Weight Sensitive Human-Machine Interface for Electric Longboards

Abstract

Electric longboards have recently become an increasingly popular means to commute to work due to their portability, speed, intuitive learnability, and environmental friendliness. However, most electric longboards rely on the rider using a Bluetooth or infrared control remote to change the board speed. This project aims to explore a novel human machine interface for electric longboards that is different from the conventional remote controlled electric longboards. We propose and build a human machine interface that relies on using the rider's weight shifting dynamics, similar to the dynamics of using a Segway or hoverboard, to control the longboard. This is accomplished by modifying an existing electric longboard and reading the full distribution of a rider’s weight on the longboard under various conditions with a variety of sensors. This rich sensor data is run through an adaptive control algorithm and signal processing for the rider’s weight to produce a natural output speed for the rider.