When: Mon Nov 18th, 11a
Where: Rose St Building Seminar Area
Speaker: Rami Khushaba, Building IQ & UTS
Title: Improved Electromyogram (EMG) Pattern Recognition for Multifunction Prosthesis Control
Abstract: Myoelectric control employs pattern recognition (PR) systems decipher the content of the Electromyogram (EMG) signals from the remaining muscles in the amputees stump to recover lost functionality by controlling powered prosthetics. Limb prostheses are essential for maintaining personal independence and supporting effective inclusion in society. However, due to their poor control, imposed by the limited accuracy of hand movement recognition in clinical settings, the EMG-driven prostheses are not widely acceptable. This is attributed to the big gap between systems developed in labs using ideal settings on intact-limbed subjects and those that are suitable for online recognition on amputees. Such a gap is imposed by factors like the lack of intuitive control, poor system reliability and the lack of robustness against practical problems like limb position change, electrodes shift, varying force levels, and EMG signal non-stationarity. Previous research has shown that the success of the EMG PR systems mainly depends on the quality of the extracted features, as they are of direct impact on clinical acceptance. A huge effort has been made by several research groups to bridge the gap between the lab settings and clinical implementations. Nonetheless, despite much advancement, there are still considerable challenges in applying research outcomes to a clinically viable implementation. A number of researchers suggested that EMG alone might be inadequate for reliable control and multi-modal sensory data is needed to complement the EMG features, e.g., accelerometers, gyroscopes, magnetometers, near-infrared spectroscopy and ultrasound images. An alternative approach considers different feature extraction methods like deep neural networks mixed with high-density (HD) EMGs. In this presentation, we shed the light on some of the latest developments in the field and how the performance of properly engineered temporal- spatial feature extraction algorithms can approximate that of deep learning methods at a much less computational cost while maintaining the accuracy.
Bio: Dr. Rami Khushaba, received his PhD degree in human robot interaction, with a specific focus on signal processing and machine learning for controlling powered prosthetics for amputees (UTS, 2010). A major goal of his research is to develop clinically realizable and robust myoelectric control systems that can be made available to persons with limb loss. He has a significant number of publications/contributions in the field of myoelectric control. He previously held several positions, including lecturing and a postdoctoral fellow at UTS, working in several projects including, exoskeletons and prosthesis control, driver drowsiness/ fatigue detection, and consumer neuroscience and Neuromarketing research. He joined ResMed, the Australian/international leader in medical devices for sleep disordered breathing detection and treatment, for nearly 5 years working on algorithmic interventions for noncontact Doppler radar detection of SDB, heart failure and COPD symptoms deteriorations (with publication in prestigious journals and 3 patents). He’s been with BuildingIQ since 2017, and is currently leading the data science team, with a focus on HVAC energy optimization and machine learning control. He recently patented a new causal inference engine and pioneered its use for fault detection and tracking in HVAC systems with up to 20,000 IoT sensors readings per building.