Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning

Bales, Dustin Bennett

Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning

dc.contributor.author	Bales, Dustin Bennett	en
dc.contributor.committeechair	Tarazaga, Pablo Alberto	en
dc.contributor.committeechair	Kasarda, Mary E.	en
dc.contributor.committeemember	Gugercin, Serkan	en
dc.contributor.department	Mechanical Engineering	en
dc.date.accessioned	2017-12-13T07:00:43Z	en
dc.date.available	2017-12-13T07:00:43Z	en
dc.date.issued	2016-06-20	en
dc.description.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.	en
dc.description.degree	Master of Science	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:7921	en
dc.identifier.uri	http://hdl.handle.net/10919/81183	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Goodwin Hall	en
dc.subject	Machine learning	en
dc.subject	Gender Classification	en
dc.subject	Sensor Reduction	en
dc.title	Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning	en
dc.type	Thesis	en
thesis.degree.discipline	Mechanical Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	masters	en
thesis.degree.name	Master of Science	en