Low-Cost 3-D Positioning System Based on SEMG and MIMU

With the development of the ubiquitous pedestrian position in environments where global navigation satellite system is unavailable or there is no preinstalled wireless device for positioning, the use of self-contained sensors becomes more important. Thus, this paper proposed a 3-D pedestrian position system combined with commercial microelectromechanical systems microinertial measurement unit (MIMU) and surface electromyography (SEMG) sensor. The signal of the SEMG is used mainly in two aspects: 1) to improve the recognition rate of motion pattern and 2) to obtain the more accurate 3-D velocity contrasted to MIMU. Therefore, satisfied 3-D position accuracy is obtained in two different terrain environments.

[1]  Lei Wang,et al.  A Self-Calibration Method for Accelerometer Nonlinearity Errors in Triaxis Rotational Inertial Navigation System , 2017, IEEE Transactions on Instrumentation and Measurement.

[2]  Augusto Luis Ballardini,et al.  An Indoor Localization System for Telehomecare Applications , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Weihua Sheng,et al.  Design and Evaluation of a Teleoperated Robotic 3-D Mapping System using an RGB-D Sensor , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  Thomas B. Schön,et al.  Magnetometer Calibration Using Inertial Sensors , 2016, IEEE Sensors Journal.

[5]  Min Li,et al.  The Analysis of Pedestrian’s Motion State Based on the Entropy of sEMG , 2016 .

[6]  Aboelmagd Noureldin,et al.  Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices , 2016, IEEE Transactions on Instrumentation and Measurement.

[7]  Alessio De Angelis,et al.  A magnetic ranging aided dead-reckoning indoor positioning system for pedestrian applications , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[8]  Teresa Orlowska-Kowalska,et al.  An on-line trained neural controller with a fuzzy learning rate of the Levenberg-Marquardt algorithm for speed control of an electrical drive with an elastic joint , 2015, Appl. Soft Comput..

[9]  Mark G. Petovello,et al.  Measuring GNSS Multipath Distributions in Urban Canyon Environments , 2015, IEEE Transactions on Instrumentation and Measurement.

[10]  Liu Le Lower limb locomotion-mode identification based on multi-source information and particle swarm optimization algorithm , 2015 .

[11]  James Pinchin,et al.  The potential of electromyography to aid personal navigation , 2014 .

[12]  Gareth P. Bailey,et al.  Assessment of Foot Kinematics During Steady State Running Using a Foot-mounted IMU , 2014 .

[13]  Yuwei Chen,et al.  Electromyography-Based Locomotion Pattern Recognition and Personal Positioning Toward Improved Context-Awareness Applications , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Zhu Haigang EMG signal recognition based on EMD sample entropy , 2013 .

[15]  K. V. S. Hari,et al.  Foot-mounted INS for everybody - an open-source embedded implementation , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[16]  Viswanath Talasila,et al.  Calibration of 3-axis Magnetometers , 2012 .

[17]  Michal Prauzek,et al.  Built-in Smartphone Accelerometer Motion Pattern Recognition Using Wavelet Transform , 2012, CISIS/ICEUTE/SOCO Special Sessions.

[18]  Kongqiao Wang,et al.  A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Yuwei Chen,et al.  Sensing strides using EMG signal for pedestrian navigation , 2011 .

[20]  Yuwei Chen,et al.  Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning , 2011, Journal of Navigation.

[21]  Yang Qinghua sEMG signal analysis method and its application in rehabilitation medicine , 2011 .

[22]  Wan Yan-hui Multiposition calibration method of laser gyro SINS , 2011 .

[23]  Zhang Yan-hua The New Advancement and Trend of Inertial Navigation Technology , 2008 .

[24]  Siti Anom Ahmad,et al.  Moving approximate entropy applied to surface electromyographic signals , 2008, Biomed. Signal Process. Control..