Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection

Chi Chiu Tsang; Philip H. W. Leong; Guanglie Zhang; Chor Fung Chung; Zhuxin Dong; Guangyi Shi; Wen J. Li

doi:10.1109/ROBIO.2007.4522191

Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection

Chi Chiu Tsang, Philip H. W. Leong, Guanglie Zhang, Chor Fung Chung, Zhuxin Dong, Guangyi Shi, Wen J. Li^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Citations (Scopus)

Abstract

We have recently developed a ubiquitous digital writing instrument system based on a micro inertial measurement unit (μIMU), which consists of MEMS (microelectro-mechanical system), accelerometers and gyroscopes, to compute the position of a marker through double integration of the acceleration measured, so as to real-time record and recognize human handwriting motion in a large writing area, i.e., a large whiteboard or screen. Owing to the random errors that exist in the MEMS sensors, the accuracy of the position estimate degrades with time. Although Kalman filtering algorithm provides a good navigation tracking solution, its accuracy depends on the amount of position information given about the target. In vehicles, the global positioning system (GPS) can be used to augment an IMU with absolute position information and improve its tracking accuracy. However, due to indoor-usage and a higher accuracy requirement, the GPS is not suitable for updating a μIMU used for hand-motion tracking with absolute position information. In this paper, we propose a novel position estimation method which makes use of an electromagnetic resonance (EMR) motion detection board for position information to improve the tracking accuracy of a μIMU-based digital writing instrument. The EMR board cannot provide high resolution (only 3 cm per grid in our case) position information for a large writing area because of high construction cost and poor tracking performance. However, the combined scheme of using the μIMU and the EMR board can compensate their respective weaknesses. The EMR board can bound the μIMU position estimate error and the μIMU can provide detailed information of the handwriting trajectory for the rough locus obtained from the EMR board. Details of the estimation algorithm will be discussed and experimental results of its implementation are compared with the conventional Kalman filtering without the extra position feedback information. © 2008 IEEE.

Original language	English
Title of host publication	2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
Pages	377-381
DOIs	https://doi.org/10.1109/ROBIO.2007.4522191
Publication status	Published - 2008
Externally published	Yes
Event	2007 IEEE International Conference on Robotics and Biomimetics, ROBIO - Yalong Bay, Sanya, China Duration: 15 Dec 2007 → 18 Dec 2007

Conference

Conference	2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
Place	China
City	Yalong Bay, Sanya
Period	15/12/07 → 18/12/07

Research Keywords

Digital writing instrument
Error compensation
Human motion sensing
Kaiman filtering
MEMS μIMU

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10.1109/ROBIO.2007.4522191

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@inproceedings{dc3fcc3f265b49edbc8ee2730fe67dd6,

title = "Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection",

abstract = "We have recently developed a ubiquitous digital writing instrument system based on a micro inertial measurement unit (μIMU), which consists of MEMS (microelectro-mechanical system), accelerometers and gyroscopes, to compute the position of a marker through double integration of the acceleration measured, so as to real-time record and recognize human handwriting motion in a large writing area, i.e., a large whiteboard or screen. Owing to the random errors that exist in the MEMS sensors, the accuracy of the position estimate degrades with time. Although Kalman filtering algorithm provides a good navigation tracking solution, its accuracy depends on the amount of position information given about the target. In vehicles, the global positioning system (GPS) can be used to augment an IMU with absolute position information and improve its tracking accuracy. However, due to indoor-usage and a higher accuracy requirement, the GPS is not suitable for updating a μIMU used for hand-motion tracking with absolute position information. In this paper, we propose a novel position estimation method which makes use of an electromagnetic resonance (EMR) motion detection board for position information to improve the tracking accuracy of a μIMU-based digital writing instrument. The EMR board cannot provide high resolution (only 3 cm per grid in our case) position information for a large writing area because of high construction cost and poor tracking performance. However, the combined scheme of using the μIMU and the EMR board can compensate their respective weaknesses. The EMR board can bound the μIMU position estimate error and the μIMU can provide detailed information of the handwriting trajectory for the rough locus obtained from the EMR board. Details of the estimation algorithm will be discussed and experimental results of its implementation are compared with the conventional Kalman filtering without the extra position feedback information. {\textcopyright} 2008 IEEE.",

keywords = "Digital writing instrument, Error compensation, Human motion sensing, Kaiman filtering, MEMS μIMU",

author = "Tsang, {Chi Chiu} and Leong, {Philip H. W.} and Guanglie Zhang and Chung, {Chor Fung} and Zhuxin Dong and Guangyi Shi and Li, {Wen J.}",

year = "2008",

doi = "10.1109/ROBIO.2007.4522191",

language = "English",

isbn = "9781424417582",

pages = "377--381",

booktitle = "2007 IEEE International Conference on Robotics and Biomimetics, ROBIO",

note = "2007 IEEE International Conference on Robotics and Biomimetics, ROBIO ; Conference date: 15-12-2007 Through 18-12-2007",

}

Tsang, CC, Leong, PHW, Zhang, G, Chung, CF, Dong, Z, Shi, G & Li, WJ 2008, Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection. in 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO., 4522191, pp. 377-381, 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO, Yalong Bay, Sanya, China, 15/12/07. https://doi.org/10.1109/ROBIO.2007.4522191

Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection. / Tsang, Chi Chiu; Leong, Philip H. W.; Zhang, Guanglie et al.
2007 IEEE International Conference on Robotics and Biomimetics, ROBIO. 2008. p. 377-381 4522191.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection

AU - Tsang, Chi Chiu

AU - Leong, Philip H. W.

AU - Zhang, Guanglie

AU - Chung, Chor Fung

AU - Dong, Zhuxin

AU - Shi, Guangyi

AU - Li, Wen J.

PY - 2008

Y1 - 2008

N2 - We have recently developed a ubiquitous digital writing instrument system based on a micro inertial measurement unit (μIMU), which consists of MEMS (microelectro-mechanical system), accelerometers and gyroscopes, to compute the position of a marker through double integration of the acceleration measured, so as to real-time record and recognize human handwriting motion in a large writing area, i.e., a large whiteboard or screen. Owing to the random errors that exist in the MEMS sensors, the accuracy of the position estimate degrades with time. Although Kalman filtering algorithm provides a good navigation tracking solution, its accuracy depends on the amount of position information given about the target. In vehicles, the global positioning system (GPS) can be used to augment an IMU with absolute position information and improve its tracking accuracy. However, due to indoor-usage and a higher accuracy requirement, the GPS is not suitable for updating a μIMU used for hand-motion tracking with absolute position information. In this paper, we propose a novel position estimation method which makes use of an electromagnetic resonance (EMR) motion detection board for position information to improve the tracking accuracy of a μIMU-based digital writing instrument. The EMR board cannot provide high resolution (only 3 cm per grid in our case) position information for a large writing area because of high construction cost and poor tracking performance. However, the combined scheme of using the μIMU and the EMR board can compensate their respective weaknesses. The EMR board can bound the μIMU position estimate error and the μIMU can provide detailed information of the handwriting trajectory for the rough locus obtained from the EMR board. Details of the estimation algorithm will be discussed and experimental results of its implementation are compared with the conventional Kalman filtering without the extra position feedback information. © 2008 IEEE.

AB - We have recently developed a ubiquitous digital writing instrument system based on a micro inertial measurement unit (μIMU), which consists of MEMS (microelectro-mechanical system), accelerometers and gyroscopes, to compute the position of a marker through double integration of the acceleration measured, so as to real-time record and recognize human handwriting motion in a large writing area, i.e., a large whiteboard or screen. Owing to the random errors that exist in the MEMS sensors, the accuracy of the position estimate degrades with time. Although Kalman filtering algorithm provides a good navigation tracking solution, its accuracy depends on the amount of position information given about the target. In vehicles, the global positioning system (GPS) can be used to augment an IMU with absolute position information and improve its tracking accuracy. However, due to indoor-usage and a higher accuracy requirement, the GPS is not suitable for updating a μIMU used for hand-motion tracking with absolute position information. In this paper, we propose a novel position estimation method which makes use of an electromagnetic resonance (EMR) motion detection board for position information to improve the tracking accuracy of a μIMU-based digital writing instrument. The EMR board cannot provide high resolution (only 3 cm per grid in our case) position information for a large writing area because of high construction cost and poor tracking performance. However, the combined scheme of using the μIMU and the EMR board can compensate their respective weaknesses. The EMR board can bound the μIMU position estimate error and the μIMU can provide detailed information of the handwriting trajectory for the rough locus obtained from the EMR board. Details of the estimation algorithm will be discussed and experimental results of its implementation are compared with the conventional Kalman filtering without the extra position feedback information. © 2008 IEEE.

KW - Digital writing instrument

KW - Error compensation

KW - Human motion sensing

KW - Kaiman filtering

KW - MEMS μIMU

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-49249106596&origin=recordpage

U2 - 10.1109/ROBIO.2007.4522191

DO - 10.1109/ROBIO.2007.4522191

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424417582

SP - 377

EP - 381

BT - 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO

T2 - 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO

Y2 - 15 December 2007 through 18 December 2007

ER -

Handwriting tracking based on coupled μIMU/electromagnetic resonance motion detection

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