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Home exercises with nobody there to correct them

Context

Activate is my final year specialisation project, an application built to assist RSI Type 1 patients with their exercises in real time. It started from watching friends and family go through physiotherapy rather than from a brief.

The project aligns with Goal 3 of the United Nations Sustainable Development Goals, Health and Well-Being.

RSI Type 1 covers the specific named conditions rather than diffuse strain, which is why the screens carry carpal tunnel syndrome, tennis elbow and bursitis rather than one general diagnosis.

Findings

I interviewed people currently in physiotherapy, people who had completed it, and physiotherapists, and read up on the state of physiotherapy in India, the existing apps and available technology, and RSI literature. Here is what I found.

  • The WHO recommends one physiotherapist per 10,000 citizens. India has 5,000 qualified ones.
  • Patients may be told to follow varied steps for the same exercise.
  • RSI Type 1 is increasingly prevalent among people with desk jobs, with a rise of 30 to 40% observed in Bengaluru.
  • Exercising at home lacks guidance, leading to potential errors and an increase in pain.
  • Patients forget steps, perform with the wrong posture, and most of them fail to complete their treatment.

Explorations and decisions

Few options were compared before anything was designed.

Video calls Rejected

A physiotherapist watches remotely and corrects in the moment.

Dropped because it does not remove the constraint. Physiotherapists are already balancing clinic patients against online ones, so availability stays limited, and the cost rises precisely because a person has to be present for it to work at all.

A patient on a laptop video call with a clinician.

Recorded exercises Rejected

Film the correct movement once and let patients follow along.

Dropped because a recording cannot see the patient. Each condition has a standard set, but the steps get modified according to how severe the case is, so a generic video is wrong for most of the people watching it. The posture risk stays, and the patient still gets no signal about whether they are doing it right.

A tablet on a table playing a follow along exercise video.

Computer vision

Detect the patient’s own hand and body points, and judge the movement against a reference.

This was the option taken. Open source pose libraries already handle the detection part, so the actual work was the judgement part. That meant extending detection into a decision about whether the movement is correct, and telling the user while they are still doing it.

A person mid pose with a skeleton overlay tracking their joints.

Idea

Using open source libraries like MediaPipe to detect hand points and body points, and extending the code further to guide patients with their exercises.

Four steps. One, the exercise is recorded at the clinic with the physiotherapist present and stored as data set D1. Two, the same exercise is performed at home and collected as D2. Three, the two are compared to decide correct or incorrect. Four, when incorrect the patient is given feedback in audio and visual form while they are still exercising.

Applications for both patient and orthopedist

The patient needs to be told what to do and corrected while doing it. The orthopedist needs to walk into the next appointment already knowing what happened in between. Those are different products, so Activate is two of them, each on desktop and mobile.

The final design

Patient side

Exercise preview

A gif previews the exercise before the patient starts, so they know the shape of the movement before they are being judged on it.

The exercise screen, with three exercises as tabs and a preview clip beside the timing and instructions.

Guided exercise

Bad form is detected and called out in both channels at once, audio and visual, with a snippet of the clinic recording playing in the corner to correct against. The audio can be muted.

Guided exercise, with the tracked skeleton over the live feed, a red border for bad form, and the reference clip in the corner.

Get feedback

After exercising, patients receive a report on their exercise duration and a feedback form. Both are sent to the orthopedist for review.

The feedback screen, showing time taken beside six pain rating sliders for different daily contexts.
Patient mobile home screen.

Home

Patient mobile feedback form.

Feedback form

Patient mobile feedback summary.

Feedback

Patient mobile schedule screen.

Schedule

Orthopedist side

More but less

The home screen shows the working day rather than a set of metrics. It lists appointments in order, who is being consulted now, and how the day’s diagnoses break down. Clicking details opens further patient information, so the list stays readable and the depth is one click away.

Orthopedist home screen, with the day's appointments beside a pie chart of diagnoses.

Easy monitoring

Patients who are not doing their exercises are highlighted, so the orthopedist can monitor them better.

Patient list screen, with patients who are not doing their exercises highlighted.

Clear insights

Patient information is presented in a clear and understandable manner. Pain location is marked on a body map, progress is a chart across months, the exercise calendar shows which days were actually done, and observations are kept as a dated log.

Patient information screen, with a body map, progress chart, exercise calendar and observations log.
Orthopedist mobile home screen.

Home

Orthopedist mobile feedback screen.

Feedback

Orthopedist mobile schedule screen.

Schedule

Proof of concept

Used MediaPipe and Python to write the code and executed it using Unity and C#. Blue spheres indicate the exercise is performed in right form and red spheres indicate wrong form.

A webcam window with hand points detected and labelled left.

Detecting hand points

A hand rendered in Unity with blue spheres at the joints, indicating correct form.

Right form

The same hand rendered with red spheres at the joints, indicating incorrect form.

Wrong form

Watch the proof of concept →

Reflection

Healthcare was new to me, and so were Python and MediaPipe. I learned enough of all three to build the thing rather than describe it.

What I wanted next, written at the time: better accuracy in the posture correction, more areas of physiotherapy than RSI, and using the accumulated data to suggest exercises to physiotherapists rather than only report to them.

It was never tested with patients. Until that happens the design is a proposal.

← Previous project Designing evaluation around evidence, not just answers