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All too often, industrial designers face seemingly intractable obstacles as they endeavor to, as Simon (1996, p. 111) describes, devise "courses of action aimed at changing existing situations into preferred ones." These problems, described by Rittel and Webber (1973) as "wicked," are insurmountable due to the contradictory and changing nature of their requirements. I argue that that industrial design (ID) is largely subject to Rittel's quandary because of its penchant for producing single solutions for large populations; such design solutions are bound, in some senses, to fail due to the contradictory and changing nature of large and, thus, inherently diverse populations. This one-size-fits-all approach is not a necessary attribute of ID, rather, it is a consequence of the time in which it came into being, specifically, the period of industrial mass production. Fortunately, new, agile manufacturing techniques, inexpensive sensors, and machine learning provide an alternative course for ID to take, but it requires a new way of thinking and it requires a new set of methods, which I will elaborate in this thesis. According to Duguay, Landry, and Pasin (1997), we are entering an age where it will be feasible to produce individualized, one-off products from large-scale industrial manufacturing facilities in a way that is not only cost effective, but in many ways as cost effective as the existing techniques of mass production. By availing ourselves of these opportunities, we can tame the problem, not by defeating Rittel's logic, rather by reducing the extent to which his theories are appropriate to the domain of ID. This thesis also describes a test study: an experiment whose design was guided by the proposed design methodologies. The goal of the experiment was to determine the feasibility of a noninvasive system for measuring the health of the forearm muscles. Such a tool would provide the basis for assessing the true impact and possible pathogeny of the manual use of products or modifications to products. Previously, it was considered impossible to use surface electromyography (as opposed to needle or wire based electromyography) to assess muscular activity and muscular health due to the complexity of the arrangement of muscles in the forearm. Attempts to overcome this problem have failed because they have tried to create a single solution for all people. My hypothesis is that, by designing for each individual, a solution may be found. Specifically, I show that, for any given individual, there is a high correlation between the EMG signal and the movements of the fingers that, ostensibly, those muscles control. In other words, by knowing, with great accuracy, the position and the motion of the hand then it would become possible to disambiguate the mixed signals coming from the complex web of muscles in the forearm and enable the assessment of the forearm's health by non-invasive means.
Bioscience High School, a small magnet high school located in Downtown Phoenix and a STEAM (Science, Technology, Engineering, Arts, Math) focused school, has been pushing to establish a computer science curriculum for all of their students from freshman to senior year. The school's Mision (Mission and Vision) is to: "..provide a rigorous, collaborative, and relevant academic program emphasizing an innovative, problem-based curriculum that develops literacy in the sciences, mathematics, and the arts, thus cultivating critical thinkers, creative problem-solvers, and compassionate citizens, who are able to thrive in our increasingly complex and technological communities." Computational thinking is an important part in developing a future problem solver Bioscience High School is looking to produce. Bioscience High School is unique in the fact that every student has a computer available for him or her to use. Therefore, it makes complete sense for the school to add computer science to their curriculum because one of the school's goals is to be able to utilize their resources to their full potential. However, the school's attempt at computer science integration falls short due to the lack of expertise amongst the math and science teachers. The lack of training and support has postponed the development of the program and they are desperately in need of someone with expertise in the field to help reboot the program. As a result, I've decided to create a course that is focused on teaching students the concepts of computational thinking and its application through Scratch and Arduino programming.
Electromyography (EMG) and Electroencephalography (EEG) are techniques used to detect electrical activity produced by the human body. EMG detects electrical activity in the skeletal muscles, while EEG detects electrical activity from the scalp. The purpose of this study is to capture different types of EMG and EEG signals and to determine if the signals can be distinguished between each other and processed into output signals to trigger events in prosthetics. Results from the study suggest that the PSD estimates can be used to compare signals that have significant differences such as the wrist, scalp, and fingers, but it cannot fully distinguish between signals that are closely related, such as two different fingers. The signals that were identified were able to be translated into the physical output simulated on the Arduino circuit.