A Hybrid Controller with Chedoke-McMaster Stroke Assessment for Robot-Assisted Rehabilitation

AbstractAmongst the major challenges in post-stroke rehabilitation are the repetitiveness nature of rehabilitation procedure, and the accessibility of therapists for long-term treatment. In manual rehabilitation procedure, the patient is subjected to repetitive mechanical movement of the affected limb by the therapist. In one of the techniques called active-assist exercise, the subject moves his affected limb along a specified trajectory with the therapist guiding the motion. The therapist gives some assistance to the subject to complete the course if deemed necessary and the procedure repeats. The significant advantages of using robots in assisting rehabilitation are its efficiency and it is fatigue free. The robots however need to be developed to have the capability of human therapist in providing the rehabilitation more naturally. In this paper, the work focuses on developing a new framework for the robot controller system. In particular, a low-level controller, which is in the form of force controller based on impedance control theory is discussed. The controller is capable of governing the active-assist exercise through autonomous guidance during the therapeutic procedure based on the Chedoke-McMaster stroke assessment method

Surface Electromyography (sEMG)-based Thumb-tip Angle and Force Estimation Using Artificial Neural Network for Prosthetic Thumb

AbstractNormally, humans were born with five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. In this work the relationship between the electromyogram (EMG) signals and thumb tip forces are investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The classification of the EMG signals based on different force and thumb configuration is performed by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to classify the EMG signals

Controller Design Of Unicycle Mobile Robot

ABSTRACT: The ability of unicycle mobile robot to stand and move around using one wheel has attracted a lot of researchers to conduct studies about the system, particularly in the design of the system mechanisms and the control strategies. This paper reports the investigation done on the design of the controller of the unicycle mobile robot system to maintain its stability in both longitudinal and lateral directions. The controller proposed is a Linear Quadratic Controller (LQR) type which is based on the linearized model of the system. A thorough simulation studies have been carried out to find out the performance of the LQR controller. The best controller gain, K acquired through the simulation is selected to be implemented and tested in the experimental hardware. Finally, the results obtained from the experimental study are compared to the simulation results to study the controller efficacy. The analysis reveals that the proposed controller design is able to stabilize the unicycle mobile robot. ABSTRAK: Kemampuan robot satu roda untuk berdiri dan bergerak di sekitar telah menarik minat ramai penyelidik untuk mengkaji sistem robot terutamanya didalam bidang rangka mekanikal dan strategi kawalan robot. Kertas kajian ini melaporkan hasil penyelidikan ke atas strategi kawalan robot bagi memastikan sistem robot satu roda dapat distabilkan dari arah sisi dan hadapan. Strategi kawalan yang dicadang, menggunakan teknik kawalan kuadratik sejajar (Linear Quadratic Control) yang berdasarkan model robot yang telah dipermudahkan. Kajian simulasi secara terperinci telah dijalankan bagi mengkaji prestasi strategi kawalan yang dicadangkan. Dari kajian simulasi sistem robot, pemilihan faktor konstan, K yang sesuai di dalam strategi kawalan telah dibuat, agar dapat dilaksanakan ke atas sistem robot yang dibangunkan. Keputusan dari kajian simulasi dan tindak balas oleh sistem robot yang dibangunkan akhirnya dibandingkan bagi melihat kesesuaian faktor kostan, K yang dipilih. Analisa menunjukkan dengan menggunakan strategi kawalan yang dicadangkan dapat menstabilkan robot satu roda. KEYWORDS: unicycle mobile robot; nonholonomic system; LQ

Exploiting wheel slips of mobile robots to improve navigation performance

Improving navigation performance of autonomous wheeled mobile robot (WMR) in a dynamic unstructured environment requires improved maneuverability. In such cases, the dynamics of wheel slip may violate the ideal no-slip kinematic constraints generally used to model nonholonomic WMR. In this paper, a new method is proposed to tackle the modeling inadequacy that arises when slip is neglected by including both longitudinal and lateral slip dynamics into the overall dynamics of the WMR. This new model of the WMR provides a realistic simulation environment that can be utilized to develop model-based controllers to improve WMR navigation. In this paper, a dynamic planner with a path-following controller is designed to allow the WMR to navigate efficiently by autonomously regulating the generated traction force due to wheel slip. Extensive simulation results demonstrate the importance of the proposed modeling technique to capture slip phenomenon and the efficacy of the presented control technique to exploit such slip for better navigation performance

Measurement system to study the relationship between forearm EMG signals and hand grip force

Hand grip force, wrist flexion and wrist extension are the result of forearm muscle activity. In certain applications such as controlling the movements of a robotic prosthetic hand, information relating wrist joint angles to forearm muscle activity is useful to be used as part of the control algorithm. In this paper, we study the relationship between the muscular activity of forearm muscles and wrist joint angles/position while hand grip force is varied. In order to do that, an electronic circuit was constructed to amplify and filter the electromyogram (EMG) signals measured from the Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS) and Extensor Digitorum Communis (EDC). Neural networks were used to model the relationship between EMG signals and wrist joint angle data at different hand grip strength levels. The performances of the networks were indicated by the corresponding Mean Absolute Error values

Signal processing of EMG signal for continuous thumbangle estimation

Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses as a challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in total imitation. The paper looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle throughout the range of flexion and simultaneously gather the EMG signals. Further various different features are extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A ‘piecewise-discretization’ approach is used for continuous angle prediction. The most determinant features are found to be the 2nd order Auto-regressive (AR) coefficients with Simple Square Integral (SSI) giving an accuracy of 85.41% in average while the best classification method is Support Vector Machine (SVM - with Puk kernel) with an average accuracy of 86.53%

Thumb-tip Force Prediction Based on Hill’s Muscle Model using Electromyogram and Ultrasound Signal

The use of prostheses is necessary to restore lost limbs to a level of functionality to enable activity of daily living. Many prostheses are now using myoelectric based control techniques to operate. However, to develop a model based controller for the system remains a challenge as accurate model is necessary. This study investigates the use of electromyogram and ultrasound signal to predict thumb tip force based on Hill’s Muscle model. The results obtained has shown a significant improvement in the prediction of thumb tip force as much as 31.45% of average RMSE over the benchmark model that leverages on biomechanics model and active marker to characterize the muscle

Interactive robotic platform for education and language skill rehabilitation

In this paper, we present the mechanism and system design of a robot that is suitable for rehabilitation process for autistic children. Through some researches, robot seems to have the ability to improve the communication skills of the children with autism. An interactive robotic platform has been developed taking into consideration the robot appearance and features to encourage positive outcome in the rehabilitation of autism spectrum disorder (ASD) children. The interaction between the robot and the child included language skills, eye contact, imitation behavior, facial expression and movement of the robot. Here, a high-level controller is integrated to the system to help therapist monitors the children’s reactions towards the robot. In result, the developed robot has the ability to help therapist to diagnose and conclude the therapy session in a shorter period

Portable thumb training system for EMG signal measurement and analysis

Restoring human limb that is lost due to accidents or amputation because of vascular diseases with prosthesis is one challenging issue in engineering field. The loss of human body, especially the upper and lower limbs will limit the daily activities of patient in many ways. There were many studies that had been done previously in developing prostheses which were simpler in the forms than human limb. However, the performance of these units was mostly unnatural and is damped by constraint in the model of the limbs. In this paper, the development of thumb training system that allows the measurement of electromyography (EMG) signal when the thumb is flexed at different angles with varying force is discussed. The parameters are then possible to be used with conjunction of Hill’s muscle model to develop a more precise model of the thumb. The platform also features several adjustments so as to suit various size of human hands. The result from EMG measurement from the system shows that the system is able to characterize the EMG signal of thumb at different pose and force