Music-Bike

Category: Progress Updates

  • Video Demos and Final Report

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    Nick
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    Final Presentation Testing Bike Walkthrough Final Report

  • Week 9 Update – Testing On Bike

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    ethdiep
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    This week, we shifted focus toward real-world testing on the bike to evaluate how our adaptive audio responds during different ride conditions. The goal: determine what structure and sound design will work best for the final demo. ML Introduced a background InferenceService that runs machine learning on live sensor data (pitch, roll, yaw, g-force) using…

  • Week 8 Update – Putting Everything Together

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    lahnbennett
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    This week was especially exciting because we finally mounted our sensor pack onto the bike. We faced difficulties getting I2C running with our new PCB but after a software update modifying the pin layout everything is working as it should. Sensor Pack This is the top of our sensor pack. It contains the display, accelerometer…

  • Week 7 Update – Preparing to Test

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    ethdiep
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    This week, we focused on enhancing both the functionality and polish of the adaptive music bike app. Here’s what we accomplished: ML Introduced a file list viewer in the Sensors tab showing all saved .txt files, complete with pull-to-refresh and delete options. IMU data is now saved to timestamped .txt files in internal storage for…

  • Week 6 Update – Tuning & FMOD Integration

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    ethdiep
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    This week, we made significant strides in both hardware tuning and software integration for the adaptive music bike system. The test version of the firmware was successfully updated to include three potentiometers, enabling real-time adjustment of the jump and drop detection thresholds. This allows for rapid fine-tuning during test rides without needing to reprogram the…

  • Week 6 – Prototype Example Images

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    lahnbennett
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    Example images of our rapid prototype used for the in-class presentation on 5/6/2025

  • Week 5 Update – Hardware & ML

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    ethdiep
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    This week, we focused on both hardware stabilization to enhance sensor reliability and prepare for machine learning. We hope to begin testing on the bike soon. Hardware Improvements: Supply voltage was increased from 3.3V to 5V to address signal instability and eliminate phantom speed readings. This change significantly improved the reliability of wheel speed measurements.…

  • Week 4 – Rapid Prototype

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    lahnbennett
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    This post is a hands-on look at the current state of development and a overview of how we plan to integrate the separate pieces of our design: our app, FMOD studio, and our embedded sensors. We’ve integrated FMOD studio into our app and have begun testing it. This means the software that will play adaptive…

  • Week 4 Update – Sensor Fusion & Connectivity

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    Nick
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    Technical Breakthroughs Improved Connectivity: Fixed a critical limitation in our device’s Bluetooth connectivity, allowing all sensor data to flow properly to the mobile app. Enhanced Data Efficiency: Switched from text-based to binary data transmission between the device and app, dramatically improving performance and battery life. Direction Detection Upgrade: Implemented a “steadiness” feature that prevents false…

  • Music Bike – Product Requirements Document

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    Nick
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    1. Project Summary Music Bike aims to revolutionize the cycling experience by transforming rider actions into a dynamic, real-time musical journey. Using sensors mounted on the bicycle (measuring motion like speed, tilt, and G-force) connected via Bluetooth Low Energy (BLE) to an Android application, the system will adaptively manipulate music playback using the FMOD audio…