentation at the annual RESNA conference. This article presents summaries of the 1999 competition winning entries which covered the following topics: relating wheelchair setup to propulsion biomechanics; gender differences in the kinematic features of manual wheelchair propulsion; comparison of side-to-side wheelchair propulsion forces using an ARMA modeling technique; feasibility of EEG control for a hand grasp neuroprosthesis; clinical use of in situ pressure sensors for prosthetic socket fit; bowling ramp modification to increase autonomy and normalization; passive robot for frail visually impaired people; a telephone device to improve communication; voice operated wheelchair; wheelchair-based mounting system for automated positioning of electronic augmentative communication device.

Rehabilitation engineering education at the University of Virginia.

The graduate rehabilitation engineering program of study at the University of Virginia is the first program of its type in the United States. The first students were admitted to the program in the fall of 1979. The program is designed to train students with engineering and clinical science backgrounds in the field of rehabilitation engineering. Emphasis is placed on practical training through internship activities at the University of Virginia Rehabilitation Engineering Center and Medical School Department of Orthopedics and Rehabilitation. Field experience is received at the Woodrow Wilson Rehabilitation Center and the University of Virginia Children's Rehabilitation Center.