GrolluSpik

Smartphone-based Velocity Tracking for Powerlifters

A research platform that turns ordinary training videos into detailed barbell velocity profiles using computer vision — no special equipment required.

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How It Works

1. Record

Film your set with any smartphone. Side view works best.

2. Upload

Upload the video with exercise type, weight, reps, and RPE.

3. AI Analysis

YOLO segmentation + ellipse fitting extract barbell velocity frame-by-frame.

4. Insights

Per-rep velocity, ROM, phase timing, trends, and coach feedback.

Platform Features

VBT Analysis

Peak & mean velocity, ROM, concentric/eccentric split, velocity decay, and rep consistency — all computed automatically from video.

Dashboard

At-a-glance summary of sessions, exercises, average RPE, and recent uploads.

Trends & Progression

e1RM progression, load-velocity profiles, readiness tracking, rep consistency, personal records, and auto-generated insights.

Athlete Profile

Track bodyweight over time, log competition results, set goals, and calculate Dots & IPF GL points.

Coaching

Coaches invite athletes, view their videos, trends, and profile. Approval-based linking ensures privacy.

Feedback & Messaging

Threaded comments on individual videos plus direct messaging between coach and athlete.

Notifications

Real-time alerts for coach requests, new uploads, feedback, messages, and completed analyses.

Dark Mode

Full light/dark theme toggle, respects system preference.

Privacy First

Google SSO login, data ownership, GDPR-aware design, and opt-out from research use.

Research Background

This platform is the practical component of a Master's thesis investigating two research questions:

RQ1 — How accurately can smartphone video + YOLO segmentation + ellipse fitting measure barbell velocity and range of motion compared to a tethered sensor (RepOne)?
RQ2 — Can classical ML models trained on VBT metrics predict athlete-reported RPE?

Built with Flask, Bootstrap, Chart.js, Google Cloud Run, and a custom 6-step ML pipeline (preprocess → segment → ellipse fit → cleanse → perspective transform → rep detection).