Gyroscope and Accelerometer based Assistive Spoon for Motor Disorder Patients
Bibliographic record
Abstract
Parkinson's disease is a neurodegenerative disorder that affects motor function, leading to hand tremors during simple tasks such as eating. In this project, it is proposed to create an AI augmented self-stabilizing spoon for Parkinson's patients to help them regain autonomy while eating. The device is fitted with inertial measurement sensors, such as gyroscopes and accelerometers, which sense real time movement of the hand. The sensor data is processed by a machine learning model to distinguish between voluntary hand movements and involuntary tremors. When tremors are identified, the built in micro actuators control the spoon head position to stabilize it and prevent spills while facilitating smoother meal consumption. The proposed research work mainly focused three base classifiers, namely Support Vector Machine (SVM), Random Forest (RF), and 1D Convolutional Neural Network (1D-CNN), The AI system learns users distinct tremor patterns for individualized support. Ergonomically and user friendly in design, the spoon is light, transportable, and can be recharged. It also has the possibility of wireless capability, with data logging and monitoring by carers or health professionals. The final aim is to improve the quality of life for Parkinson's sufferers by enabling autonomy and minimizing frustration at mealtimes. Future enhancements will be aimed at improving the AI model, enhancing battery life, and linking the gadget with mobile health applications for ongoing assistance and monitoring.
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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) | 0.000 | 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) | 0.003 | 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".