Mico Woolard, PhD student at VTSIL, and Dr. Tarazaga will join a group from VT traveling to the Hannover Messe Expo 2016, the world’s largest trade show for industrial technology. Dr. Tarazaga is scheduled to present a talk entitled "Smart Buildings in the Age of IoT: How Virginia Tech is Inviting the Future." We look for a successful outcome in new contacts, sponsors, and partners as well as the chance to share what we are doing on a worldwide platform. You can read more details about this at the VT website here. |
Mico Woolard and Dr. Tarazaga head out to represent VT and VTSIL at the Hannover Messe Expo 2016!4/21/2016
Dustin Bales successfully defended his master thesis entitled "Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning." His work has multiple applications in soft biometrics, specialized marketing and security and threat detection. Great job Dustin! Abstract: The ability to classify occupants in a building has far-reaching applications in security, monitoring human health, and managing energy resources effectively. In this work, gender and weight of walkers are classified via machine learning or pattern recognition techniques. Accelerometers mounted beneath the floor of Virginia Tech’s Goodwin Hall measured walkers’ gait. These acceleration measurements serve as the inputs to machine learning techniques allowing for classification. For this work, the gait of fifteen individual walkers was recorded via fourteen accelerometers as they, alone, walked down the instrumented hallway, in multiple trials. These machine learning algorithms produce an 88 % accurate model for gender classification. The machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64 % accuracy. The data reduction techniques are Discrete Empirical Interpolation Method (DEIM), Q-DEIM, and Projection Coefficients. A two- part methodology is proposed to implement the approach completed in this thesis work. The first step validates the algorithm design choices, i.e. using bagged or boosted decision trees for classification. The second step reduces the walking data measured to truncate accelerometers which do not aid in increasing characteristic classification. Candidate Biography: Dustin conducts research at Virginia Tech as part of the Smart Infrastructure Laboratory. He is a member of GAiTE LLC which is working to transition the technology developed in the Smart Infrastructure Lab to consumer markets. Candidate Education: Bachelors of Science in Mechanical Engineering, 2014 University of Wyoming, Laramie, WY |
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