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Case study

Weightlifting Video Analysis Application

Computer vision bar path tracking and 3D skeleton replay in a premium light sports UI, with plate color semantics and reduced motion accessibility.

Strength training equipment in a gym
Industry
Fitness and Sports
Project type
Computer vision application
Representative stack
React · TypeScript · Python

Proposed architecture; original stack not verified.

Integrations
Video input and computer-vision analysis

Photography shows industry context, not the client’s premises or a product screenshot.

The context

The business behind the brief

The video analysis application is a delivered video-analysis product for Olympic weightlifting. It combines computer vision bar path tracking with 3D skeleton replay.

A premium light sports interface, plate color semantics and reduced motion accessibility are part of the delivered experience.

The challenge

What the system needed to solve

  • 01

    Present bar movement and body movement as complementary ways to review a weightlifting video.

  • 02

    Make a technical analysis product readable through a light sports interface and familiar plate colors.

  • 03

    Include reduced motion accessibility in an experience whose central subject is movement.

The solution

From requirements to working software

Delivered functionality and implementation boundaries.

01

Analyze the lift footage

Video provides the input for examining a lift. Recording angle, visibility and image quality affect how footage can be interpreted. Computer vision bar path tracking and 3D skeleton replay give the analysis interface two complementary representations of the recorded movement.

02

Inspect the bar path

Bar path tracking provides a representation of the bar's movement through the lift. This gives a lifter or coach a way to examine spatial behavior alongside ordinary video playback. It supports questions about the movement without claiming to replace a coach's interpretation.

03

Review the skeleton replay

The 3D skeleton replay offers a body-focused view of the recorded movement. Bar motion and body motion are related but distinct perspectives on the same lift. A reconstructed skeleton remains an interpretation, so reviewing the source footage is important when a result appears inconsistent.

04

Use the sport's visual conventions

The light sports UI uses plate color semantics. Familiar equipment colors help connect the analysis interface with the sport. Reduced motion accessibility addresses how users encounter movement in the interface, while keeping the analysis itself available as the purpose of the product.

Strength training equipment in a gym

Core functionality

What the product lets people do

  • Computer vision bar path tracking
  • 3D skeleton replay
  • Plate color semantics
  • Reduced motion accessibility

Technical implementation

Architecture and system boundaries

A representative design separates video ingestion and analysis from browser playback. Python workers can return time-indexed tracking and pose data, while a React/TypeScript interface synchronizes overlays and 3D replay. The delivered capabilities establish bar-path tracking, skeleton replay and reduced-motion support, but do not establish this exact historical implementation.

Representative implementation

React · TypeScript · Python

Business impact

The delivered outcome

  • Lifters and coaches can inspect the bar path
  • A lift can be reviewed through 3D skeleton replay
  • The interface reflects strength-sport conventions
  • Reduced motion accessibility is included

Let’s talk about the system you need.

Tell us where the current process breaks down. We can map the workflow, examine the integration requirements and define a practical scope for the build.

  • A mutual NDA is signed before project details are shared.
  • Scope is fixed in writing before any build starts.
  • You own the source code, the IP and the documentation at delivery.
  • Support plans are available and tailored on a discovery or technical call.