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Healthcare·AI Engineers On Demand·24 weeks

Motion Clinic

Replacing Weekly Clinic Visits With Real Time Remote Monitoring

A US physiotherapy platform needed to let patients complete exercises at home while clinicians evaluated their form remotely, but real time human movement analysis through a webcam was a specialized AI challenge outside the team's core domain. They brought in AI engineers who built a movement analysis system that watches patients through their own cameras and grades every repetition against clinical rules on the spot.

60 FPS

Processing Speed

30 hours

Weekly Time Recovered

3x capacity

Patient Volume

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Motion Clinic case study hero
“We needed specialized AI talent fast, and they delivered people who were ready to work from day one.”
MD

Mary Davis

VP Information Technology, Motion Clinic

Before

Clinicians present for every session, Manual exercise form evaluation, Spreadsheet based clinic billing

After

Patients exercise from home, Automated real time movement scoring, Usage based automated invoicing

Narrative

The full story.

The Challenge

A 5-minute movement check shouldn't eat a full appointment slot, but that's what was happening. Clinicians had to be in the room for every rep of every exercise, capping each one at 6 to 8 patients a day. Referrals were piling up, schedules were full, and there was no way to evaluate patient form without physically being there.

What We Built

The founding team needed computer vision expertise they didn't have in-house, so they brought in on-demand AI engineers. The system uses the patient's own device camera to map 32 points across their body during exercise. Each rep gets scored against rules the clinician defines, the same criteria they'd apply standing in the room. Processing runs at 60 frames per second on desktop with no perceptible delay.

Results

Clinicians went from 6 to 8 in-person sessions a day to reviewing over 20 patients remotely. The bottleneck was physical presence, and the platform removed it. A mid-sized clinic recovered about 30 hours a week previously lost to travel and scheduling overhead. The 32-landmark tracking gives clinicians more movement detail than in-person observation, where you're stuck with one angle. Within two quarters the platform had paid for itself, with billing shifted from "clinician is in the room" to "clinician reviews movement data.

Technical proof

How it was built.

Implementation details and the stack behind the delivery.

Implementation highlights

Key engineering decisions from this project.

Founding team sought specialized AI and computer vision engineers to complement their existing product team
Embedded engineering team ramped and delivering within the first two weeks
Full platform shipped across web and mobile in under 12 months
Mobile processing speed optimized from 7 frames per second to 27 on older devices

Stack snapshot

Tools and platforms used in the delivery.

React NativeTypeScriptDynamoDBPythonAWS

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