ArticleSensors (Basel, Switzerland)2015
Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Commercial Smart Gloves: Current Status and Applications.Sensors (Basel, Switzerland) · 2021Pooled it
- Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion.Sensors (Basel, Switzerland) · 2022Article
- Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB.PeerJ. Computer science · 2021Article
- A Model-Based System for Real-Time Articulated Hand Tracking Using a Simple Data Glove and a Depth Camera.Sensors (Basel, Switzerland) · 2019Article
- Article
- Wearable Stretch Sensors for Motion Measurement of the Wrist Joint Based on Dielectric Elastomers.Sensors (Basel, Switzerland) · 2017Article
- Sensor Fusion and Smart Sensor in Sports and Biomedical Applications.Sensors (Basel, Switzerland) · 2016Review
- Are We Ready to Build a System for Assisting Blind People in Tactile Exploration of Bas-Reliefs?Sensors (Basel, Switzerland) · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vision based interfaces for human computer interaction have gained increasing attention over the past decade. This study presents a data fusion approach of the Nimble VR vision based system, using the Kinect camera, with the contact based 5DT Data Glove. Data fusion was achieved through a Kalman filter. The Nimble VR and filter output were compared using measurements performed on (1) a wooden hand model placed in various static postures and orientations; and (2) three differently sized human hands during active finger flexions. Precision and accuracy of joint angle estimates as a function of hand posture and orientation were determined. Moreover, in light of possible self-occlusions of the fingers in the Kinect camera images, data completeness was assessed. Results showed that the integration of the Data Glove through the Kalman filter provided for the proximal interphalangeal (PIP) joints of the fingers a substantial improvement of 79% in precision, from 2.2 deg to 0.9 deg. Moreover, a moderate improvement of 31% in accuracy (being the mean angular deviation from the true joint angle) was established, from 24 deg to 17 deg. The metacarpophalangeal (MCP) joint was relatively unaffected by the Kalman filter. Moreover, the Data Glove increased data completeness, thus providing a substantial advantage over the sole use of the Nimble VR system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.