Development and Validation of Deep Learning Models for Motion Artifact Mitigation in Wearable PPG Devices
Bibliographic record
Abstract
Wearable devices are assuming a larger role in remote healthcare, fitness tracking, and athletics. Predominantly based on photoplethysmography, these devices use light to non-invasively detect changes in blood flow and oxygenation within peripheral circulation – leading to estimates of heart rate, pulse oximetry, and the identification of arrhythmias. However, the presence of motion greatly reduces the quality and interpretability of data from wearable devices and limits the development of ML/AI models. Currently, accelerometers are used to detect excessive motion and dispose of ‘contaminated’ data. However, accelerometers capture global motion, not the relative motion at the sensor-skin interface – the predominant source of motion artifacts. Here, we propose the reconstruction of physiological waveforms in the presence of motion through the development of a deep learning model, PPG-MART, trained through a unique multimodal sensor platform. The platform consists of two synchronized multiwavelength photoplethysmography sensors, a pressure sensor that captures relative motion at the sensor interface, and a linear actuator that can apply arbitrary motion-mimicking waveforms. Systematic application of basis stimuli generate a perturbed multiwavelength data channel which is compared to a non-perturbed control dataset obtained on the opposite hand. We demonstrate the training of multiple deep learning models that reconstruct photoplethysmography waveforms based on subsets of data, down to a single PPG channel. Results show improving performance with an increasing number of wavelength channels and inclusion of a pressure channel, but that significant improvements are accessible with only a single PPG channel. Comprehensive testing on five external PPG datasets verifies the generalizability of the model and applicability to all optical devices which perform PPG. This work enables real-time motion artifact cancellation in wearable optical devices and will lead to more robust remote health care devices, athletic performance trackers, and algorithms. The training concept can also be applied to any wearable measurement modality that supports multiple, independent measurements sites of a systemic biomarker or vital sign.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".