tinyML Talks: On-sensor TinyML implementation: Advancing Neuroscience through Wearable Devices
On-sensor TinyML implementation: Advancing Neuroscience through Wearable Devices”
Muhammad Awais Bin Altaf
Assistant Professor
Lahore University of Management Sciences
Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. Today, neurology faces multiple challenges in the field of diagnostic and management modalities. This ranges from simple issues like the identification of healthy sleep patterns to more complicated issues like early detection and reduction in the duration of rehabilitation of acute ischemic stroke diagnosis of rare subtypes of epilepsy. The increasing availability and progress of analytical techniques are opening new doors in health care. Machine learning (ML), neural networks, and other AI tools are used to classify the patient’s electroencephalogram (EEG) data to help neurologists in making an early diagnosis and improve care. Hence, the development of ultra-low-power System-on-Chip (SoC) for the next generation of neuro-wearables, in the realms of detecting, diagnosing, and even preventing irreversible outcomes due to neurological disorders is essential. The uptake in the use of neuro-wearable technology by both patients and clinicians will have a huge impact on the future of healthcare. This talk will cover the design strategies of power-efficient (1mW) ML-based patient-specific biomedical processors. I will first explore the challenges, limitations, and potential pitfalls in on-sensor processor design, and strategies to overcome such issues. Moreover, I will describe on-chip energy-efficient digital processing techniques for the implementation of ML algorithms for disease detection focusing on the negative emotion outburst in the early detection of Autistic patients. Finally, I will conclude my talk with future research directions to design the next generation of wearable healthcare systems.
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