Discrete Gesture Recognition Using Multimodal PPG, IMU, and Single-Channel EMG Recorded at the Wrist
Bibliographic record
Abstract
Discrete hand-gesture recognition using sensors built into wrist-wearable devices could enable always-available input across a wide range of ubiquitous environments. For example, a user could flick their wrist to dismiss a phone call or tap their thumb and index fingers together to make a selection in mixed reality. To move toward such applications, this work evaluates a new multimodal commercially available device (theBioPointbySIFI Labs) for recognizing seven dynamic hand gestures. Three sensors were evaluated, including a single channel of electromyography (EMG), a three-axis accelerometer (ACC), and photoplethysmography (PPG). Using a deep LSTM-based network, the relative performance of each sensor and all possible combinations were compared for their gesture classification abilities. The results show that the combination of all sensors led to the highest classification accuracy ($>$96%), significantly outperforming the individual performance of each sensor (p$< $0.05). In addition, the fusion of all sensors significantly improved performance across days (p$< $0.05) and was significantly more resilient when classifying gestures elicited in unseen limb positions (p$< $0.05). These results highlight the complementary benefits of fusing EMG, ACC, and PPG signals as a viable path forward for the reliable recognition of discrete event-driven gestures using wrist-based wearables.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".