Shahrul Na'im Sidek

Mapping of EMG signal to hand grip force at varying wrist angles

Limb loss is a growing problem in Malaysia and the rest of the world due to the increasing number of industrial accidents, diseases and armed conflicts. After a tragic incident resulting in an amputation or paralysis, the disabled individual needs to be assisted with all possible technological means to improve his quality of life. A cybernetic prosthesis is a device which can greatly assist individuals with hand disabilities by enabling them to have some of the hand capabilities of an able bodied individual. The central nervous system which consists of the brain and spine governs hand grip force and hand movement in the human body by spatial and temporal motor unit recruitments. Electromyogram (EMG) is an electrical biological signal that can be measured from the skin surface and consists of the summation of Motor Unit Action Potentials (MUAP). Hand grip strength, wrist extension and wrist flexion are hand functions which result from the forearm muscle activity and are used in a wide range of daily tasks. Extracting hand grip force and wrist angle information from forearm EMG signals is useful to be used as inputs for the control of cybernetic prostheses. By establishing the relationship between forearm EMG and hand grip force/wrist angles, the prosthetic hand can be controlled in a manner that is customized to an amputees intent. In this research work, a myoelectric interface which consists of an electronic conditioning circuit to measure EMG signals and the software to record and process the EMG signals was developed. Experimental training and testing data sets from five subjects were collected to investigate the relationship between forearm EMG, hand grip force and wrist angle simultaneously.

Dynamic modeling and verification of unicycle mobile robot system

Unicycle mobile robot main advantages over multi-wheeled mobile robot are higher degree of mobility and less space as it only has one wheel to move. The system developed consists of two parts which are the lower and the upper parts. The lower part is composed of a wheel which is moving back and forth to stabilize the pitch angle. Meanwhile, the upper part consists of a reaction disc and the main frame that functions to stabilize the roll angle of the unicycle system. The dynamic model of the unicycle mobile robot is developed using Lagrangian method and verified through simulation using MATLAB software. The results show that the dynamic model developed can be used to design a model-based controller.

Development of EMG circuit to study the relationship between flexor digitorum superficialis muscle activity and hand grip strength

Hand grip strength plays a vital role in performing basic daily tasks such as holding an object. These tasks require a lot of effort from the muscles in the forearm. In this paper, we study the relationship between the muscular effort of the flexor muscles in the forearm and hand grip strength. In order to do that, an electronic circuit was constructed to amplify and filter the electromyogram (EMG) signals measured from the Flexor Digitorum Superficialis (FDS) muscle. The EMG signals measured from the FDS are used to study the relationship between muscular effort of the flexor muscles in the forearm and hand grip strength. EMG signals were measured from the subject while he applied minimum, intermediate and maximum hand grips on a hand gripper. The results show that EMG frequency from the FDS increase with increased hand grip strength. This information relating EMG from flexor muscles to hand grip strength is useful to be used in hand rehabilitation devices to estimate suitable resistance to be provided to patients during rehabilitation routines. Each stage of the circuit development is described in detail so that this experiment can be easily reproduced by others.

Design of intelligent multifinger gripper for a robotic arm using a DSP-based fuzzy controller

The design and modeling of a robotic arm gripper that has elements of intelligent decision making while grasping object has been previously discussed. This new system is different in that it uses an appropriate controlling scheme so that the correct force is applied to pick an object without dropping or crushing it. This is achieved by controlling the shear stresses at the interface material between finger-ends and the object using smart sensors and an intelligent controller. A slip sensor that is based on the operation of an optical encoder is used to monitor the slip rate as a result of insufficient force being applied to pick an object. A two-stage control scheme is suggested for the implementation of this system. First a limit switch is used to control the positioning of the fingers thereby solving the problem of uncertainty in the location and orientation of the object. Then, to ensure that an appropriate force is used in picking up an object a fuzzy logic controller is used. The use of a TMS320C24XX series DSP controller to implement the control strategy provides the flexibility needed in altering the control code and the prototype can be tested at low cost.

Neuro-Based Thumb-Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control

Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can 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. The movement of the prosthetic device can be naturally controlled by using electromyogram (EMG) signals. In this work, the EMG signals from the human muscles were measured in different thumb configurations and thumb-tip forces in flexion movement. The muscles involved are the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). The classification of the EMG signals based on different force and thumb configurations is performed using an Artificial Neural Network (ANN). From a series of experiments, the results show that the neural network efficiently classified the signals and a unique set of EMG signals was generated for each thumb movement and force. Therefore, EMG signals were used to control the prosthetic movement with aid from the developed neural network.

Affective State Classification Using Bayesian Classifier

This paper elaborates the basic structure of a machine learning system in classifying affective state. There are several techniques in classifying the states depending on the type of input-output dataset. A proper selection of techniques is crucial in determining the success rate of the system prediction. The paper proposes a machine learning technique in classifying affective states of human subjects by using Bayesian Network (BN). A structured experimental setup is designed to induce the affective states of the subjects by using a set of audiovisual stimulants. The affective states under study are happy, sad, and nervous. Preliminary results demonstrate the ability of the BN to predict human affective state with 86% accuracy.

Non-invasive non-contact based affective state identification

This paper discusses a study on detecting affective states of human subjects from their bodys electromagnetic (EM) wave. In particular, the affective states under investigation are happy, nervous, and sad which play important roles in Human-Robot Interaction (HRI) applications. A structured experimental setup was designed to invoke the desired affective states. These states are induced by exposing the subject to a specific set of audiovisual stimulations upon which the EM waves are captured from ten different regions of the subjects body by using a handheld device called Resonant Field Imaging (RFI™). Nine subjects are randomly chosen and the collected data are then preprocessed and trained by Bayesian Network (BN) to map the EM wave to the corresponding affective states. Preliminary results demonstrate the ability of the BN to predict human affective state with 80.6% precision, and 90% accuracy.

Design of intelligent braking system

It is anticipated that a variety of cars with diversified features that include anti-lock braking system (ABS), traction control system (TCS) antiskid steering, collision warning system (CWS) will be more commercially produced to satisfy the consumer needs in the near future. This is parallel to the trend of current technology of manufacturing smart cars and the desires of people who always want to have comfortable and safe ride in their vehicles. Moreover this type of vehicles can fit much better into the intelligent highway that Malaysian government is planning to have in the near future. Consequently, there is a need to modify the current conventional braking system so as to make it work automatically. This paper considers the use of an intelligent controller to achieve the above objective. To ensure high speed of system response, a DSP controller TMS320C24x with an embedded fuzzy algorithm is used in the implementation of this new device. Results of simulation studies using MATLAB have demonstrated the feasibility of this new system under investigation.

Performance evaluation of the deconvolution techniques used in analyzing multicomponent transient signals

Deconvolution is an important preprocessing procedure often needed in the spectral analysis of transient exponentially decaying signals. Three deconvolution techniques are studied and applied to the problem of estimating the parameters of multiexponential signals observed in noise. Both the conventional and optimal compensated inverse filtering approaches produce data which are further analyzed by SVD-based autoregressive moving average (ARMA) modeling techniques. The third procedure is based on homomorphic filtering and it is implemented by the fast Fourier transform (FFT) technique. A comparative study of the performance of the above deconvolution techniques in analyzing multicomponent exponential signals with varied signal-to-noise ratio (SNR) is examined. The results of simulation studies show that the homomorphic deconvolution technique is most computationally efficient, however, it produces inaccurate estimates of signal parameters even at high SNR, especially with closely related exponents. Simulation results show that the optimal compensation deconvolution technique is indeed a generalized form of the conventional inverse filtering and has the potential of producing accurate estimates of signal parameters from a substantial wide range of SNR data.

Emotion Embodiment in Robot-Assisted Rehabilitation System Using Hybrid Automata

The embodiment of emotions in the paper is structured under hybrid automata framework. In particular, the paper focuses on the description of the automata model designed for robot-assisted rehabilitation system in term of its initialization value, modes, condition for each mode, guard conditions, and transition between modes. A structured experimental setup was designed to evaluate the performance of the hybrid automata proposed. The result demonstrates the efficacy of hybrid automata approach in the rehabilitation application where emotion of the subject is taken into consideration in deploying suitable rehabilitation tasks.