A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications
Why this work is in the frame
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Bibliographic record
Abstract
The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6%, and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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