用手机检测走路脚步，生成音乐的实时系统
Iola Walker: A Mobile Footfall Detection System for Music Composition
- 通过蓝牙接收可穿戴传感器数据，在安卓端用LSTM实时识别脚步
- 在带GPU的电脑上训练模型，再部署到手机实现低延迟检测
- 开源代码供改进，适合音乐科技与交互设计爱好者
本研究是音乐技术项目的第一部分，旨在通过软硬件结合增强音乐创作。系统基于Android应用，利用LSTM模型实时检测佩戴者的脚步动作。数据由Mbient Labs IMU通过蓝牙传输至移动端，用户将采集的. csv文件移至配备GPU的计算机后，使用Python代码训练LSTM模型。训练完成后，将模型导出至安卓应用，即可实现脚步检测。项目代码已开源，鼓励读者与自己的LLM编程助手共同改进和实验。
原文摘要 · Abstract (English)
This outing is part of a larger music technology research project. The objective is to find a way to enhance music using hardware and software. This is the documentation for the Whimsical first part of the research project: it's an android app that detects a wearer's footfalls by running live inference on an LSTM. The system works by getting data from an Mbient Labs IMU to a mobile app over bluetooth. After you move the .csv file to a computer with a GPU, you can use the python code to train an LSTM on that data. You then export the LSTM to the android app and can begin detecting footfalls. Feel free to download and experiment with the code. It's meant to be read and improved upon by you and your LLM codewriter of choice! https://github.com/willbjames/iolawalker
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