Human Action Recognition of Autonomous Mobile Robot Using Edge-AI

Shih Ting Wang, I. Hsum Li*, Wei Yen Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The development of autonomous mobile robots (AMRs) has brought with its requirements for intelligence and safety. Human action recognition (HAR) within AMR has become increasingly important because it provides interactive cognition between human and AMR. This study presents a full architecture for edge-artificial intelligence HAR (Edge-AI HAR) to allow AMR to detect human actions in real time. The architecture consists of three parts: a human detection and tracking network, a key frame extraction function, and a HAR network. The HAR network is a cascade of a DenseNet121 and a double-layer bidirectional long-short-term-memory (DLBiLSTM), in which the DenseNet121 is a pretrained model to extract spatial features from action key frames and the DLBiLSTM provides a deep two-directional LSTM inference to classify complicated time-series human actions. Edge-AI HAR undergoes two optimizations - ROS distributed computation and TensorRT structure optimization - to give a small model structure and high computational efficiency. Edge-AI HAR is demonstrated in two experiments using an AMR and is demonstrated to give an average precision of 97.58% for single action recognition and around 86% for continuous action recognition.

Original languageEnglish
Pages (from-to)1671-1682
Number of pages12
JournalIEEE Sensors Journal
Volume23
Issue number2
DOIs
Publication statusPublished - 2023 Jan 15

Keywords

  • Autonomous mobile robot (AMR)
  • ROS
  • bidirectional long-short-term-memory (BiLSTM)
  • edge artificial intelligence (Edge AI)
  • human action recognition (HAR)

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Human Action Recognition of Autonomous Mobile Robot Using Edge-AI'. Together they form a unique fingerprint.

Cite this