Paper Info
Title
An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model.
Abstract
Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB's toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.
Year
DOI
Venue
2020
10.3390/s20174803
SENSORS
Keywords
DocType
Volume
gesture recognition,acoustic-based input,artificial neural network,Hidden Markov models
Journal
20
Issue
ISSN
Citations 
17
1424-8220
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Bruna Salles Moreira100.34
Angelo Perkusich227961.03
Saulo O D Luiz300.34