Date of Award

Spring 2024

Access Restriction

Thesis

Degree Name

Master of Science

Department

Computer Science

School or College

Seaver College of Science and Engineering

First Advisor

Delaram Yazdansepas, Ph.D.

Second Advisor

John David N. Dionisio, Ph.D.

Third Advisor

Mandy B. Korpusik, Ph.D.

Abstract

Sensor-based human activity recognition has become an important research field within pervasive and ubiquitous computing. Techniques for recognizing atomic activities such as gestures or actions are mature for now, but complex activity recognition still remains a challenging issue. I was a candidate in an activity classification thesis. It collected 4 activities, which included walking on the sidewalk for a set distance, walking up and down a set of stairs, walking on the treadmill at 2.5 mph for 2 minutes, and jogging on the treadmill at 5.5 mph for 1 minute. It took 30 minutes to collect one candidate data. If complex activity data can be made up with atomic activities data, the data collecting process will be simplified. In this thesis, I used methods to mimic a complex activity shapelet by combing atomic activity shapelets. I first collect two candidates walk, jump and skip time series data, in which walk and jump are considered the atomic activities of skip. Time series patterns, shapelets, are extracted using tsshapelet package. Shapelets are small sub-series, or parts of the time-series, that are informative or discriminative for a certain class. They can be used to transform the time-series to features by calculating the distance for each of the time-series you want to classify to a shapelet. In order to create skip representative shapelet, Barycenter Dynamic Time Warping and Weighted Dynamic Time Warping are used to average walk and jump shapelet, and then compare the euclidean distance between skip shapelet with walk shapelet, jump shapelet and, combined-shapelet. Experimental result show that the combined-shapelet is closer to skip shapelet than single walk or jump shapelet. Then I use three evaluation methods to mathematically and statistically show that combined-shapelet and real skip shapelet are similar. Evaluation methods include sliding window, cycle comparison and random comparison. To verify whether combined-shapelet can substitute real skip shapelet, a new labeled time series data is introduced, the result shows that both shapelets have the label accuracy around 70%, accuracy difference is less than 1%.

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