University of Technology Sydney. Faculty of Engineering and Information Technology.Tremor is a neurological disorder characterized by involuntary oscillations and poses a functional problem to a large number of patients. The most preferred description for tremor is an involuntary movement that is an approximately rhythmic and roughly sinusoidal movement. It results from a neurological disorder which can affect many daily activities. People who are affected by Parkinson’s diseases (PD) have tremor on their upper limb especially in the forearm and hand. Difficulties associated with tremor in patients with PD have motivated the researchers to work on developing various methods for tremor suppression. There is some medical and non-medical treatment for tremor reduction. Despite the considerable experience in tremor management, current treatment based on drugs or surgery does not achieve an effective reduction in 25 % of patients.
For such case, many researchers concentrated on finding non-medical treatments for tremor diminution. Active force control (AFC) or active vibration control (AVC) is one of the most famous non-medical methods for tremor suppression and control. Active vibration control is an alternate way to control and attenuate the vibration. In this method a counter force which is equal to the original vibration force but in the opposite direction is applied to the vibrating structure to supress the vibration. Finally, the vibration of structures will be stopped as two opposite forces cancel each other. The AFC method for tremor attenuation in the human forearm and hand is considered in this thesis. An AFC system is proposed which has a piezoelectric actuator and a classic proportional-derivative (PD) controller. Tremor behavior is investigated in three different models. The first model is a four degrees-of-freedom (4-DOF) biodynamic model of the human hand, which is the combination of mass-spring-damper system. The second model is a one degree-of-freedom (1-DOF) musculoskeletal model of the elbow joint with two links and one joint which includes two muscles, biceps, and triceps as the flexor and the extensor of the elbow joint. The third model is a three degrees-of-freedom (3-DOF) musculoskeletal model including wrist flexion-extension (FE), radial-ulnar deviation (RUD), and pronation supination (PS). The musculoskeletal model contains four muscles; extensor carpi radialis longus, extensor carpi ulnaris, flexor carpi ulnaris and flexor carpi radialis. First, simulation of the tremor generation in the model is performed and then the performance of the AFC system for suppressing tremor is investigated in all three models. A single piezoelectric actuator is embedded in the AFC system for controlling the behavior of the PD controller. MATLAB Simulink is used to analyze the model. Results show that the proposed AFC-based system with a piezoelectric actuator and a PD controller is very effective in suppressing the tremor for the three models.
Using piezoelectric material as a smart material for AVC has been of interest to many researchers. Human forearm is modelled as a continuous beam which has a layer of piezoelectric actuator on its top surface and tremor is controlled actively. Also using piezoelectric material as an actuator and sensor in the closed loop control system has a very significant effect on tremor suppression. A closed loop active control system is developed and the human forearm is modeled as a beam which has two layers of piezoelectric on its top and bottom surface as an actuator and sensor respectively. Tremor behaviour of the model is studied in this control system to identify the effect of piezoelectric material on tremor suppression. Hamilton principle is used to obtain the equation of motion of both beam model. Thus, by employing the Galerkin procedure, the governing equation of motion which is a second order ordinary differential equation in time is derived. An external force as a tremor disturbance is applied to the beam by a sinusoidal force to analyse the beam behaviour. The response of the system to the force stimulation gives the analytical relations for natural frequency and amplitude of the vibration. Using the obtained analytical relations, the effects of different factors and piezoelectric properties on the vibration of this beam are examined. The results indicate that the piezoelectric layer as an actuator provides an effective tool for active control of vibration. Also using a piezoelectric layer as a sensor and actuator on the closed loop control system has a significant effect on tremor suppression.
Tremor characteristics help researchers to find the best treatment for its suppression. The frequency and amplitude of tremor have different level in patients. Some patients experience a severe kind of tremor compared to others who have weak tremor. As a result, tremor classification for PD subjects provides beneficial information for the researchers.
Human hand tremor is recorded using electromyography (EMG) from 14 patients who are affected by Parkinson’s disease. Different signal-processing including filtering and data segmentation, feature extraction, feature reduction and classification are applied to the raw EMG signal to get accurate and useful information from recorded signals. Extreme learning machine (ELM) was used to classify data into three different classifications; severe, moderate and weak. The results illustrate that the proposed system which consists of the PCA, ELM and the majority vote is successful to recognising the tremor severity in three different classes; weak, moderate and severe
