Sensors for Posture and Human Motion Recognition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 28180
Special Issue Editors
Interests: motion capture; sensor technologies; digital health; machine learning; computer animation
Special Issues, Collections and Topics in MDPI journals
Interests: computer animation; physics-based modelling; computer algebra; life science informatics; hybrid modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The capture and analysis of human posture and motion has made great advances in the past decade. On the one hand, with the development of new wearable sensor technology, a broad range of motion characteristics can now be measured with wearables. In comparison to full-body motion capture, the use of wearables needs only a small amount of time and instructions for application. Hardware is much cheaper—in a consumer price range compared to professional equipment. On the other hand, the advances in AI and machine learning (ML) provide new ways to interact and gain insights into the captured data. This development on the software side has resulted in new techniques for the analysis, segmentation, classification, and recognition of human posture and motion.
These two developments allow for completely new approaches to capture and analyze human posture and motion. It is a challenge to meaningfully combine new sensor technology and the algorithmic advances in AI and ML.
This Special Issue will cover a wide range of topics around human posture and motion, including new sensor technologies to capture posture and motion; new algorithmic approaches to derive, analyze, and recognize posture and motion from sensor data; and all combinations of these techniques.
Dr. Björn Krüger
Prof. Dr. Andreas Weber
Guest Editors
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Keywords
- wearable devices
- inertial sensors
- hybrid systems
- sensor data fusion
- hybrid modeling approaches
- temporal segmentation of motions
- action classifications from sensor data
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