A Real-Time IoT System and ML algorithms: A Comparative Study
Bibliographic record
Abstract
Wearable sensors are frequently used for monitoring physical activities and medical conditions. A variety of sensors are used, such as the accelerometer (ACC), gyroscope (GYR), and magnetometer (MAG), which are often embedded in Inertial Measurement Units (IMU). Data collected from these sensors can be used to identify context or physical activities through context-aware learning methods that apply a variety of learning algorithms. Implementing a real-time system (RTS) that serves a specific application like fall detection or heart condition, for example, is challenging as response time must be within a certain interval. This response time depends on the speed of data collection and transmission as well as the prediction time. Even though a lot of research was done in this area, to the best of our knowledge there is no comparative study based on the criteria we are using here. In this paper, we propose an Edge-based RTS for health-related applications and conduct a comparative study of several Machine Learning Algorithms (MLA) according to four criteria: source of data, sampling period, prediction time (PT), and success rates (SR). Even though MLA demonstrate different behavior toward these criteria, our simulation results showed that it is possible to implement a RTS that can identify or predict accurately physical activities within acceptable time constraints which are application dependant. Simulations were performed on wearable sensors’ data that we collected from 24 participants practicing five different physical activities. Our simulation results showed that the Decision Tree algorithm, with SR of 97.93% and PT of 0.17 seconds, outperformed all other algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".