The Use of Empirical Mode Decomposition-Based Algorithm and Inertial Measurement Units to Auto-Detect Daily Living Activities of Healthy Adults
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Bibliographic record
Abstract
he use of inertial measurement units (IMUs) in motion analysis for clinical purpose is relatively recent. However, the use of such system in free environment remains sparse. This is in part due the lack of robust algorithms to handle large volumes of data for performance evaluation and patient diagnosis. The present work examines the ability of using Empirical Mode Decomposition and discrete-time detection of events to automatically detect and segment tasks associated with activities of daily living (ADL) using IMUs. Seven healthy older adults (73± 4 years old) performed ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5-min). They wore a suit (Synertial-IGS180) comprised of 17-IMUs positioned strategically on body segments to capture full body motion. After a systematic process examining time series of each sensor, it was determined that 6-IMUs were sufficient to detect the 9 tasks at hand (such as walking, sit to stand, stand to sit, reaching to the ground to pick or to put down objects on the floor, step an obstacle and turning). The proposed method automatically identified the proper set of template waveforms associated to ADL tasks based on kinematic data acquired from the selected IMUs. The ground truth on timing of tasks was established by visual segmentation of recordings using the system's software. Despite the variation in the occurrences of the performed tasks (freely moving), the proposed algorithm exhibited high global accuracy under unscripted conditions of motion, for both Se. and Sp. of 97% (Nevents=1999), using a few features and without learning process. This work will eventually allow for the assessment of mobility performance within the segmented signals; specifically how well the person is moving in his/her environment.
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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