Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.

In this thesis, I explored the interconnected ways in which human experience can shape and be shaped by environments of the future, such as interactive environments and spaces, embedded with sensors, enlivened by advanced algorithms for sensor data processing. I have developed an abstract representational experience into the vast and continual journey through life that shapes how we can use sensory immersion. The experimental work was housed in the iStage: an advanced black box space in the School of Arts, Media, and Engineering, which consists of video cameras, motion capture systems, spatial audio systems, and controllable lighting and projector systems. The malleable and interactive space of the iStage transformed into a reflective tool in which to gain insight into the overall shared, but very individual, emotional odyssey. Additionally, I surveyed participants after engaging in the experience to better understand their perceptions and interpretations of the experience. With the responses of participants' experiences and collective reflection upon the project I can begin to think about future iterations and how they might contain applications in health and/or wellness.