TY - GEN
T1 - Real-time arm tracking for HMI applications
AU - Masters, Matthew
AU - Osborn, Luke
AU - Thakor, Nitish
AU - Soares, Alcimar
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/15
Y1 - 2015/10/15
N2 - Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.
AB - Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.
UR - http://www.scopus.com/inward/record.url?scp=84961583749&partnerID=8YFLogxK
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U2 - 10.1109/BSN.2015.7299391
DO - 10.1109/BSN.2015.7299391
M3 - Conference contribution
AN - SCOPUS:84961583749
T3 - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
BT - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Y2 - 9 June 2015 through 12 June 2015
ER -