On Natural Motion Processing using Inertial Motion Capture and Deep Learning

Human motion collected in real-world environments without instruction from researchers - or natural motion - is an understudied area of the field of motion capture that could increase the efficacy of assistive devices such as exoskeletons, robotics, and prosthetics. With this goal in mind, a natural motion dataset is presented in this thesis alongside algorithms for analyzing human motion. The dataset contains more than 36 hours of inertial motion capture data collected while the 16 participants went about their lives. The participants were not instructed on what actions to perform and interacted freely with real-world environments such as a home improvement store and a college campus. We apply our dataset in two experiments. The first is a study into how manual material handlers lift and bend at work, and what postures they tend to use and why. Workers rarely used symmetric squats and infrequently used symmetric stoops typically studied in lab settings. Instead, they used a variety of different postures that have not been well-characterized such as one-legged lifting and split-legged lifting. The second experiment is a study of how to infer human motion using limited information. We present methods for inferring human motion from sparse sensors using Transformers and Seq2Seq models. We found that Transformers perform better than Seq2Seq models in producing upper-body and full-body motion, but that each model can accurately infer human motion for a variety of postures like sitting, standing, kneeling, and bending given sparse sensor data.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2020

Author dc:creator

• Geissinger, John Herman

Chair dc:contributor.committeechair

• Fox, Edward A.

Committee members dc:contributor.committeemember

• Dhillon, Harpreet Singh
• Asbeck, Alan T.

• inertial motion capture
• deep learning
• ergonomics
• manual material handlers

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:25889

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/98523