Control Of Assistive Devices Research Articles

Design and Control of a Wearable Stewart Platform-Type Ankle-Foot Assistive Device

The design and control of an active ankle-foot rehabilitation orthotic system that was designed as a wearable and portable rehabilitation and walking assistive tool is presented. This device can measure and assist the six degree of freedoms (DOFs) movement of the human ankle joint by using a Stewart platform mechanism, which can adapt to the displacement of the rotation axis during the movement of a human foot. The estimation method of an instantaneous rotation axis of ankle-foot motion is also proposed. In this paper, the motion measurement and motion control performance of the developed assistive device is evaluated. Static and dynamic motion measurement and motion reproduction performance verification experiments are conducted. The experimental results showed that the developed assistive device is enough for measuring and controlling the human ankle-foot motion.

A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury

This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (> 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.

Identification of isometric contractions based on High Density EMG maps

Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve pattern-recognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it.The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the “isolated masses” method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

2A1-A10 舌の運動を利用した移動支援ロボットの制御(感覚・運動・計測(1))

Tongue movements usually remain unaffected after spinal cord injury (SCI) on cervical level. Such SCI patients are able to perform voluntary tongue movements that can be detected and utilized for control of assistive technology devices. In this study, we investigated the magnitude of the tongue tip force for different positions of the tongue-palatal contact area and the change of the force during the exertion time. For the experiments we designed a palatal mouthpiece with seven thin-film force sensors. The information from these experiments was used for the development of three tongue control methods (on/off, time-dependent, and force-dependent) that were tested with a mobility assistance robot. Results from the tongue control of the robot showed that the force-dependent control algorithm is most efficient. It allows proportional control of the assistance robot. The user can control easily the direction and the speed of the robot by changing the position and force of the tongue.

Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors.

In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study. Subjects were instructed to perform six functional tasks while their muscle activation patterns were recorded by ten surface electrodes placed on the forearm and hand of the impaired limb. In order to identify intended functional tasks, a pattern classifier using linear discriminant analysis was applied to the EMG feature vectors. The classification accuracy was mainly affected by the impairment level of the subject. Mean classification accuracy was 71.3% for moderately impaired subjects (Chedoke Stage of Hand 4 and 5), and 37.9% for severely impaired subjects (Chedoke Stage of Hand 2 and 3). Most misclassification occurred between grip tasks of similar nature, for example, among pinch, key, and three-fingered grips, or between cylindrical and spherical grips. EMG signals from the intrinsic hand muscles significantly contributed to the inter-task variability of the feature vectors, as assessed by the inter-task squared Euclidean distance, thereby indicating the importance of intrinsic hand muscles in functional manual tasks. This study demonstrated the feasibility of the EMG pattern classification technique to discern the intent of stroke survivors. Future work should concentrate on the construction of a subject-specific EMG classification paradigm that carefully considers both functional and physiological impairment characteristics of each subject in the target task selection and electrode placement procedures.

Discreet discrete commands for assistive and neuroprosthetic devices.

Many new assistive devices are available for individuals paralyzed below the neck due to spinal cord injury. Severely paralyzed individuals must be able to command their complex assistive devices using remaining activity from the neck up. Electromyographic (EMG) sensors enable people to use contractions of head and neck muscles to generate multiple proportional command signals. Electroencephalographic (EEG) signals can also be used to generate commands for assistive device control by conveying information about imagined or attempted movements. Fully-implanted wireless biopotential detection systems are now being developed to reliably detect EMGs, EEGs, or a mixture of the two from recording electrodes implanted just under the skin or scalp thus eliminating the need for externally worn hardware on the head or face. This present study shows how novel patterns of jaw muscle contractions, detected via biopotential sensors on the scalp surface or implanted just under the scalp, can be used to generate reliable discrete EMG commands, which can be differentiated from patterns generated during normal activities, such as chewing. These jaw contractions can be detected with sensors already in place to detect other muscle- or brain-based command signals thus adding to the functionality of current device control systems.

National Institute of Neurological Disorders and Stroke support for brain-machine interface technology

Brain-machine interfaces (BMIs) offer the promise of restoring communication, enabling control of assistive devices, and allowing volitional control of extremities in paralyzed individuals. Working in multidisciplinary teams, neurosurgeons can play an invaluable role in the design, development, and demonstration of novel BMI technology. At the National Institutes of Health, the National Institute of Neurological Disorders and Stroke has a long history of supporting neural engineering and prosthetics efforts including BMI, and these research opportunities continue today. The author provides a brief overview of the opportunities and programs currently available to support BMI projects.

Repetitive Task Training for Improving Functional Ability After Stroke

Repetitive Task Training for Improving Functional Ability After Stroke

Improved EEG based input concept for hands free control of assistive devices for persons with severe disability

Research and development has been conducted on a “hands-free” brain wave activity (electroencephalography-based) assistive technology device called the Mind Switch. It has been designed to provide a severely disabled person (such as someone with tetraplegia) with the ability to switch using their brain activity and thus activate devices in the environment. An area of concern has been the time it takes to complete set-up of the system before individual use, as well as the occurrence of errors in switching rates. In order to improve the user friendliness of the system, this study compared the efficacy of detecting electroencephalography (EEG) changes in brain signals that occur with eye closure and used as a “switch”, with two different methods of processing the EEG. Using data from spinal cord injured participants, fractal analysis, that is, the change in fractal dimension of the EEG wave during eye closure was compared to the original method of using spectral analysis change. In comparison to the spectral technique, the fractal dimension technique improved set up time, and was substantially improved in reducing the number of false positive and false negative errors when activating the device. Furthermore, the time to switch was also marginally reduced when using fractal dimension processing. Implications for improving the viability of an EEG based environmental control system for the severely neurologically injured are discussed.

Generation of rhythmic and voluntary patterns of mastication using Matsuoka oscillator for a humanoid chewing robot

We intend to apply Matsuoka neural oscillator into humanoid chewing robots to generate rhythmic actuation of central pattern generator (CPG) and adapt it for voluntary actuation due to sensory feedback. In this paper a single Matsuoka oscillator of two neurons is used for two phase-locked muscles (e.g. masseter and digastric muscles) or for a single robotic joint. To help design and tune the oscillator we have developed three graphical user interfaces (GUI) with aid of which the simulation, parameters’ influence and adaptation of the oscillator can be analysed and for specific pattern of muscle activities the oscillator can be selected. Discussions are made in relation to the experimentally confirmed EMG (electromyography) of muscle activities for various foods. A case study involving a jaw, driven by a couple of opening and closing muscles that are commanded by motoneurons is presented. The force of the muscles is described in nonlinear Hill model while the motoneuron for muscle activities is modelled in the oscillator. Simulations are performed to show the oscillator’s ability in generating and adapting its rhythmic outputs with respect to the chewing without food (i.e, EMG only for rhythmic muscle activities), with foods (i.e., EMG for rhythmic and additional muscle activities) and with crushable foods (to see how quickly the oscillator to reduce its force commands in order not to damage the teeth). Our work is also meaningful for brain-based control of assistive or rehabilitative devices and EMG-driven neuromusculoskeletal models.

Coordinated Control of Assistive Robotic Devices for Activities of Daily Living Tasks

Recent research in rehabilitation indicates that tasks that focus on activities of daily living (ADL) are likely to show significant increase in motor recovery after stroke. Most ADL tasks require patients to coordinate their arm and hand movements to complete these tasks. This paper presents a new control approach for robot-assisted rehabilitation of stroke patients that enables them to perform ADL by providing controlled and coordinated assistance to both arm and hand movement. The control architecture is represented in terms of a hybrid system model combining a high-level controller for decision-making and two low-level assistive controllers (arm and hand controllers) for arm and hand motion assistance. The presented controller is implemented on a test-bed and the results of this implementation are presented to demonstrate the feasibility of the proposed control architecture.

Development of a future Intelligent Sweet Home for the disabled

This article introduces a new smart house, the Intelligent Sweet Home, developed at the Korea Advanced Institute of Science and Technology (KAIST), Korea, for testing advanced concepts for independent living for elderly and physically handicapped individuals. The work focuses on human-friendly technical solutions for motion/mobility assistance, health monitoring, and advanced human–machine interfaces that provide easy control of both assistive devices and home-installed appliances. The smart house behaves in accordance with the user's commands, the user's intentions, and the user's current health status. Various interfaces based on hand gestures, voice, body movement and posture, and the health monitoring system were studied and tested. This article emphasizes the structure of the system.

An Inductive Tongue Computer Interface for Control of Computers and Assistive Devices

This letter introduces a new inductive tongue computer interface to be used by disabled people for environmental control. The interface demands little effort from the user, provides a basis for an invisible interface, and has potential to allow a large number of commands to be facilitated.

Design and implementation of an inertial measurement unit for control of artificial limbs: Application on leg orthoses

This paper presents the design and implementation of a cost-effective and small inertial measurement unit (IMU) for application on leg smart orthotics and prosthetics. IMU design based on biomechanical considerations for lower leg devices is presented. Methods for calculation of a number of biomechanical parameters related to gait based on the unit are discussed, including calibration and offset correction procedures. An approach for electronic knee joint control of orthosis during cyclic walking based on IMU signals is discussed. Finally, experiments are conducted for a subject walking on a flat surface wearing a mechanically driven orthosis with the proposed sensor. Experimental results demonstrate the IMU suitability and feasibility for real-time embedded control of wearable assistive devices for walking restoration and monitoring.

The comparison of electromyographic pattern classifications with active and passive electrodes

Electromyographic (EMG) signals are usually acquired using surface electrodes, and they commonly serve as the control sources of myoelectric prosthetic limbs. The use of passive electrodes and amplifiers with adjustable gain is very popular in laboratories for the development of new control strategies. However, active electrodes without conductive jelly are used in most clinical applications of myoelectric hand control. There remains an important question: Are there any differences between using active and passive electrodes in EMG pattern classifications? Autoregressive and cepstral coefficients were used to evaluate recognition rates via both types of electrodes. The results showed that the estimated recognition rates in the passive electrodes were comparable to those in the active ones (averaged recognition rate, 88.5 vs. 85.84% in the autoregressive coefficients, and 84.84 vs. 83.5%, in the cepstral coefficients, respectively). Aside from the lack of significant statistical differences between them, the results imply that the differences between the recognition rates via these electrodes could be negligible. This would be helpful for the myoelectric control of assistive devices.

Integrating control of multiple assistive devices: a retrospective review.

Advances in technology now allow individuals to operate multiple assistive devices through a single, integrated control. A retrospective review of clients at the Center for Applied Rehabilitation Technology (CART) was conducted to determine the factors that support the recommendation of integrated controls. A total of 290 client charts were reviewed; data from 87 clients met the study criteria and were included in the review. For each individual, information was obtained on available access sites, types of assistive technology controlled, and types of input devices used. Meetings were held with CART therapists to determine reasons for recommending specific access configurations for each individual. Twelve reasons falling into four general categories (i.e., performance, functional abilities, subjective, and external) were identified and used to code responses. The findings indicated that integrated controls may be useful in the following cases: 1) when an individual has a single, reliable access site; 2) when the optimum access method for each assistive device is the same; and 3) when the individual prefers integrated control for personal reasons (e.g., aesthetics). Integrated controls may not be appropriate in the following cases: 1) when performance on one or more assistive devices is severely compromised by integrating control; 2) when the individual wishes to operate an assistive device from a position other than from a powered wheelchair; 3) when physical, cognitive, or visual/perceptual limitations preclude integrating; 4) when it is the individual's personal preference to use separate controls; and 5) when external factors such as cost or technical limitations preclude the use of integrated controls.