Work

Real-time arm tracking for HMI applications

Year: 2015

Type: conference-paper

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.

Source: Wearable and Implantable Body Sensor Networks

DOI: 10.1109/bsn.2015.7299391

Authors: Matthew Masters, Luke Osborn, Nitish Thakor, Alcimar Soares

Institutions: Johns Hopkins University, Universidade Federal de Uberlândia

Language: English

Topic: Inertial Sensor and Navigation

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Johns Hopkins University, National Institute of Neurological Disorders and Stroke

Award: An Intuitive Prosthetic System for Upper Extremity Amputees