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Demo: Complex human gestures encoding from wearable inertial sensors for activity recognition

conference contribution
posted on 2023-06-09, 09:31 authored by Mathias Ciliberto, Luis Ponce CuspineraLuis Ponce Cuspinera, Daniel RoggenDaniel Roggen
We demonstrate a method to encode complex human gestures acquired from inertial sensors for activity recognition. Gestures are encoded as a stream of symbols which represent the change in orientation and displacement of the body limbs over time. The first novelty of this encoding is to enable the reuse previously developed single-channel template matching algorithms also when multiple sensors are used simultaneously. The second novelty is to encode changes in orientation of limbs which is important in some activities, such as sport analytics. We demonstrate the method using our custom inertial platform, BlueSense. Using a set of five BlueSense nodes, we implemented a motion tracking system that displays a 3D human model and shows in real-time the corresponding movement encoding.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

EWSN ’18 Proceedings of the 2018 International Conference on Embedded Wireless Systems and Network

Publisher

Association for Computing Machinery

Page range

193-194

Event name

International Conference on Embedded Wireless Systems and Networks

Event location

Madrid, Spain

Event type

conference

Event date

February 14-16, 2018

ISBN

9780994988621

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Sensor Technology Research Centre Publications

Full text available

  • No

Peer reviewed?

  • Yes

Editors

Dimitrios Koutsonikolas, Domenico Giustiniano

Legacy Posted Date

2018-01-02

First Compliant Deposit (FCD) Date

2017-12-21

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