Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/19317
Title: Smartglove : a multi-finger sensing system based on optical linear encoder
Authors: Li, Kang
Keywords: DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
Issue Date: 2009
Source: Li, K. (2009). Smartglove : a multi-finger sensing system based on optical linear encoder. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: Objective measures of human hands as individuals participate in everyday activities are needed in order to expand the dexterous use of the hand or to evaluate the hand functions in rehabilitation or skill training. Data gloves for measurements of finger movements are a promising tool for this purpose. The requirements for the data glove include easy and comfortable to wear and remove, durability, cost-effectiveness and measurement repeatability and reliability. This thesis presents the design of a wearable glove-based multi-finger motion capture device (SmartGlove) with a specific focus on the development of a new optical linear encoder (OLE) with novel sensing technology. Modelling of the full hand kinematics and constraints are introduced, working principles of the OLE and the multi-point sensing method are illustrated. The OLE development and the SmartGlove construction are also presented. The OLE specially designed for this project has a compact size, light weight and low power consumption. The characterization tests also show that the OLE’s digital output has good linearity and accuracy. The first prototype of SmartGlove which uses ten OLEs to capture the flexion/extension motion of the 14 finger joints is constructed based on the multi-point sensing method. A case study for the evaluation of SmartGlove using a standard protocol shows high repeatability and reliability in both the gripped and flat hand positions compared with another four evaluated data gloves using the same protocol. Conclusively, measuring outcomes in a portable manner can provide important information for the utilization and evaluation of the hand’s motion data. Results demonstrated that SmartGlove is an important improvement in this direction as both a research and an evaluation tool for widespread use of hand motion capture.
URI: https://hdl.handle.net/10356/19317
DOI: 10.32657/10356/19317
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

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