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• W1926530485 abstract "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." @default.
• W1926530485 created "2016-06-24" @default.
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• W1926530485 date "2015-06-01" @default.
• W1926530485 modified "2023-09-26" @default.
• W1926530485 title "Real-time arm tracking for HMI applications" @default.
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• W1926530485 doi "https://doi.org/10.1109/bsn.2015.7299391" @default.
• W1926530485 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8142556" @default.
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