Edge AI

Embedded Machine Intelligence Lab
Last updated on Feb 20, 2024

The stringent constrained resources available on tiny sensor nodes introduce a number of challenges regarding accuracy, power-efficiency, user-comfort, and security. These design considerations, however, often impose conflicting requirements. Thus, a comprehensive research approach to design future medical embedded systems and corresponding optimizations at different levels must consider these interdependent and conflicting requirements. We research methods of optimizing medical embedded systems for power-efficiency while taking into account other performance metrics. The goal is to develop tools, methodologies, and algorithms towards comprehensive approaches to address cross-layer optimization issues related to power, performance, user-comfort, and security.

Embedded Machine Intelligence Lab
Embedded Machine Intelligence Lab
Research Lab

The current focus of our research in the Embedded Machine Intelligence Lab (EMIL) is on design, development, and validation of algorithms, tools, and technologies that enhance utilization and large-scale adoption of digital health systems. To validate and refine the new technology, we actively collaborative with domain experts, community stakeholders, and end-users. This end-to-end approach results in innovative, evidence-based and cost-conscious health solutions for individuals and care providers.

Publications

Minimum-Cost Channel Selection in Wearables
We introduce a framework for the channel selection problem, mathematically formulate the minimum-cost channel selection (MCCS), and propose two novel algorithms to solve the MCCS problem.
IEEE-EMBS International Conference on Body Sensor Networks: NextGen Health: Sensor Innovation, AI, and Social Responsibility (BSN'24)
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Minimum-Cost Channel Selection in Wearables
Efficient Sensing and Classification for Extended Battery Life
We propose a resource-efficient, real-time human activity recognition framework, transforming the multi-class classification problem into a hierarchical model based on the Metabolic Equivalent of Task (MET), creating a personalized structure for each individual.
In Activity Recognition and Prediction for Smart IoT Environments, Springer
Project
Efficient Sensing and Classification for Extended Battery Life
On-Device Machine Learning for Diagnosis of Parkinson's Disease from Hand Drawn Artifacts
CNN model developed, quantized, and deployed on Android platform to screen PD patients from hand-drawn spirals.
IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN’22)
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On-Device Machine Learning for Diagnosis of Parkinson's Disease from Hand Drawn Artifacts
Probabilistic Cascading Classifier for Energy-Efficient Activity Monitoring in Wearables
A cascading approach for optimizing energy and memory requirements of a machine learning model running on an embedded device.
IEEE Sensors Journal, 2022.
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Probabilistic Cascading Classifier for Energy-Efficient Activity Monitoring in Wearables
Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems
International Joint Conference on Artificial Intelligence (IJCAI), 2020
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Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems
Improved Knowledge Distillation via Teacher Assistant
AAAI Conference on Artificial Intelligence (AAAI), 2020
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Improved Knowledge Distillation via Teacher Assistant
Resource-Efficient Computing in Wearable Systems
IEEE Workshop on Smart Service Systems (SmartSys) in Conjunction with IEEE SmartComp 2019, June, 12, 2019, Washington D.C., USA
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Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification
The 16th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 19), Chicago, IL, USA
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Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition
IEEE Transactions on Mobile Computing (TMC)
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Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition
Trading Off Power Consumption and Prediction Performance in Wearable Motion Sensors: An Optimal and Real-Time Approach
ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 23, no. 5, article 67, October 2018
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Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition
IEEE/ACM Design, Automation and Test in Europe (DATE), March 27-31, 2017, Lausanne, Switzerland
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Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition
A Hardware-Assisted Energy-Efficient Processing Model for Activity Recognition Using Wearables
ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 21, no. 4, pp.1-27, 2016
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CyHOP: A generic framework for real-time power-performance optimization in networked wearable motion sensors
International Conference on Computer Design (ICCD), 2016
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CyHOP: A generic framework for real-time power-performance optimization in networked wearable motion sensors
Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection
IEEE Transactions on Mobile Computing (TMC), 14(4), pp.800-812, 2016
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