UCLA-STMicroelectronics/Reference Designs

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Introduction

Many UCLA students have collaborated in the development of Internet of Things (IoT) SensorTile systems in course projects. The senior capstone design course has included teams who have developed novel systems for motion classification with SensorTile data sources and machine learning methods.

This has included systems with single and dual SensorTile devices along with signal processing, signal feature extraction, neural network design, neural network training, and finally in-field performance analysis.

Reference Designs have been developed by these student teams that include design and development documentation as well as source code for your use.

These are available for evaluation and guidance with the objective that these may inspire other future development,

If you have questions regarding the Reference Designs, please contact kaiser@ee.ucla.edu.

Student Project Team Reference Designs

  1. STMicroelectronics SensorTile Reference Design: Basketball Freethrow Classifier by Machine Learning
    This Reference Design developed by Alexander Graening and James Xu describes the comprehensive development of a system capable of detecting characteristics of the Basketball Freethrow motion. This relies on the complete set of signal acquisition from SensorTIle systems, signal processing, and machine learning. The SensorTile systems are used in pairs with attachment at upper and lower arm.
  2. STMicroelectronics SensorTile Reference Design: Basketball Hookshot Classifier by Machine Learning
    This Reference Design developed by Ziyue Yang and Zhitong Qian describes the comprehensive development of a system capable of detecting the characteristic motion of arm and hand associated with optimal and suboptimal Basketball Hookshot motion. The complete description of SensorTIle systems, signal processing, and machine learning development is included The SensorTile systems are used in pairs with attachment at upper and lower arm.
  3. STMicroelectronics SensorTile Reference Design: Tennis Motion Classifier by Machine Learning
    This Reference Design developed by Bonnie Lam and Gheorge Schreiber describes the another comprehensive development of a system capable of detecting Tennis motion swing types and swing quality. This also relies on the complete set of signal acquisition from SensorTIle systems, signal processing, and machine learning. Here the SensorTile is attached to the tennis racket.
  4. STMicroelectronics SensorTile Reference Design: Resistance Training Motion Classifier by Machine Learning
    This Reference Design developed by Guang Liew and Zhijie Yao describes another development based on dual SensorTile motion sensors each providing data sources for feature extraction. This system classifies proper and improper resistance training motions by SensorTile data sources and machine learning classifier systems.
  5. STMicroelectronics SensorTile Reference Design: Climb On: A Climbing Motion Classifier by Machine Learning
    This Reference Design developed by Loic Maxwell and Craig Young describes the development of a unique system that classifies the complex motions occurring in climbing. This classifies proper and improper motion applying dual SensorTile systems and a series of signal processing, feature extraction, and machine learning solutions.
  6. STMicroelectronics SensorTile Reference Design: Shoulder Rehabilitation Motion Classifier by Machine Learning
    This Reference Design developed by Michael Qi and July Zamora also applies dual SensorTile data sources to the important problem of classifying proper and improper motion required in shoulder rehabilitation. This also applies dual SensorTile devices along with end-to-end development from signal acquisition to machine learning system design and performance analysis.
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