Migrating Intelligence from Cloud to Ultra-Edge Smart IoT Sensor Based on Deep Learning: An Arrhythmia Monitoring Use-Case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditionally, the Internet of Things (IoT) devices, deployed on the ultra-edge of the network, lack computation, and energy resources. In this paper, we press on the need to go beyond the realms of traditional edge computing (e.g., limited to user-smartphones) and investigate how to incorporate intelligence into the ultra-edge IoT sensors. Among numerous use-cases, we select a mobile Health (mHealth) scenario where we conceptualize a smart IoT sensor to collect and intelligently process single-channel Electrocardiogram (ECG) signals to detect arrhythmia, a heart-condition often associated with morbidity and even mortality. The arrhythmia detection can be regarded as a non-linear Delay Differential Equation (DDE) time-series analysis problem, and the conventional solutions to this problem are not suitable for integration with IoT sensors due to rigorous pre-processing steps. As a solution, a Convolutional Neural Network (CNN)-based, lightweight Arrhythmia classification system is proposed in the paper without the need for noise-filtering and feature extraction steps. Four classes of the heartbeats are considered to comply with the ANSI/AAMI EC57:1998 standard. The proposed system's performances and generalization potential are assessed using three datasets from PhysioNet trained on a deep learning workstation and then transferred to virtualized micro-controllers connected to IoT sensors. The proposed deep learning model exhibits encouraging performance (accuracy 95.27%) in heartbeat classification. Experimental and numerical results demonstrate that the proposed deep learning technique outperforms conventional DDE-based optimization techniques and machine learning techniques such as K-Nearest Neighbor (KNN), and random forest (RF).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it