A novel design and implementation of wheelchair navigation system using Leap Motion sensor
Bibliographic record
Abstract
PURPOSE: In this paper, a novel design for a leap motion wheelchair navigation system is proposed, and the suggested model is implemented on a prototype. The behaviour of the created prototype is closely observed during the different performance tests carried out, and the results are presented throughout this manuscript. MATERIALS AND METHODS: In the prototype, a Leap Motion sensor is implemented to acquire navigation data through hand gestures of the users. This navigations system design is specifically implemented to facilitate wheelchair use for amputee users and stroke patients as it does not rely on the movement of the fingers. Through this design, wheelchair movement can be controlled through detection of finger, fist, palm or wrist (for amputees) movement by the leap motion sensor. Bluetooth connection is used as the navigation system's communication means, removing the need for constant internet connection and providing freedom of movement outside of internet-covered territory. Additionally, two Dynamixel motors are used as movement force, which yield optimal computational time and minimal delay. RESULTS: The performance of the designed prototype is tested by considering response time and speed resolution as evaluation metrics. Results suggest that the designed wheelchair will give movement independence to users who cannot use their fingers to control the movement of their wheelchairs, while reducing delay, being independent of internet connection, providing high resolution and minimising detection error. CONCLUSIONS: The promising results obtained from prototype testing suggest the possibility of real-life application of this wheelchair navigation system, which can greatly assist amputee users and rehabilitation patients.Implications for rehabilitationA novel wheelchair navigations system designed to facilitate amputee users, stroke patients and rehabilitation patients.The proposed system eliminates the reliance on finger movements, is gaze independent, and does not require voice or gesture control, creating much more freedom for users undergoing specific medical conditions or still under rehabilitation or treatment.Results demonstrate very low delay time in wheelchair command to action, allowing improved control for users and reducing the occurrence of control-related accidents.The designed wheelchair navigation system is independent of internet connection, allowing more freedom in range for wheelchair users compared to available cloud based models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".