Development of a low cost, sensing device to assist in the physiotherapy and day-to-day treatment of incorrect posture due to the tightening of pectoral muscles and hip-flexors or Kyphosis and Scoliosis
Bibliographic record
Abstract
The failure to maintain correct posture can have drastic effects; the more severea curvature in the spine becomes, the more likely that the afflicted person will needeither a back brace or, have to undergo corrective surgery1. Permanent deformity andmuscle strain can occur, as prolonged effects2. In an attempt to avert these issues,physiotherapists often prescribe stretches and exercises to patients, that can be practicedthroughout the day in order to correct the spinal position and improve overall posture3.The patient however, may forget or become too tired to continue practicing properposture. With this in mind, a potential solution that could be implemented would be adevice that detects movement in the back. It would alert the patient if they slouch theirshoulders or arch the lower region of their back to an extreme degree, for example. Thisalert would be the vibration of a pad on the patient’s stomach, or a vibration or beepingsound from the patient’s phone. In order to create this mechanism, familiarization withthe ADXL 345 accelerometer and how to interface it with a processor, was necessary.After the program was complete, examination of the acceleration values obtained wasdone. Observed anomalies led to the need for calculation of angular movement. Theapproach of using equations involving 3-dimensional movement, and derivatives ofthese equations were implemented into the program. Calibration of the accelerometerat rest was needed as well. Both of these would allow for more accurate detection oftwists and tilts in a human’s back. Introducing a second or third accelerometer into thisdevice is a valid option that may be explored in the future. This modification wouldallow for increased accuracy in values obtained, as well as for continuous calibrationwhile the device is in use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".