Case study

EMG Biceps Curl Analysis

This project processes surface EMG and goniometer data from biceps-curl trials. The workflow filters and normalizes EMG signals, computes RMS-based comparisons, and evaluates activation behavior across elbow moments, joint angles, and dynamic repetition phases.

RoleBiomechanics signal analysis and MATLAB implementation

TimelineENGG3150 Module 3

FocusEMG + Biomechanics Signal Processing

EMG filtering + normalizationJoint-angle phase analysisDynamic rep segmentationTechnical reporting

MATLABPlatform

ENGG3150 Module 3Module

https://github.com/MaxwellM34/EMG_Lab3_Biceps_Curl_AnalysisRepository

The brief

Challenge

Quantify muscle activation differences across contraction conditions while handling noisy biosignal data and preserving fair normalization between trials.

Approach

What we made

I built MATLAB scripts to filter raw EMG, compute normalized RMS metrics, align data to biomechanical phases, and compare activation across load, angle, and repetition segments.

Implemented EMG preprocessing with band-pass filtering and envelope-based signal preparation.
Normalized activation profiles against isometric reference trials for consistent comparison.
Analyzed muscle behavior in both static-angle and dynamic biceps-curl contexts.

Outcome

Results

Delivered a reproducible EMG analysis pipeline and report-ready visual outputs showing clear activation trends for biceps brachii and brachioradialis in isometric and dynamic conditions.

MATLABEMG processingSignal Processing ToolboxGoniometer data

Gallery

Visual snapshots

Click any image to expand.

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