Assistive Devices For Rehabilitation Research Articles

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning.

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

Guidelines for setting up low-vision and rehabilitation services in India.

Low vision and blindness are increasing public health issues impacting individuals' quality of life. During clinical low-vision services, vision rehabilitation is crucial for enhancing daily living skills and improving life quality. Low-vision and rehabilitation (LVR) services encompass comprehensive measures that aid visually disabled individuals in restoring function, autonomy, and social participation. Such holistic management requires a multidisciplinary approach, facilitating adaptation to environmental and sociocultural changes. However, the lack of awareness about the principles and practices of LVR services poses a major hindrance to setting up such a special clinic in the eye hospital. This article is about a consensus statement on the guidelines for establishing LVR services focusing on basic requirements, especially in low-resource countries. The present recommendation to set up an LVR clinic was made after group discussions and debates among various experts and stakeholders during the National Workshop on Strengthening Low-Vision and Rehabilitation Services organized at Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi. The event was participated by many ophthalmologists and optometrists coming from across the country. The recommendations required at the tertiary level are outlined under the four headings: Human resources and training: two ophthalmologists, that is, a low vision specialist or a well-experienced in low vision and one who has received one-week orientation and training, one optometrist, one rehabilitation supervisor, and two rehabilitation assistants; Assessment equipment: basic screening and diagnostic; Assistive devices for low vision, including, digital and non-digital; Assistive devices for rehabilitation, and methods for records maintenance. The institution may not follow strictly the present guidelines but will provide an idea on LVR services initiation.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

Personalized Symmetrical and Asymmetrical Gait Generation of a Lower Limb Exoskeleton

Personal assistive devices for rehabilitation will be in increasing demand during the coming decades due to demographic change, i.e., an aging society. Among the elderly population, difficulty in walking is the most common problem. Even though there are commercially available lower-limb exoskeleton systems, the coordination between user and device still needs to be improved to achieve versatile personalized gaits. To tackle this issue, an advanced EXOskeleton framework for Versatile personalized gaIt generation with a Seamless user-exo interface (called “EXOVIS”) is proposed in this study. The main control of the framework uses adaptive bio-inspired modular neural mechanisms. These mechanisms include decoupled central pattern generators (CPGs) with Hebbian-based synaptic plasticity and adaptive CPG post-processing networks with error-based learning. The control method facilitates the rapid online learning of personalized walking gaits described by the walking frequency as well as hip, knee, and ankle joint patterns. The method is verified on a real lower-limb exoskeleton system with six degrees of freedom (DOFs) on different subjects under static and dynamic conditions such as flat terrain and a split-belt treadmill. The results show that the proposed method can not only automatically learn to generate personalized symmetrical gaits, but also asymmetrical gaits, which have not been explicitly shown by other approaches so far.

Mixed [Formula: see text]-synthesis tracking control and disturbance rejection in a robotic digit of an impaired human hand for anthropomorphic coordination.

In a partially impaired anthropomorphic hand, maintaining the movement coordination of the robotic digits with the central nervous system (CNS) and natural digits is crucial for robust performance. A challenge in the control perspective of movement coordination of a human hand is finding methods robust to the disturbances in a well-posed control problem of a biomechanical model. We use visco-elastic dynamics in the human palm frame of reference to explore the biomechanics of movement coordination to solve this control problem. Our biomechanical model incorporates the time delay due to actuation force, parametric uncertainty, exogenous disturbances, and sensory noise to constitute a 21-degree of freedom model. A mixed [Formula: see text]-synthesis controller, considering the real parametric uncertainty, represents the CNS in the control paradigm. We consider the robotic finger's flexion movement when perturbed from the initial equilibrium. The controller provides feedback force at the joints to regulate the robotic finger movement. The index finger follows a reference trajectory of the joint angular position profile and stabilizes at a flexion angle of 1 rad/s at a time of 1 s. The main control objective is to keep the angular displacement of the finger joint constant when a disturbance force acts. We simulate the modeling scheme in MATLAB/ Simulink. The results demonstrate that our controller scheme is robust against the worst-case disturbance and achieves the desired performance value. Developing a biologically inspired neurophysiological controller with robust performance has many applications, including assistive rehabilitation devices, hand movement disorder diagnosis, and robotic manipulators.

Identifying Interaction Forces Via EMG Under Changing Motion Dynamics.

Musculoskeletal injuries can severely inhibit performance of activities of daily living. In order to regain function, rehabilitation is often required. Assistive rehabilitation devices can be used to increase arm mobility by guiding therapeutic exercises or assisting with motion. Electromyography (EMG) may be able to provide an intuitive interface between the patient and the device if appropriate classification models allow smart systems to relate these signals to the desired device motion. Unfortunately, the accuracy of pattern recognition models classifying motion in constrained laboratory environments significantly drops when used for detecting dynamic unconstrained movements. The objectives of this study were to quantity how various motion factors affect arm muscle activations during dynamic motion, and to use these motion factors and EMG signals for detecting interaction forces between the person and the environment during motion. The results quantity how EMG features change significantly with variations in arm positions, interaction forces, and motion velocities. The results also show that pattern recognition models were able to detect intended characteristics of motion based solely on EMG signals. Prediction of force was improved from 73.77% correct to 79.17% accuracy during elbow flexion-extension by properly selecting the features, and providing measurable arm position and velocity information as additional inputs to a linear discriminant analysis model.

Modelling and Design of Asymmetric Vibrations to Induce Bidirectional Force Sensation for Portable Rehabilitation Devices.

Assistive rehabilitation devices have been developed to help post-stroke patients to recover and live independently for a number of years. As a way to communicate with the physical world, force sensation is extremely helpful to rebuild neuroplasticity [1] during the rehabilitation process. This paper presents a model and design of asymmetric vibrations to provide bidirectional force sensation, which can be beneficial to design a portable rehabilitation haptic device. Users will feel a directional cue generated by asymmetric vibrations by holding the device. This directional cue can navigate users around in a rehabilitation training along with visual guidance and provide physical force sensations. The system consists of a current-drive single solenoid rigidly attached to a base. The system's model is verified through experiment at three different frequencies. Our analysis shows that by varying the signal's duty ratio, the direction of peak accelerations change from positive to negative. In addition, two other waveforms (saw-tooth and step-ramp) at several frequencies and different spring's stiffness are also discussed to determine the ideal characteristics of the input signal for rehabilitation applications.

Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device

The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.

Motor-Imagery based EEG Signals Classification using MLP and KNNClassifiers

The electro encephalo gram (EEG) signals classification playsa major role in developing assistive rehabilitation devices for physically disabled performs. In this context, EEG data were acquired from 20 healthy humans followed by the pre-processing and feature extraction process. After extracting the 12-time domain features, two well-known classifiers namely K-nearest neighbor (KNN) and multi-layer perceptron (MLP) were employed. The fivefold cross-validation approach was utilized for dividing data into training and testing purpose. The results indicated that the performance of MLP classifier was found better than the KNN classifier. MLP classifier achieved 95% classifier accuracy which is the best. The outcome of this study would be very useful for online development of EEG classification model as well as designing the EEG based wheelchair.

Strength Evaluation and Modification of a 3D Printed Anterior Ankle Foot Orthoses

Ankle foot orthosis (AFO) is widely used to prevent foot drop and improve walking ability for individuals with cerebral palsy and stroke. However, traditional anterior AFO (TAAFO) could only last within months because the bilateral neck of TAAFO was easy to break. Currently, a 3D-printing technique is used to develop assistive devices for rehabilitation. The study aimed to implement the finite element (FE) method to revise the 3D printed AAFO (3DP-AAFO) and evaluate its strength. A 3.2 mm-thickness for the TAAFOs and 3DP-AAFOs were fabricated, respectively. The stiffness of TAAFO and 3DP-AAFO were tested by a material machine and compared to the FE model. In the FE analysis, the thickness of AAFO model was increased at the neck to enhance its strength. A plantarflexion and dorsiflexion moment were respectively subjected to 3DP-AAFO models to undergo stress analysis. Under the mechanical test, the 3DP-AAFO (K = 1.09 Nm/degree) was 7.8 times stiffer than the traditional AAFO (K = 0.14 Nm/degree). The FE results showed that thickening the 3DP-AAFO on the neck up to 4.7 mm could moderate stress concentration and increase the stiffness of the 3DP-AAFO. Therefore, the study concluded that the 3DP-AAFO was stiffer than the traditional AAFO. Increasing the appropriate thickness around neck of 3DP-AAFO could avoid neck fracture as much as possible.

EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.

Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.

Design of intention-based assistive robot for upper limb

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Análisis de coherencia de señales EEG en locomoción usando grafos

One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.

ASSISTIVE REHABILITATION DEVICES: THEORY AND PRACTICE OF SELECTION AND PRESCRIPTION IN MEDICAL ORGANIZATIONS

In this article the authors examine the place and role of assistive technologies in facilitating limitations of disabled people's lives, the basic principles of medical and social diagnostics with the use of International Classification of Functioning, Disability and Health (ICF), organizational and legal aspects of providing assistive rehabilitation devices, the basic principles and procedures of their prescription by practicing doctors

Translating civilian and defense technologies for pediatric critical care and rehabilitation research

A conference sponsored by the National Institutes of Health (NIH) and the Defense Advanced Research Projects Agency (DARPA) titled "Translating Civilian and Defense Technologies for Pediatric Critical Care and Rehabilitation Research" was held on May 16th and 17th, 2005 in Rockville, Maryland. A summary of presentations from the conference is provided. Topics presented addressed: advances in monitoring and imaging devices used in the pediatric intensive care unit, regulatory issues and recent technological developments relating to medical devices for children, the new role that virtual reality is playing in rehabilitation medicine, and the evolving future of assistive devices for rehabilitation.

A mathematical model for evaluation of forces in lower extremeties of the musculo-skeletal system

This study deals with the development of a mathematical model for the musculo-skeletal system capable of evaluating muscle forces and joint reactions for different static postures. The problem is formulated as a linear program and solved by means of a simplex algorithm. Different criteria are used for evaluation of the muscle forces necessary to maintain the human body equilibrium in standing, leaning and stooping. The analytical results show good correlation with electromyographic signals from surface electrodes placed on the superficial muscles. The feasibility of this approach for quantitative analysis of musculoskeletal forces is demonstrated. The technique represents the first attempt at a mathematical model of the musculo-skeletal system and can be readily extended for complete simulation of the force system controlling human body actions and motions. It provides a valuable tool for diagnosis of muscle malfunctions, changes in musculoskeletal forces resulting from injury and synthesis of assistive devices for rehabilitation.