Explore edge machine learning for mobile health technologies in this tinyML Talks Sweden meetup. Delve into the essential role of machine learning in next-generation Internet of Things (IoT) systems, particularly in mobile health and wearable devices. Discover the opportunities and challenges of implementing machine learning in resource-constrained environments, focusing on real-time health abnormality detection. Learn about innovative edge machine-learning techniques designed for portable and wearable technologies with limited processing power, communication bandwidth, memory storage, and battery life. Gain insights from Amir Aminifar, Assistant Professor in the Department of Electrical and Information Technology at Lund University, as he discusses epilepsy monitoring, distributed classification, fog edge computing, real-time federated learning, and multimodal data sources in the context of TinyML applications.
Overview
Syllabus
Introduction
Sponsors
Internet of Things
Epilepsy
Epilepsy Monitoring
State of the Art
Distributed Classification
Classification Levels
Fog Edge Computing
RealTime Federated Learning
Questions
Collaboration
Arm
Multimodal
Labelling
Data Source
Edge Computing
Interrupts
Timing
Taught by
tinyML
