Research Article
A smart wireless inertial measurement unit system: Simplifying & encouraging usage of WIMU technology
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2011.245996, author={Mark Gaffney and Michael Walsh and Se\^{a}n O’Connell and Binyu Wang and Brendan O’Flynn and Cian \^{O} Math\^{u}na}, title={A smart wireless inertial measurement unit system: Simplifying \& encouraging usage of WIMU technology}, proceedings={5th International ICST Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2012}, month={4}, keywords={Inertial Measurement Ubiquitous Computing}, doi={10.4108/icst.pervasivehealth.2011.245996} }- Mark Gaffney
Michael Walsh
Seán O’Connell
Binyu Wang
Brendan O’Flynn
Cian Ó Mathúna
Year: 2012
A smart wireless inertial measurement unit system: Simplifying & encouraging usage of WIMU technology
PERVASIVEHEALTH
ICST
DOI: 10.4108/icst.pervasivehealth.2011.245996
Abstract
Wireless Inertial Measurement Units (WIMUs) combine motion sensing, processing & communications functions in a single device. Data gathered using these sensors has the potential to be converted into high quality motion data. By outfitting a subject with multiple WIMUs full motion data can be gathered. With a potential cost of ownership several orders of magnitude less than traditional camera based motion capture, WIMU systems have potential to be crucially important in supplementing or replacing traditional motion capture and opening up entirely new application areas and potential markets particularly in the rehabilitative, sports & at-home healthcare spaces. Currently WIMUs are underutilized in these areas. A major barrier to adoption is perceived complexity. Sample rates, sensor types & dynamic sensor ranges may need to be adjusted on multiple axes for each device depending on the scenario. As such we present an advanced WIMU in conjunction with a Smart WIMU system to simplify this aspect with 3 usage modes: Manual, Intelligent and Autonomous. Attendees will be able to compare the 3 different modes and see the effects of good and bad set-ups on the quality of data gathered in real time.
