EMEA 2021 Student Forum: Simulating Edge AI Inference to early predict Chronic obstructive…



EMEA 2021 Student Forum
Simulating Edge AI Inference to early predict Chronic obstructive pulmonary disease from Exhaled Breath Fingerprint
Samson Otieno OOKO – Postgraduate Student, University of Rwanda

According to the World Health Organization (WHO), every year over 4 million people die prematurely from different types of respiratory diseases, The high rate of mortality results from late diagnosis mainly occurring when a patient starts to experience symptoms and go to a healthcare facility; a diagnosis requiring expensive resources such as equipment and healthcare professionals to envision regular preventive check-ups for all populations. Affordable, free to use, noninvasive early prediction solutions for home use would anticipate on-time medical consultation and therefore treatment, and at the same time increase the datasets of biomarkers required to develop improved respiratory disease prediction analytics.
Existing portable non-invasive breath analysis studies and commercial solutions integrating the Internet of Things (IoT) and Machine Learning (ML) use a cloud-centered architecture, meaning that the breath biomarkers collected by IoT sensors are sent to the cloud for inferencing. Considering bandwidth-constrained scenarios and potentially privacy-preserving requirements of healthcare services, such cloud-centered solution is not viable making an offline breath analysis solution a necessity. Our Master thesis research fits in this context and explores rapid prototyping of an edge Artificial Intelligence (AI) embedded prototype that predicts respiratory disease from exhaled breath fingerprints with objectives to (1) enable offline free to use and regular checkup in home settings and (2) collect large curated datasets to enable development of analytics achieving clinical-grade accuracy.
Up to now, we have set in place a rapid development and simulation pipeline integrating Edge Impulse, STM32IDE Cube and Proteus VSM; respectively for (1) training dataset and generating a TinyML library, (2) integrating the generated library into our application and compile an executable for our target board and finally (3) simulate the edge AI inference process in an embedded context similar to targeted board. We are currently evaluating this set-up on an open-source Chronic obstructive pulmonary disease (COPD) time-series dataset but our design process is scalable to other diseases as long as corresponding datasets are available. This simulation setup around Proteus VSM is particularly interesting to further explore the impact of non-idealities in embedded hardware as a way to design robust edge AI models. The simulated inference results on Proteus matches pretty well the one on the remote edge impulse platform with a slight variation in classification accuracy. We are currently investigating the origin of this delta.

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