Ted Clancy
Professor
The proliferation of advanced electronics and signal analysis methods has created new opportunities in healthcare. ECE faculty are innovating at the forefront of medical technologies with the goal of improving the quality of human life. Specific applications include: design of custom medical devices and wearable sensors to enable real-time monitoring of vital sensory information, medical imaging (including optimization methods for MRI), physiologic signal analysis, electromagnetic propagation within and about the body, on-body communication networking (“body area networks”), and smart prosthetics.
Lab for Sensory and Physiological Signal Processing
My research interests are in signal processing, modeling and instrumentation, prbeenincipally as applied to biomedical engineering. My major area of specialization has developing techniques for improving estimates of the amplitude of the surface electromyogram (EMG). EMG, the electrical activity of skeletal muscle, can be described mathematically as a random (stochastic) process which is amplitude modulated. When muscular effort is low, the amplitude of EMG is low; when muscular effort is high, the amplitude of EMG is high. Thus, better estimates of EMG amplitude improve the ability to determine the activation level of muscles. Applications of this technology include myoelectrically-controlled powered prosthesis, analysis of gait, non-invasive estimation of torques about a joint and ergonomics. I have also been involved in needle EMG decomposition in clinical and scientific studies; as well as high-resolution surface EMG, in which arrays of tightly-spaced, small electrodes are placed on the skin surface and used to detect the activity of individual motor units.
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Ulkuhan Guler
Professor
Integrated Circuits and Systems Lab (ICAS)
Our research program explores the designs of a range of biomedical devices from implantable devices to wearable devices that ensure device security, personal privacy, accurate bio-sensing, and reliable operation and proposes possible directions of study that tackle the fundamental challenges including
i) Sustainable Energy Harvesting Systems for Continuous Long-Term Health Monitoring: how sustainable energy harvesting and its efficient storage and usage are possible for continuous long-term personal health monitoring,
ii) Secure Bio-implants and Wearables: how the security of all these sensors associated with smart healthcare will be assured in terms of maintaining proper functionality of devices and protecting private information,
iii) Wireless, Sensor Interfaces for Medical and General Purpose IoTs: how accurate and reliable sensing interfaces will be able to receive very low-amplitude signals coming from various environments, such as inside the body.
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Xinming Huang
Professor
Embedded Computing Lab
Our research group is focused on the computer vision and deep learning algorithms, efficient hardware architecture designs, and their applications to autonomous driving, robotics, and embedded systems. Our research works have been published regularly in top venues such as CVPR, ICCV, ICRA, IROS, RAL, TPAMI, TNNLS, TMM, TCAS1, etc. Our mission is to build integrate circuits for artificial intelligence.
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Reinhold Ludwig
Professor
Center for Imaging and Sensing
To assist our industrial partners in their quality assurance and imaging requirements. It focuses on inspection and imaging methodologies, fundamental sensor and instrumentation research, and turn-key prototype system development.
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Sergey Makarov
Professor
Center for Bioelectromagnetic Modeling & Design
Our research (jointly with Mass General Hospital, A. A. Martinos Ctr. for Biomed. Imaging) is in bioelectromagnetics of the brain in application to neurostimulation and neurophysiological recordings. It includes both computational modeling at mesoscale and multiscale, and experiment.
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Bringing Awareness through Systems for Humans (BASH) Lab
The Bringing Awareness through Systems for Humans Lab (BASH Lab) focuses on understanding and enhancing the usability, intelligence, and processing capabilities of tiny low-power edge computing devices to realize their full potential in our daily lives.
We aim to develop a new set of artificially intelligent edge computers that provide sustainable and scalable sensing solutions in various application domains ranging from health wearable to environment monitoring.
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