LapOps

Optimize your RC racing performance by letting our app learn.

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Things used in this project

Hardware components

Android device

Story

Intro

This project is created in context of the lecture “IoT ApplicationsPrototyping” at the Brunel University / TAE Esslingen. It covers the invention of an own IoT Idea and Application from the scratch solving a real-world problem. In the lecture the MSc students learn about signal processing, pattern recognition and scientific analysis in context of IoT applications.

Smartphone Sensing Framework

The Smartphone Sensing framework (SSF) provides a collection of modules anda quick way to develop context aware apps on android in an experimetal way.

IoT Prototyping Framework

The IoT Prototyping Framework (IoTPF) is a collection of tools, modules andsamples with the aim to empower students and developers creating full-stack IoT prototypes in a short time period.

Contact:

Idea and Application: Team D @ Lecture-Class 2018

Lecture, SSF and IoTPF: dionysios.satikidis@gmail.com

Tools:

https://github.com/MrDio/Smartphone-Sensing-Framework

https://github.com/MrDio/IoT-Prototyping-Framework

Brunel University London / TAE Esslingen 2018

This project was developed by students of Brunel University London as part of the Embedded Systems Engineering course.

Introduction

LapOps is an Android app about recognizing race tracks with their sections and improving racing performance by giving tips based on the performance analysis of each section.

Therefore two sensors of a smartphone had to be used. By measuring forces on the car with an accelerometer and identifying different laps with the activation of a proximeter triggered by a bridge at the start of each lap, a race track can be identified surprisingly precise.

Since the identification of sections was not possible to realise in real-time given the early deadline of the project, the scope had to be set to storing the race data and analyse the data after finishing the race.

This is done by capturing the forces acting on the car, remove the noise as good as possible and differentiate patterns, what does raise problems since not every round is driven validly or shows strange occurrences in the data set. These problems are equalized by testing different threshold parameters and using the best analysis outcome.

After the identification of sections is completed, these sections are classified into different categories. This way each lap is divided into sections matching the data collected.

The laps are then compared to each other and tips regarding the performance improvement in each section is given. These tips are gathered inside a report, which can be reviewed at any time.