Development of a Brain Controlled Assistive Feeding System with OpenBCI
Bibliographic record
Abstract
Amyotrophic Lateral Sclerosis (ALS) Patients, individuals confined to respective homes, and those with upper limb disabilities frequently experience feeding issues and malnourishment.Asphyxia or choking can occur during feeding, which is frequently uncomfortable and time-consuming.Currently, these individuals are assisted in eating by robotic devices.All the same, persons with severe disabilities-such as sensory loss-or trouble with basic physical mobility should not employ assistive robots that need movement from the user.An amazing help in this area is a robotic system that is controlled only by brain signals.Therefore, a prototype of an electroencephalogram (EEG)-based feeding robot is proposed based on the specifications for a real-time helpful robot which is a Brain Computer Interface (BCI).An assistive technology called a feeding assistance robot is used to help people who are unable to independently move food from a container into their mouths.Feeding assistance robots have been introduced to help those who experience upper limb function loss due to cerebral palsy, spinal cord injuries, or amputations.These individuals may find it impossible to feed themselves.A set of experiments were carried out with healthy subjects to validate the proposed system and results are here presented.According to experimental data, the built system can do the necessary tasks in real-time with acceptable errors of an average of about 21% with a 77% overall accuracy for the system in performing the feeding of the users.With further supervision, this level of inaccuracy can be decreased or, in certain situations, completely eliminated.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".