Development of 2D Ultrasound Tracking Software and Hardware to Monitor Multiple Flexor Tendon Displacement for Applications Toward Hand Prostheses
Bibliographic record
Abstract
This thesis work provides a new way to detect and track the displacement of flexor tendons within the human arm, using a non-invasive, ultrasound-based, speckle tracking technique. By tracking the tendons in the arm, it provides a way to monitor a person’s intention to move their hands and fingers. This has application to hand prosthetic control, as well as tendon injury assessment, which has significant contributions to the medical and rehabilitation community. The system works by capturing and processing a sequence of B-scan ultrasound images, to detect and track the flexor tendon motion (excursion) in the wrist, as the user flexes their muscles. Given the biomechanics of the hand, tendon displacement is correlated to the user’s intention to move their finger. Several speckle tracking techniques using B-scan ultrasound image sequences are developed in this work, including: auto-location of the tendon, a stationary ROI (region of interest), and novel use of similarity measures such as FT (Fisher Tippett), and hybrid methods. As well, work is done to investigate various speckle tracking parameters, and their effects on tracking accuracy. The different speckle tracking techniques are developed using data obtained from cadaver hands, and human volunteers undergoing regular surgery. The tracking techniques are compared in terms of successfully detecting the tendon, accurately tracking tendon displacement, successfully tracking multiple tendons, successfully detecting and tracking the onset of low tendon displacement, and computational efficiency of the algorithms. Another major aspect of this work is the design of a novel quad-array transducer that can collect image sequences from up to four tendons simultaneously. This transducer is instrumental to the motivation for controlling an advanced prosthesis. As well, specialized hardware is designed for the cadaver-based studies. Overall, this thesis successfully demonstrated the proposed tracking algorithms and newly designed hardware, for tracking the displacement of single and multiple flexor tendons. It has provided several important contributions to the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".