Sensor-Based Continuous Hand Gesture Recognition by Long Short-Term Memory

TL;DR

This paper introduces a sensor-based continuous hand gesture recognition algorithm using LSTM that leverages accelerometer and gyroscope data, achieving robust and accurate classification on smartphone prototypes.

Contribution

The paper presents a novel LSTM-based algorithm for continuous hand gesture recognition using minimal sensors, with a prototype implementation and experimental validation.

Findings

Effective gesture recognition accuracy demonstrated

Robust performance with basic accelerometers and gyroscopes

Prototype system validated on smartphones

Abstract

This article aims to present a novel sensor-based continuous hand gesture recognition algorithm by long short-term memory (LSTM). Only the basic accelerators and/or gyroscopes are required by the algorithm. Given a sequence of input sensory data, a many-to-many LSTM scheme is adopted to produce an output path. A maximum a posteriori estimation is then carried out based on the observed path to obtain the final classification results. A prototype system based on smartphones has been implemented for the performance evaluation. Experimental results show that the proposed algorithm is an effective alternative for robust and accurate hand-gesture recognition.