MotionBeep: Enabling Fitness Game for Collocated Players With Acoustic-Enabled IoT Devices
Bibliographic record
Abstract
Fitness games recently attract much attention these years due to its combination of playability and athleticism. However, most fitness games are entertainment for a single person, with only a few deliver distinctive experiences for multiplayers. Enabling interaction between multiple players would be more enjoyable due to exciting cooperation among players. Considering a Big Stomach Challenge for two players, a certain amount of food can be only eaten when a mouth size, represented by the distance between players is reached collaboratively. Similarly, a certain type of food can only be picked up when the food grabbing speed, denoted by the approaching speed between players, is fast enough. Such games require accurate ranging and speed estimation in a relatively long distance (1-15 m) to deliver a good gaming experience. However, existing ranging schemes cannot meet the above requirements. They either cannot work under Doppler channels or have to strike a balance between accuracy and operational range, prohibiting a heuristic implementation for the above games. To this end, we design MotionBeep, a novel acoustic ranging scheme that achieves centimeter-level ranging and dm/s-level speed estimation accuracy under representative indoor and outdoor scenes within 15 m. In MotionBeep, we design a new working paradigm and incorporates a state-space model to maintain accurate ranging in both static and dynamic channels. We have implemented a system prototype and evaluate its performance in representative environments. Evaluation results demonstrate that MotionBeep achieves a median of centimeter accuracy with up to 15 m even under Doppler effect.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".