Activity monitoring and prediction for humans and NAO humanoid robots using wearable sensors

Saminda Abeyruwan, Faisal Sikder, Ubbo E Visser, Dilip Sarkar

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

3 Scopus citations

Abstract

While humans or biped humanoid robots perform activities such as jogging and running, an accident event such as a fall may occur. This might cause damage to the human body or to the structural components of the robot. For humans, immediate identification of a fall will allow fast responses, while for a robot, early prediction can be used to take corrective measures to prevent a fall. Modern wireless sensing devices can be attached to humans or robots to collect motion data. We propose: 1) methods to learn and predict different activities for humans and robots; and 2) software tools to realize these functions on embedded devices. Our contributions include: 1) detection of falls for both humans and robots within a unified framework; and 2) a novel software development environment for embedded systems. Our empirical evaluations demonstrate that with high accuracy different types of fall-events are predicted using the same learning algorithms for humans and biped humanoid robots.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015
PublisherAAAI Press
Pages342-347
Number of pages6
ISBN (Print)9781577357308
StatePublished - 2015
Event28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015 - Hollywood, United States
Duration: May 18 2015May 20 2015

Other

Other28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015
CountryUnited States
CityHollywood
Period5/18/155/20/15

ASJC Scopus subject areas

  • Artificial Intelligence

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