CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control

06/07/2023
by   Hymalai Bello, et al.
0

We present CaptAinGlove, a textile-based, low-power (1.15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control. We employ lightweight convolutional neural networks as the backbone models and a hierarchical multimodal fusion to reduce power consumption and improve accuracy. The system yields an F1-score of 80 evaluation of nine classes; eight hand gesture commands and null activity. For the RTE, we obtained an F1-score of 67

READ FULL TEXT
research
10/29/2021

On-device Real-time Hand Gesture Recognition

We present an on-device real-time hand gesture recognition (HGR) system,...
research
09/09/2013

Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks

New and more natural human-robot interfaces are of crucial interest to t...
research
01/29/2019

Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks

Real-time recognition of dynamic hand gestures from video streams is a c...
research
09/04/2023

An FPGA smart camera implementation of segmentation models for drone wildfire imagery

Wildfires represent one of the most relevant natural disasters worldwide...
research
10/03/2019

Convolutional Neural Networks for Speech Controlled Prosthetic Hands

Speech recognition is one of the key topics in artificial intelligence, ...
research
11/17/2019

NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions

We present the design, implementation, and evaluation of a multi-sensor ...
research
01/26/2020

Multimodal Data Fusion based on the Global Workspace Theory

We propose a novel neural network architecture, named the Global Workspa...

Please sign up or login with your details

Forgot password? Click here to reset