Multi-Gait Recognition for a Soft Ankle Exoskeleton with Limited Sensors
Bibliographic record
Abstract
In order to offer appropriate and reliable assistance to users, wearable robotic devices usually detect human locomotion through multi-sensor fusion system. However, multi-sensor fusion system increased the complexity of the sensor system and the burden of wearing on users for ankle exoskeleton. To optimize the sensor system and recognize multi-gait, we present a multi-gait recognition algorithm for a soft ankle exoskeleton with two IMUs (Inertial Measurement Units) mounted on foot. Five gait features are extracted during swing phase, including mean vertical velocity, mean horizontal velocity, vertical displacement, horizontal displacement, and the inclination angle at foot contact. Then, these gait features are used as the input of BPNN (Back Propagation Neural Network) to recognize five common gait modes (level walking, stair ascent/descent, ramp ascent/descent). The proposed algorithm can provide an accurate automatic recognition result at the early beginning of each stance phase. The results of the experiment shown that the proposed algorithm can distinguish above gait modes with 99.0% success rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".