A clinical review workspace for selecting recorded movement frames, correcting detected key points, lines, and angles, comparing reference guidance, and saving validated measurements.
Clinical Movement Re-annotation is a web application created to make a real workflow clearer, faster, and easier to trust. It turns a focused product idea into something people can use, evaluate, and understand without needing to inspect the source code.
Why Clinical Movement Re-annotation exists
Automated movement analysis can place measurement geometry on an imperfect frame or anatomical position. Clinical reviewers need a precise, explainable way to choose the right frame, correct lines and angles, compare reference guidance, and save revised measurements without losing the original exercise context.
The goal is not simply to add another tool. Clinical Movement Re-annotation is designed to reduce the friction around that problem and give people a more dependable way to complete the work.
What it delivers
- Reviewers can correct automated clinical measurements through a guided workflow instead of manipulating an opaque legacy canvas.
- Typed domain models preserve line, angle, direction, and measurement semantics across load, edit, render, and save operations.
- Malformed or unavailable upstream data produces explicit recovery guidance rather than partially initialized review state.
- The modernized boundaries make geometry and API behavior independently testable while legacy services continue to operate.
The experience
A human-in-the-loop clinical workspace for reviewing recorded movement frames and correcting AI-generated lines, angles, key points, and measurement values before results are saved.
The project brings the important parts of the workflow into one coherent experience. Its interface and behavior are shaped around the people using it, while the implementation stays focused on predictable results and honest feedback.
What to know
Every product has boundaries. These are the most important considerations for Clinical Movement Re-annotation:
- Measurement geometry must remain correct while media is scaled, points are dragged, and line types restrict movement to one axis.
- The workflow depends on multiple tenant-specific legacy APIs and session values whose payloads can be missing, stale, or inconsistent.
- Large video frames and rendered canvas images must be validated and processed without freezing the review interface.
- Clinical reviewers need explicit unavailable, loading, validation, save, retake, and completion states rather than ambiguous canvas failures.
These limitations are presented openly so visitors can understand where the project works well, what it depends on, and what may change in future versions.
Current status
Private employer-owned clinical tool under active modernization. The current release demonstrates the core value of the project and the main end-to-end experience.
My role was frontend architecture and modernization within the mymedicalhub team, covering clinical review workflow, canvas geometry, legacy api integration, validation, error recovery, and automated testing. The related case study explains the decisions and delivery process in greater depth for visitors who want the engineering story behind the product.