This thesis aims to find opportunities to support communication between new mothers to foster a sense of connectedness, during a pandemic where social interactions such as parent groups are unavailable. The project is based on research that shows that new parents have a great need for social support. Due to physical distancing, there was room for interaction design to intervene by exploring tangible solutions to develop innovative custom design. Through a user-centered design process, including findings from theory, interviews and design workshops with the target group, the study resulted in a co-created design proposal of a communication device in the form of a wearable wristband. The proposal showed positive indications in meeting the needs and desires of mothers found during a co-design workshop.

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We are seeing a revolution in computing and artificial intelligence; intelligent machines have become ingrained in and improved every aspect of our lives. Despite the increasing number of intelligent devices and breakthroughs in artificial intelligence, we have yet to achieve truly intelligent environments. Audio is one of the most common sensing and actuation modalities used in intelligent devices. In this thesis, we focus on how we can more robustly integrate audio intelligence into a wide array of resource-constrained platforms that enable more intelligent environments. We present systems and methods for adaptive audio filtering that enables us to more robustly embed acoustic intelligence into a wide range of real time and resource-constrained mobile, embedded, and cyber-physical systems that are adaptable to a wide range of different applications, environments, and scenarios. First, we introduce methods for embedding audio intelligence into wearables, like headsets and helmets, to improve pedestrian safety in urban environments by using sound to detect vehicles, localize vehicles, and alert pedestrians well in advance to give them enough time to avoid a collision. We create a segmented architecture and data processing pipeline that partitions computation between embedded front-end platform and the smartphone platform. The embedded front-end hardware platform consists of a microcontroller and commercial-off-the shelf (COTS) components embedded into a headset and samples audio from an array of four MEMS microphones. Our embedded front-end platform computes a series of spatiotemporal features used to localize vehicles: relative delay, relative power, and zero crossing rate. These features are computed in the embedded front-end headset platform and transmitted wirelessly to the smartphone platform because there is not enough bandwidth to transmit more than two channels of raw audio with low latency using standard wireless communication protocols, like Bluetooth Low-Energy. The smartphone platform runs machine learning algorithms to detect vehicles, localize vehicles, and alert pedestrians. To help reduce power consumption, we integrate an application specific integrated circuit into our embedded front-end platform and create a new localization algorithm called angle via polygonal regression (AvPR) that combines the physics of audio waves, the geometry of a microphone array, and a data driven training and calibration process that enables us to estimate the high resolution direction of the vehicle while being robust to noise resulting from movements in the microphone array as we walk the streets. Second, we explore the challenges in adapting our platforms for pedestrian safety to more general and noisier scenarios, namely construction worker safety sounds of nearby power tools and machinery that are orders of magnitude greater than that of a distant vehicle. We introduce an adaptive noise filtering architecture that allows workers to filter out construction tool sounds and reveal low-energy vehicle sounds to better detect them. Our architecture combines the strengths of both the physics of audio waves and data-driven methods to more robustly filter out construction sounds while being able to run on a resource-limited mobile and embedded platform. In our adaptive filtering architecture, we introduce and incorporate a data-driven filtering algorithm, called probabilistic template matching (PTM), that leverages pre-trained statistical models of construction tools to perform content-based filtering. We demonstrate improvements that our adaptive filtering architecture brings to our audio-based urban safety wearable in real construction site scenarios and against state-of-art audio filtering algorithms, while having a minimal impact on the power consumption and latency of the overall system. We also explore how these methods can be used to improve audio privacy and remove privacy-sensitive speech from applications that have no need to detect and analyze speech. Finally, we introduce a common selective audio filtering platform that builds upon our adaptive filtering architecture for a wide range of real-time mobile, embedded, and cyber-physical applications. Our architecture can account for a wide range of different sounds, model types, and signal representations by integrating an algorithm we present called content-informed beamforming (CIBF). CIBF combines traditional beamforming (spatial filtering using the physics of audio waves) with data driven machine learning sound detectors and models that developers may already create for their own applications to enhance and filter out specified sounds and noises. Alternatively, developers can also select sounds and models from a library we provide. We demonstrate how our selective filtering architecture can improve the detection of specific target sounds and filter out noises in a wide range of application scenarios. Additionally, through two case studies, we demonstrate how our selective filtering architecture can easily integrate into and improve the performance of real mobile and embedded applications over existing state-of-art solutions, while having minimal impact on latency and power consumption. Ultimately, this selective filtering architecture enables developers and engineers to more easily embed robust audio intelligence into common objects found around us and resource-constrained systems to create more intelligent environments.

The objective of this study was to assess the biomechanical and subjective measures of elementary school teachers while wearing active and/or passive wearable devices during the average workday. Five elementary school teachers wore a harness that held an Upright GO 2 posture tracking device and a Vicon Blue Trident sensor on the participant's upper back for two school days. Haptic feedback was on for one day and off for the other. Data from the Vicon wearable was analyzed to determine participants’ trunk flexion severity, frequency, and duration. Surveys were used to determine perceived exertion and perception of wearable technology. This study proved that teachers are undergoing severe trunk flexion throughout the day; however, there was not consistent improvement in trunk flexion when haptic feedback was applied. Results also indicated that perceived exertion levels of teachers did not always correlate to the frequency of trunk flexion measured through the wearable device.

Fundamentally, interaction design is about getting in between: Between people and what is important to them. While it often makes sense to make mediated interactions explicit, implicit interactions can integrate harmoniously into the here and now and happen almost unnoticed. In this master’s thesis, I explore different approaches to harmoniously integrate interactions with mobile and wearable devices into cycling. Cycling can be treated as a modality. It can influence the behaviour of the operating system or provide considerations for the design of applications. Rather than adding to the existing flow of signals, I propose to tap into that flow and even take it a step further: I explore ways to add meaning to what people are already doing as if there were no interface. Wearable devices, such as the current Apple Watch, can provide comprehensive insight into ongoing activities. The name "smartwatch" suggests that this device is meant to be attended to, but the greater opportunity lies in sensing and adding meaning to what is already happening. This allows people to immerse themselves in their ‘natural’ environment with their heads up and their hands free.

Addressing the shortage of healthcare professionals is critical for improving access to safe surgery in low- and middle-income countries. Patient safety precautions in surgical training include restricting the number of trainees present in the operating room, imposing limitations on how many surgeons can be trained simultaneously. The use of head-mounted cameras is suggested as a means to enhance and make surgical training more accessible by enabling remote viewing of surgical procedures from the surgeon's point-of-view. This research explores healthcare professionals' acceptance of incorporating a head-mounted smart camera as a complementary tool in surgical training. The case study, conducted during three surgical training workshops in Kenya and Ethiopia, utilized a modified UTAUT model. Questionnaires and five semi-structured interviews were employed, revealing a high level of technology acceptance. While some drawbacks were noted, overall positivity was expressed. The smart camera did in many ways align well with the needs of surgeons and anesthesiologists. Challenges mainly revolved around facilitating conditions such as poor connectivity and costs. Although the smart camera in many aspects enhanced the efficiency of surgical training, the findings highlight its inability to fully replace direct observation in the operating room.

Childhood obesity is a foremost concern throughout the health care community. Approximately 17.6% of the pediatric population meet the criteria for obesity, which can lead to health disparities later in life, such as hypertension, type 2 diabetes mellitus, and metabolic syndrome. Emerging mobile and wearable lifestyle tracking devices can be a viable solution to the challenging problem of childhood obesity through behavior changes, feasibility, and adherence. The purpose of this literature review was to determine the effect that mobile and wearable activity tracking devices have on the obese pediatric population. A centralized review of the literature was conducted using various data basesand resulted in 19 articles. 5 articles were chosen to review in more detail. 13 other articles were hand searched through credible resource citations, rendering 14 articles that met all criteria. The three general themes found in this literature review suggest that wearable activity tracking devices can be designed and effectively used by the pediatric population. Also, wearable activity tracking devices are accurate in conveying information on physical activity, calories, and heart rate. Lastly, wearable activity tracking devices can initiate behavioral changes in children leading to an increase in physical activity, resulting in the prevention and treatment of pediatric obesity.While in a majority of the studies analyzed trails were short. The research suggests wearable activity tracking devices will produce the desired results of increased activity in pediatric populations when they are worn correctly, are adequately engaging, and when they are designed in a feasible manner that is appealing to children.

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Wearable technology has made a positive impact in the consumer industry with its focus on adult fitness. Devices and applications are pervasive, inexpensive and are in high demand. Our nation struggles with obesity and health concerns related to poor fitness. However, the research on such technology has been more focused on adults. Therefore, the need to investigate wearable technology for fitness improvement with children is essential. Children lead increasingly sedentary lifestyles through TV watching, technology-use and a reduction in physical activities. Further, our society is exposed to quick food loaded with calories. These factors contribute to the growing epidemic of childhood obesity. The need to educate students early, on their ability to monitor their fitness, is the focus of this research. This dissertation investigated the impact of an integrated technology-enhanced physical education model with 127 fifth grade students over an 11-week period. A detailed analysis, looking at theoretical perspectives across multiple data collections was conducted. This study answered the questions, 1. To what extent can students improve their performance with technology-enhanced physical education? 2. To what extent can students learn to self-monitor their performance levels? How do affective components impact teaching and learning with a technology-enhanced physical education model? Results showed that technology-enhanced physical education does improve performance measures, does improve students' ability to self-regulate and positively impacts student and teachers' affective states. However long term results were inconclusive, stimulating multiple, potential opportunities for continued research.

This thesis explores a concept by Sonvox AB called VoiceUp. The concept is that of a mobile application that communicates with a wearable voice measuring device and visualizes voice information in a way that helps singers. Sonvox current main product is VoxLog—a system for long-term voice monitoring, mostly used for research purposes. Sonvox believes that their voice analysis technology could be relevant to a larger audience. The main goal for the thesis is to explore if a wearable voice measuring device could be relevant to singers, and in what ways. To do this, a needs analysis was conducted where song teachers and singers were interviewed. In order to draw statistical conclusions about the occurrence of needs, a survey was conducted where people with an interest for singing were the targeted respondents. Based on the result from the needs analysis, the VoiceUp concept was refined, resulting in an idea of a product that measures and visualizes how much the user sings and speaks with the aim to increase singers motivation to practice singing more regularly. Based on theory related to self-tracking, a design proving the concept was created, resulting in a mockup and a simple prototype. The mockup and the prototype can together be seen as one example of how self-tracking technology could be relevant to singers.