This investigation is centred on suggesting a new wardrobe concept of convertible hybrid garments; 2-in-1 designs. The proposed work is in other words, build on the transformative merging of one garment type to another. Through this intertwining, these become two parts of a whole, distorted to fit together in an upside-down position on the body. This alternative approach to garment creation, not only challenges the fundamental relationship between clothing, pattern making, and the body, but also aims to suggest the potential of implementing ‘reverse engineering’ methods, as a backdrop for the contemporary and versatile deconstruction. This work is foremost motivated by a sense of social and sustainable contribution to the field of fashion. Evoked by the prospect of encouraging interaction and providing the wearer more options on how to wear clothing, by proposing designs not fixed to one outcome. Hence, given the versatility of these hybrids, this project also advocates having fewer items of clothing, to bring about a more sustainable alternative to mass consumption.

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This dissertation is comprised of three different studies researching user perception of comfort when using wearable technology. The first study investigated the use of altered smart glasses to study comfort, preference, and performance while executing common logistical order picking and shipment putting tasks. The impact of design type (weighted front, side, or back) was investigated using comfort rating scales (CRS). There was no significant difference in device preference regardless of task type. Despite the side weighted arrangement being the most comfortable, the participants still felt uncomfortable. The second study explored modifying the weights to the six dimensions of the CRS to create a comfort score. There was a strong correlation between the weighted and unweighted comfort score. Participants identified Harm as the most important dimension. The results suggest that the participants valued importance did not make a difference in the comfort score. The final study examined the use of a wand scanner and two wearable devices to study comfort and performance while executing common logistical shipment putting tasks. The impact of the wearables was investigated using the CRS. Participants identified the ring and wand scanner to be the most comfortable and the glasses as the least comfortable device. The CRS scores showed that participants became more uncomfortable using the smart glasses over time during the completion of the putting task. These three studies provided insight for industry from a comfort perspective that will be helpful when trying to incorporate wearable technology in the work place.

As individual health monitors continue to become more widely adopted in helping individuals make informed decisions, new technologies need to be developed to obtain more biometric data. Spectroscopy is a well-known tool to gain biological information. Traditionally spectrometers are large and expensive making personal or wearable health monitors difficult. Here we present the development and characterization of a miniaturized short wavelength infrared spectrometer for wearable applications. We present a carbon nanotube parallel hole collimator can effectively select a narrow set of allowed angles of light to be separated by a linear variable filter and detected at a photodiode array making a spectrometer. We will go over the calibration of the spectrometer showing a resolution of 13 nm at 1300 nm. Improvements on the original collimator data will be discussed, including carbon nanotube growth without infiltration and growth on transparent substrates. We will also show measurements made on human subjects yielding a pulse.

OBJECTIVE: Using preliminary data from the Binge-Eating Genetics Initiative (BEGIN), we evaluated the feasibility of delivering an eating disorder digital app, Recovery Record, through smartphone and wearable technology for individuals with binge-type eating disorders. METHODS: Participants (n = 170; 96% female) between 18 and 45 years old with lived experience of binge-eating disorder or bulimia nervosa and current binge-eating episodes were recruited through the Recovery Record app. They were randomized into a Watch (first-generation Apple Watch + iPhone) or iPhone group; they engaged with the app over 30 days and completed baseline and endpoint surveys. Retention, engagement, and associations between severity of illness and engagement were evaluated. RESULTS: Significantly more participants in the Watch group completed the study (p = .045); this group had greater engagement than the iPhone group (p's < .05; pseudo-R effect size = .01-.34). Overall, binge-eating episodes, reported for the previous 28 days, were significantly reduced from baseline (mean = 12.3) to endpoint (mean = 6.4): most participants in the Watch (60%) and iPhone (66%) groups reported reduced binge-eating episodes from baseline to endpoint. There were no significant group differences across measures of binge eating. In the Watch group, participants with fewer episodes of binge eating at baseline were more engaged (p's < .05; pseudo-R = .01-.02). Engagement did not significantly predict binge eating at endpoint nor change in binge-eating episodes from baseline to endpoint for both the Watch and iPhone groups. DISCUSSION: Using wearable technology alongside iPhones to deliver an eating disorder app may improve study completion and app engagement compared with using iPhones alone.

In this study, inertial measurement units (IMUs) were used to train a random forest classifier to correctly classify different jump types in volleyball. Athlete motion data were collected in a controlled setting using three IMUs, one on the waist and one on each ankle. There were 11 participants who at the time played volleyball at the collegiate level in the United States, seven male and four female. Each performed the same number of jumps across the eight jump types--five BASIC jumps and three each of the other seven--resulting in 26 jumps per subject for a total of 286. The data were processed using a max-bin method and trained using a leave-one-out cross-validation method to produce a classifier that can determine jump type with an accuracy of 0.967, as measured by an F1-score.

Brain computer interfaces (BCIs) are devices capable of relaying information directly from the brain to a digital device. BCIs have been proposed for a diverse range of clinical and commercial applications; for example, to allow paralyzed subjects to communicate, or to improve machine human interactions. At their core, BCIs need to predict the current state of the brain from variables measuring functional physiology. Functional near infrared spectroscopy (fNIRS) is a non-invasive optical technology able to measure hemodynamic changes in the brain. Along with electroencephalography (EEG), fNIRS is the only technique that allows non-invasive and portable sensing of brain signals. Portability and wearability are very desirable characteristics for BCIs, as they allow them to be used in contexts beyond the laboratory, extending their usability for clinical and commercial applications, as well as for ecologically valid research. Unfortunately, due to limited access to the brain, non-invasive BCIs tend to suffer from low accuracy in their estimation of the brain state. It has been suggested that feedback could increase BCI accuracy as the brain normally relies on sensory feedback to adjust its strategies. Despite this, presenting relevant and accurate feedback in a timely manner can be challenging when processing fNIRS signals, as they tend to be contaminated by physiological and motion artifacts. In this dissertation, I present the hardware and software solutions we proposed and developed to deal with these challenges. First, I will talk about ninjaNIRS, the wearable open source fNIRS device we developed in our laboratory, which could help fNIRS neuroscience and BCIs to become more accessible. Next, I will present an adaptive filter strategy to recover the neural responses from fNIRS signals in real-time, which could be used for feedback and classification in a BCI paradigm. We showed that our wearable fNIRS device can operate autonomously for up to three hours and can be easily carried in a backpack, while offering noise equivalent power comparable to commercial devices. Our adaptive multimodal Kalman filter strategy provided a six-fold increase in contrast to noise ratio of the brain signals compared to standard filtering while being able to process at least 24 channels at 400 samples per second using a standard computer. This filtering strategy, along with visual feedback during a left vs right motion imagery task, showed a relative increase of accuracy of 37.5% compared to not using feedback. With this, we show that it is possible to present relevant feedback for fNIRS BCI in real-time. The findings on this dissertation might help improve the design of future fNIRS BCIs, and thus increase the usability and reliability of this technology.