Handwashing Action Detection System for an Autonomous Social Robot

TL;DR

This paper introduces a CNN-based handwashing action recognition system for social robots to promote proper hand hygiene among children, achieving high accuracy in diverse environments.

Contribution

It presents a novel CNN architecture with CSAB pooling for accurate handwashing gesture recognition in social robotics.

Findings

Achieved 90% accuracy in recognizing handwashing steps

Generalizes well to unseen environments

Detects subtle hand movements effectively

Abstract

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics.