Learning human motion intention with 3D Convolutional Neural Network

Joshua Owoyemi, Koichi Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper, we present an end-to-end learning approach for human motion inference from 3D point cloud data. Examples of human motion to be learned are collected as point cloud data through a 3D sensor, mapped into 3D occupancy grids and then used as supervised learning samples for a 3D Convolutional Neural Network (3D CNN). The 3D CNN model is able to learn spatiotemporal features from time steps of occupancy grids and predict human motion intentions with an accuracy of 83% within 60% of the motion performed. We demonstrate the performance of this model in real time by predicting the intention of a human arm motion for some predetermined targets, and furthermore generalise the model to new users whose data were not used in the training of the model. This approach is useful for human-robot interaction and human-computer interaction applications that need human motion learning without explicitly modelling the dynamics of the human motion.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1810-1815
Number of pages6
ISBN (Electronic)9781509067572
DOIs
Publication statusPublished - 2017 Aug 23
Event14th IEEE International Conference on Mechatronics and Automation, ICMA 2017 - Takamatsu, Japan
Duration: 2017 Aug 62017 Aug 9

Publication series

Name2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017

Other

Other14th IEEE International Conference on Mechatronics and Automation, ICMA 2017
CountryJapan
CityTakamatsu
Period17/8/617/8/9

Keywords

  • Convolutional Neural Network
  • Motion Prediction
  • Point Clouds
  • Spatiotemporal Learning

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Artificial Intelligence
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Learning human motion intention with 3D Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this