Discriminative human full-body pose estimation from wearable inertial sensor data

Loren Arthur Schwarz, Diana Mateus, Nassirv Navab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations


In this paper, a method is presented that allows reconstructing the full-body pose of a person in real-time, based on the limited input from a few wearable inertial sensors. Our method uses Gaussian Process Regression to learn the person-specific functional relationship between the sensor measurements and full-body pose. We generate training data by recording sample movements for different activities simultaneously using inertial sensors and an optical motion capture system. Since our approach is discriminative, pose prediction from sensor data is efficient and does not require manual initialization or iterative optimization in pose space. We also propose a SVM-based scheme to classify the activities based on inertial sensor data. An evaluation is performed on a dataset of movements, such as walking or golfing, performed by different actors. Our method is capable of reconstructing the full-body pose from as little as four inertial sensors with an average angular error of 4-6 degrees per joint, as shown in our experiments.

Original languageEnglish
Title of host publicationModelling the Physiological Human - 3D Physiological Human Workshop, 3DPH 2009, Proceedings
Number of pages14
StatePublished - 2009
EventWorkshop on 3D Physiological Human 2009, 3DPH 2009 - Zermatt, Switzerland
Duration: 29 Nov 20092 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5903 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceWorkshop on 3D Physiological Human 2009, 3DPH 2009


  • Ambulatory motion analysis
  • Gaussian process regression
  • Human pose estimation
  • Wearable inertial sensors


Dive into the research topics of 'Discriminative human full-body pose estimation from wearable inertial sensor data'. Together they form a unique fingerprint.

Cite this