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CASE STUDY · PRODUCT ENGINEERING

Turning raw motion data into 12 gait metrics providers can actually read

The software layer for a connected gait-analysis product, from Bluetooth capture through to provider review.

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WHAT CHANGED

Before and after

What the hardware produced, against what the software delivers.

BEFORE
Raw rotation and acceleration readings
AFTER
12 graph-ready gait metrics
CASE STUDY INFO
Client
SelnerTX
Industry
Digital health, biomechanics
Stack
React Native · Expo · React · TypeScript · Express · MongoDB · BLE
Result
12 gait metrics from raw sensor data

The problem

SelnerTX was developing gait-analysis technology built on connected foot-worn hardware. The devices could capture movement, but capture alone is not a product. Between a sensor on a shoe and a provider making a judgement sits a long chain of problems, and every one of them crosses a boundary.

Bluetooth permissions affect whether capture works at all. Sensor orientation affects whether the data means anything. Backend processing decides whether raw readings become usable metrics. And provider review depends on clear analysis rather than a wall of device output.

The client needed more than a companion app. The software had to make the entire chain feel like one coherent flow rather than four products stitched together.

What we built

01
Mobile capture app — Expo & React Native

Onboarding, authentication, Bluetooth device connection, permission management, capture, activity logs and analysis views. Scans for recognized devices, reads rotation and acceleration samples, uploads sensor batches.

02
Express and MongoDB backend

Stores patient, activity, capture and analysis records — and calculates gait metrics centrally rather than pushing sensor interpretation into each client, so web and mobile visualise the same numbers the same way.

03
React provider dashboard

Patient lists, capture flows, activity logs, session analysis, comparative analysis, drilldowns and exportable activity reports.

04
Three-stage calibration protocol

Device alone on flat ground, device placed in the shoe, then the user standing in neutral posture — a stronger reference point for movement captured in real-world rather than lab conditions.

The analysis layer

Twelve metrics, with per-cycle drilldowns and historical trends built on top.

stride time step time stride length cadence swing time step count composite gait score alignment symmetry consistency safe range of motion ROM utilization

Bluetooth capture, calibration, backend processing, patient records, activity context and gait visualisation now run as one workflow, giving SelnerTX a base for pilots, stakeholder demos and continued product development.

PUBLISHING RESTRICTIONS ON THIS PAGE
  • No outcome figures — no adoption, user or clinic counts, no time saved, no percentages.
  • No compliance or clinical language. No HIPAA, FDA, "clinically validated" or diagnosis claims.
  • Confirm the metric count before publishing, and confirm whether SelnerTX prefers "providers", "clinicians" or "care teams".
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