Capacitive Touch Sensing

Using a capacitive touch sensor, we implemented PRBS drive and sense lines to track touch. Our code generated both a heatmap of the touch as well as a centroid estimate.

Description

• Implements capacitive touch sensing using PRBS-driven lines and cross-correlationbased detection on sense lines.

• Generates a real-time touch heatmap and calculates a centroid from sensor input.

• Optimizes sampling and frame rate to balance responsiveness and precision (up to 6 fps).

• Demonstrates trade-offs between short vs. long PRBS sequences for speed and accuracy.

• Uses Kalman filtering to estimate touch location and velocity, enabling gesture tracking.

• Touch data is visualized live, including ellipse fitting and heatmap overlays.

• System built and tested with noise analysis, baseline correction, and bit-length tuning for robust detection.

Learning Outcomes

1. Technical Skills & Knowledge

• Capacitive touch sensing principles and PRBS signal generation

• Cross-correlation algorithms for localization

• ADC configuration and sampling rate analysis

• Real-time data plotting and signal noise handling

• Application of 2D Kalman filters for motion estimation

• Comparative evaluation of signal bit-lengths for design tuning

2. General Project/Practical Skills

• Trade-off analysis: Accuracy vs. speed in sensing systems

• Experimental design and controlled testing for performance evaluation

• Interpreting and processing large sensor datasets

• Clear documentation through visuals, live demos, and code organization

Documentation

Reach full documentation and demos here: https://docs.google.com/document/d/1w31i9TI1qzB_Jlte6xMf54AVjQe2aJYGS4aqW-wkKgk/edit?usp=sharing

All the code and demo videos are in: https://drive.google.com/drive/folders/1IhsBLmNXyd7g-sWnGCal_qK_OI29GPDY?usp=sharing