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Mechatronics Engineering

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    The electromyography, electronics and sensory system for a mechatronics integrated touch hand 3.
    (2016) Mangezi, Andrew.; Stopforth, Riaan.
    This research presents the EMG, electronics and sensory system for a mechatronics integrated Touch Hand 3. This unique myoelectric hand was driven by EMG signals captured at the surface of the skin in order to achieve a more robust grasp. The research consisted of different configurations and types of electrodes to be used for an EMG device, with the research analysis of the different candidates to control the Touch Hand 3. In addition, the EMG experimental results to compare contact and non-contact electrodes were carried out to find a correlation between the EMG electrodes and an antenna. These results determined the number of layers that the EMG sensor will need to obtain the best reading in a patch-yagi antenna. Stick-on electrodes were used to monitor two muscle groups in the arm at the same time. Contact and non-contact electrode tests were conducted, which used a combination of the embroidery electrodes and the stick-on electrodes. The flexion-extension muscles were tested in the experiments, by letting each volunteer lift a 2.5 kg weight. The electronics and sensor system had to be researched, designed, developed and optimized to allow for a successful integration of the Touch Hand 3. The artificial arm is fitted with palpable sensors to read the object temperature, force, and vibration. A number of constraints were considered in designing the system including the modularity of the system, cost, weight, and its grip strength in comparison with the Touch Hand 2. The modular electrical system was designed to accommodate full control and integration with the mechanical system to form a myoelectric mechatronics prosthetic system to be used by the amputees more effectively in a quicker response.
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    Design and development of a low-cost high-performance vehicle mounted UHF RFID system for tracking goods and inventory.
    (2019) Hoskins, Gareth Oregan.; Bright, Glen.; Padayachee, Jared.
    Abstract available in the PDF.
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    System integration of a remotely accessible satellite tracker.
    (2022) Kisoon, Rushil.; Stopforth, Riaan.
    The recent rise in space missions and satellite launches has piqued the interest of many. As a result, the growing number of objects in orbit has led to an increase in space monitoring. While large-scale tracking systems exist, many enthusiasts utilise rudimentary methods for satellite monitoring. Some have created do-it-yourself rotators or radio reception stations. Subsequently, a remotely accessible system was required to track satellites and receive transmissions. A small-scale system was designed to fulfil the requirements. Aluminium extrusions were used to create the frame, while microprocessors, sensors and other components were integrated to complete the rotator assembly. A circuit layout was drawn up to coordinate the flow of power and data between components. Low-powered electronics allow the system to run off-grid via a 12 V battery. Many features of the Raspberry Pi lent well to the required application, such as wireless network connectivity and a remote access service. Gpredict, an advanced satellite tracking software, was used to calculate the coordinates for rotation. The RTL-SDR, a software-controlled tuner, provided control to receive radio frequencies. Features such as a slow scan image decoder and frequency identification toolkit were also integrated. The user interface program called TrackGUI was coded, which allows easy access to tracking features. A login system was created with ‘admin’ and ‘user’ access levels. Initially, the presence of GlobalProtect on the UKZN network posed an issue, but methods were implemented to solve this problem. Various aspects of the system were tested and iterated until they met the required parameters. Test results showed that the system could operate within 5 degrees of the intended target. Radio transmissions and slow-scan images could be received and recorded. Therefore, the aim of integrating hardware and software components to create a remotely accessible satellite tracking system was achieved.
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    Integration of a wearable IoT-based smart healthcare system with predictive modelling.
    (2021) Govender, Pragesh Ashley.; Stopforth, Riaan.; Sivarasu, S.; Markus, E.; Hands, Clive.
    Abstract: The usage of the Internet of things (IoT) equipped with intelligent machine learning (ML) or fuzzy logic (FL) systems in the medical sector is becoming a widespread normality. This following the implications of the 4th and 5th industrial revolutions (5IRs) aiming to integrate technology with the way we live and work. Many research areas have focused on incorporating individual elements of an intelligent IoT based system. However, none have holistically looked at encompassing a multi-faceted sensory device with an IoT framework backed by both ML and FL capabilities for unique monitoring and diagnostics. This implies that there is an opportunity to create such a product targeted for the medical sector. The developed system of systems (SoS) discussed therefore aims at eliminating the barrier to patient care outside of a hospital setting through the use of real time patient diagnostics. The study was approached by first establishing a streamlined IoT infrastructure. A multi-faceted Arduino based sensory device was then developed to collect user parameters. The electronics were housed in a common of the shelf (COTS) casing satisfying the criteria of wearability. Bluetooth connectivity then allowed for the transmission of sensory data from the WBAN to the IoT smartphone gateway. Sensory parameters being measured were electrocardiography (ECG), electromyography (EMG), body temperature (BT), infrared (IR) output, pulse rate (PR) as well as environmental humidity readings. Blood sugar (BS) levels were obtained non-invasively by calibrating the IR output voltage with that of a calibrated glucometer to obtain a straight line equation representing the relationship between the sensor ADC output and glucose in the blood. A Java based mobile application (MA) was then developed, which allowed for the processing of the sensory data before storage in a local SQLite database. A two interface MA for use by registered doctors and patients was necessary to allow for data sharing and security. Registration and login for the MA was done through a NoSQL Firebase database. A JDBC was established to enable the transmission of data to a MS Azure Structured Query Language (SQL) database. This served as the cloud interface within the IoT network layer. The MA is able to integrate with a phone’s Global Positioning System (GPS) to allow for simultaneous tracking of patients. A return application programming interface (API) connection between MS Azure and the developed application layers was then created. Here the mobile doctor interface as well as a developed node.js based web application (WA) served as the mediums through which health practitioners could access patient data. ML models were developed using the MS Azure ML Classic Studio suite which allowed for a real time analysis of received data through deployment and subsequent consumption of data using POST Requests via the MA and a Java software development kit (SDK). An accuracy of 92% was achieved for the stroke prediction model based on the boosted decision tree algorithm. Various ML models were analyzed to ensure that a high precision was obtained while preventing overfitting. Additional FL models were also developed, which took into consideration unique sets of vitals combinations to create a rule base depicting the patient health status and health risk. v This data was compared to the intuition of a doctor and received an 87% accuracy for model 1 which took into consideration a patient’s PR, BS, BT and age to predict the health status of a patient. The model was then compared to the Modified Early Warning Score (MEWS) rating, a popular measure of health risk utilized in the medical field. The results of comparison also yielded an accuracy of 87%. A second FL model was then developed looking at the effect of environmental conditions on the risk rating of patients. Here input variables of BT, age and humidity readings were used to determine their effect the on the risk rating of a patient. This model scored an 80% accuracy when compared to the expertise of a physician. Both models were programmed onto the MA to predict the patient’s health status. Both models were also re-developed on MATLAB software to simulate the effect of various input variables on the response variable. Overall the designed system was able to possess around 15 of the typical features found in smart wearable systems which far exceeded the features of those devices it was compared to. The designed system satisfied the requirement of a feature rich experience while also satisfying the criteria of cost effectiveness.