Muscle to Machine: Surface Electromyography for a Robot ControlDuring muscle activation, the surface Electromyography, sEMG, electrical signal is produced from small electrical currents generated by the exchange of ions across the muscle membranes and detected by electrodes. During a muscular activity, the brain sends excitation signals through the nervous system to a group of motor units which are the junction points between the neuron and the muscle fibers. As a result, each motor unit produces a ‘Motor Unit Action Potential’ (MUAP). This process is, continuously, repeated as long as the muscle is required to generate a force, producing a train of action potentials. The trains from concurrently active motor units superimpose to produce the resultant EMG signal. A group of muscles are involved in a certain movement of the human body. For a specific activity, there is a direct proportionality between the number of muscles, force, excitation from the nervous system, number of motor units, and firing rate. The bioelectric EMG signal has a wide range of applications such as a diagnostic and evaluation tool for neurological disorders, low back pain, physiotherapy, rehabilitation, sports, biofeedback and ergonomics research. Recently, EMG has found its use in the robotics field. A robotic mechanism can be effectively controlled by an EMG signal. The advances in electronics and microcontroller technology such as filtration, rectification, and amplification, improved the control options for robotic mechanisms. In this sense, we propose a design and implementation of an EMG data acquisition system with the Myoware device and a microcontroller. This thesis discusses, in detail, the effective use of sEMG as a tool for controlling a robotic hand. A detailed elaboration of the electrode types, signal acquisition technique, electronics circuit design considerations and the control procedure to drive electric motors in a robotic hand is provided. The MATLAB is used to analyze the acquired non-invasive signal.
Machine Learning based Theft Detection by Processing the Smart Meter DataThe intentional and illegal use of electricity by various means is referred to as energy theft. Several studies have been conducted using machine learning methods to detect energy theft in advanced metering infrastructure. However, there is a problem with using machine learning for energy theft detection in that it is difficult to obtain enough electricity theft data to train a machine learning model. In this paper, we propose a method for detecting electricity theft in data streams generated by smart meters that are based on anomaly pattern detection. To train the model, the proposed method requires only normal energy consumption data. Previous usage records of customers being monitored are not required for detecting energy theft. This feature makes the proposed method applicable in real-world situations. The significance of the present study relies on collecting and analyzing existing papers to find the exact energy usage records for each customer, develop an algorithm to helps reduce theft detection, implementing a machine-learning algorithm to identify the type of electricity theft behaviors and their properties, and compare between different methods used for theft detection. The significance of the project is that power consumption increases each year, the power generation and distribution industry grow, and the need for technologies to reduce power loss is increasing. Energy theft refers to the intentional and illegal usage of electricity by various means. Therefore, a smart meter is installed in a customer-filled area. Making it nearly difficult for unauthorized individuals to tamper with it. Moreover, to follow the SDG (Sustainable Development Goals) goals the 12th and 16th goals. The 12th goal states, “Responsible Consumption and Production”, and the 16th goal states “Peace, Justice, and Strong Institutions”. Also, according to vision 2030, Prince Mohammad bin Salman stated that the industry will grow, and the power will reduce. The suggested methodology is machine learning and processing the smart meter data. Experiments were carried out using real smart meter data and artificial attack data, including the standardization of daily consumption vectors, the construction of an outlier detection model on normal electricity consumption data of randomly selected customers, and the application of anomaly pattern detection on test data streams.