Wearable devices, which track a subject’s activity (e.g. steps, calories, intensity) over time, have become a popular option for research studies which seek to better understand an individual’s physical activity in the day-to-day setting. This thesis looks to address three common problems within the wearable device setting; how to address missing data and incomplete wear time, what to do when large outlying values are present, and how many observation days are required to reasonably estimate various activity metrics of interest. Given the dense nature of observations from such devices, functional data analysis (FDA) provides a natural framework for analysis, and we seek to address the first problem related to missing data by leveraging generalized functional principal components analysis (GFPCA). In addressing the second problem related to outlying values, we leverage both FDA and the novel principal component pursuit (PCP) approach, which has seen limited application within the field, to separate on observed functional value into low-rank, sparse, and error component functions. Finally, using a rich longitudinal data set, we provide insight into the third problem regarding what is an appropriate study length, utilizing the framework of measurement reliability which has been often applied in the activity data setting. Our results suggest that leveraging FDA methods can provide more accurate estimates of activity during periods of nonwear then current approaches, and that in the presence of large outliers more robust estimates of underlying activity and outlier presence can be determined by combining FDA methods and those of PCP. Finally, within our longitudinal cohort we show that current guidelines regarding the number of days necessary to achieve a reasonable measurement reliability are inaccurate, and often underestimate the true number of days required.

The decreasing cost of chip manufacturing has greatly increased their distribution and availability such that sensors have become embedded in virtually all physical objects and are able to send and receive data -- giving rise to the Internet of Things (IoT). These embedded sensors are typically endowed with intelligent algorithms to transform information into real-time actionable insights. Recently, humans have taken on a larger role in the information-to-action path with the emergence of human-centric sensing. This has made it possible to observe various processes and infer information in complex personal and social spaces that may not be possible to obtain otherwise. However, a caveat of human-centric sensing is the high cost associated with high precision systems. In this dissertation, we present two low cost and high performing end-to-end solutions for human-centric sensing of physiological phenomena. Additionally, we present a post-hoc data-driven sensor synchronization framework that exploits independent, omni-present information in the data to synchronize multiple sensors. We first propose XTREMIS -- a low-cost and portable ECG/EMG/EEG platform with a small form factor that has a sample rate comparable to research-grade EMG machines. We evaluate XTREMIS on a signal level as well as utilize it in tandem with a Gaussian Mixture Hidden Markov Model to detect finger movements in a rapid, fine-grained activity -- typing on a keyboard. Experiments show that not only does XTREMIS functionally outperforms current wearable technologies, its signal quality is high enough to achieve classification accuracy similar to research-grade EMG machines, making it a suitable platform for further research. We then present SiCILIA -- a platform that extracts physical and personal variables of a user's thermal environment to infer their clothing insulation. An individual's thermal sensation is directly correlated with the amount of clothing they are wearing. Indeed, a person's thermal comfort is crucial to their productivity and physical wellness, and is directly correlated with morale. Therefore it becomes important to be aware of actions such as adding or removing clothing as they are indicators of current thermal sensation. The proposed inference algorithm builds upon theories of body heat transfer, and is corroborated by empirical data. SiCILIA was tested in a vehicle with a passenger-controlled HVAC system. Experimental results show that the algorithm is capable of accurately predicting an occupant's thermal insulation with a low mean prediction error. In the third part of the thesis we present CRONOS -- a sensor data synchronization framework that takes advantage of events observed by two or more sensors to synchronize their internal clocks using only their data streams. Experimental results on pairwise and multi-sensor synchronization show a significant drift improvement for total drift and a very low mean absolute synchronization error for multi-sensor synchronization. / Thesis / Doctor of Philosophy (PhD)

Trunk-mounted monitoring equipment like GPSports SPIHPU units are designed to use global positioning (GPS), accelerometer and heart rate monitoring to evaluate the physical demands of an activity. A medical staff might also consider markers such as running symmetry in evaluation of injury occurrence and rehabilitation. A running symmetry is a ratio of the synchronization of the right and left lower limbs during the gait cycle. An asymmetry due to, a pathology or musculoskeletal injury, results in abnormal loading on the foot that may be detected by trunk-mounted accelerometry. The aim of this study is to evaluate the ability of SPIHPU units to detect running asymmetry. Subjects wore the HPISPU units (100Hz, 16g tri-axial accelerometer, 50Hz magnetometer) while engaged in various running activities. In the first study, artificially inducing a leg length discrepancy led to a difference between running symmetry scores. This discrepancy was confirmed using individual accelerometers attached to the lower leg near the foot. Next, varying running speed did not result in differences in running symmetry. However, the SPIHPU units did detect a running asymmetry between fatigued and non-fatigued conditions. Finally, two case studies showed that the units could identify asymmetry immediately after a lower leg injury and during rehabilitation of anterior cruciate ligament reconstruction surgery. The results of this study show that the HPUSPI units can be reliably used to monitor running symmetry and to detect asymmetrical gait patterns. / Master of Science

The physical body is the one thing we have with us from the day we enter life to the day we depart. Most of us take for granted the fact that our bodies and all the systems therein do what we expect them to without pain or discomfort. It is important to keep this vessel as functional as possible. In this society where independence and mobility are highly valued, it is often thought that the body needs to be in working order to take full advantage of the world around us. It is easier to live when the body is in complete working order since our society is one designed for those with independent mobility. Although it may be difficult to see directly after an injury, quality of life is enhanced when mobility is regained. Reduced functionality leads to physical and emotional debilitation. Reduced functionality may occur from an accident, disease, injury or genetic abnormalities. According to The Disability Statistics Rehabilitation Research and Training Center, the fourth most common condition (4.6% of all conditions) which causes individuals to limit activities, is the impairment of lower extremities, greatly effecting quality of life (Persoon, 2004 and Rikli, 2005) Among the lower extremities, impairments, knee joint injuries and chronic pain are common. A 2003 interview from ABC television quotes Scott Lephart, Ph.D. as saying, knee injuries are occurring in epidemic proportions. Knee injuries constitute only about 15% of all sports injuries but are 50% of visits to sports doctors, indicating that when they happen, they are usually significant. (McLaughlin) This project will focus on lower extremity injuries since it is the largest orthopedically limiting condition. Physical Therapy is one of the medical options used to regain normal functionality of an injured body part or to reduce chronic pain from a musculoskeletal condition. Patients are referred to PTs by a physician, orthopedist or surgeon after an injury. The referrals come after a physician has confirmed that a patient needs to regain functionality and reduce pain after surgery or the incident of chronic pain. Patients may be seen at a variety of locations including the hospital (both in-patient and out-patient), a private PT office, at home, in a nursing home, or in an athletic center. The profile of patients who see PTs is changing and growing. The population of the United States shows a breakdown of 49.1% and 50.1% males to females. (Hicks et al., 2004) In therapeutic settings, the genders are unequally represented with 44.2% of patients being male and 55.8% being female. It has been hypothesized that more women are participating in sports and suffering subsequent injuries (Brisette, 2004 and abclocal.go.com). Compared to their representation in the U.S. general population, middle aged (50-74) and older (74+) adults were overrepresented in the demographics of those seen by a physical therapist. Most frequently seen patients were middle-aged adults (50-74) and young adults (20-49). The population is living longer and, through modern medicine, are able to live with chronic and disabling conditions. These conditions require treatment possibly resulting in the rise of patient statistics weighted towards the older population (Brisette, 2004). A patient participating in a course of physical therapy has not only to contend with the physical ramifications of an injury, but also the psychological issues (Uppal, 2003). While bones, ligaments and cartilage are healing, patients are required to participate in a series of exercises both at home and in a therapists office. While rehabilitating at home, patients may find it difficult to remember, make time or feel motivated to do these exercises. These issues are a large obstacle on the path to wellness. The design solution presented in this thesis looks at the factors which influence motivation including community, positive and negative feedback, tracking and goals. The proposed solution will incorporate information from the fields of psychology, wearable technology and physical therapy. Pain and injury do not necessarily motivate individuals to participate in a course of a HEP (home exercise program) which can eventually lead to better health. Knowing a product will make you feel better does not make you use it. A goal is not enough. To further motivate individuals, aspects of fun, community, autonomy and competence can greatly improve a persons desire to heal (Uppal, 2003 and Deci-Ryan and Coleman, 1998). A customizable, networked solution is proposed where individuals may use one, two or all three parts of an integrated system to track daily and overall progress, work with a buddy and communicate with a therapist. It is hypothesized that with this system, patients will be more motivated to participate in at home physical therapy to regain full functionality. The research and design process took part in various stages. Processes moved from the general to the specific as research and interviews informed the work.

Recent advances in technology are reshaping and enhancing the role of the industrial designer. While industrial designers are already trained to be experts in process and possess a wide range of skills, there must be a higher level of fusion between design, science, and technology than ever before. This paradigm presents an opportunity in the emerging field of wearable technology; industrial design, engineering and computer science would be an optimal collaboration for the inevitable increase in mixes of disciplines to address all aspects of a product and its development. By investigating products from companies such as Nike and Adidas, and also by exploring themes of personal augmentation in science fiction, this thesis will explore the conceptualization of a soft, wearable garment system for runners that utilizes developing advancements in technology, apparel and graphical user interface. / Master of Science

Is improvisation during collaboration a design choice? What is the difference between responsive inspiration and collaboration? Who is in charge of the artistic end result? And what influences the designer’s mood? These questions come across when investigating how to present wearable technology and the role of performance.

This work demonstrates the viability of the ballistocardiogram (BCG) signal derived from a head-worn device as a biometric modality for authentication. The BCG signal is the measure of an individual's body acceleration as a result of the heart's ejection of blood. It is a characterization of an individual's cardiac cycle and can be derived non-invasively from the measurement of subtle movements of a person's extremities. Through the use of accelerometer and gyroscope sensors on a Smart Eyewear (SEW) device, derived BCG signals are used to train a convolutional neural network (CNN) as an authentication model, which is personalized for each wearer. This system is evaluated using data from 12 subjects, showing that this approach has an equal error rate of 3.5% immediately after training, and only marginally degrades to 13% after about 2 months, in the worst case. We also explore the use of our authentication approach for individuals with severe motor disabilities, and observe that the results fall only slightly short of those of the larger population, with immediate EER values at 11.2% before rising to 21.6%, again in the worst case.. Overall, we demonstrate that this model presents a longitudinally-viable authentication solution for passive biometric authentication.

<div>The present study evaluated the feasibility and social validity of using a wearable technology to detect anxiety for educational purposes. Twenty college student who planned to take a Graduate Study Exam (GRE) for attending graduate school were recruited to participate in the study. Each participant completed a practice GRE with quantitative and verbal reasoning sections. The participants were asked to complete a demographic questionnaire and State and Trait Anxiety Inventory before the practice exam. An acceptability questionnaire was administered after the practice exam to collect data on the social validity of the wearable technology. During the practice exam, the participants were asked to tap a button on the wearable device to report stressful incidents (SI) when they felt the exam questions were causing them to feel stressed. The participants’ heart rate and skin conductance data were collected and analyzed with the timing of their self-reported SI. The data indicated that significant heart rate changes were detected in 66% and 70% of the self-reported SI in the quantitative and verbal reasoning sections, respectively. The results indicated heart rate data could be used for short-term anxiety monitoring for educational purposes in the real classrooms. The social validity data indicated polarized results for the acceptance of an anxiety monitoring technology for educational purposes. Participants with higher familiarity with wearable devices in general reported the wearable device used in the study less distracting during the practice exams. Participants with higher levels of anxiety reported being less comfortable with their physiological signals monitored during the practice exams. Implications and suggestions for future research studies are discussed.</div>

Advancements in soft materials and hybrid flexible electronics have enabled developments in flexible circuits and wearables. Where rigid electronics are extremely precise over small physical areas, flexible electronics have the capability to sense over large curved areas. From the onset of epidermal electronics and flexible transistors, there have been great advancements in sensing over soft curved objects, such as human skin or brain tissue. This thesis focuses on hybrid flexible electronics to sense and stimulate over large areas. The aim of the systems presented is to provide insight into complex navigation and sensor processing systems. In addition to the design, fabrication, and characterization of each device, several important characteristics of each device are investigated: material choice, curvature limits, and device sensitivity. The first device presented in this thesis uses strain gauges to track the bending of neurosurgery navigation stylets for catheter placement. The strain gauge fabrication and characterization is presented. Adhesive testing, stylet bending modeling, and noise techniques are also discussed as they were found to be critical components of the system. The device's limit of detection is 1 mm tip displacement. The purpose of the second set of devices presented is to gain object information from curved or edged robotic structures. Three sensing modes were explored: piezoelectric, strain, and capacitive. The piezoelectric sensor was founded to have a 6.7 times increase in sensitivity when an open-cell foam compliant layer is used. The strain sensor was found to have a gauge factor of 2.83 on a silicone layer and 1.5 on a polymer layer. The combination of the piezoelectric and strain sensing modes is presented. The capacitive sensor is able to detect object shape using inverse problem mathematical techniques. The third device and system presented is a flexible electrode array for stimulating the electroreceptors of electric fish. The spatial and temporal control of a conformal stimulation array enables the decoding of motor signals in the brain. The array fabrication and system development is presented. Surface modification of the electrode array successfully altered the surface energy of the array to match that of the fish for the optimal array-fish interface. In summary, the development and integration of these flexible electronic devices has been achieved. It was found that the interface between the flexible electronic devices and binding objects is critical to device sensitivity and reliability.

The Quantified Self movement has led to an increased routine use of consumer wearables, generating large amounts of person-generated wearable device data. This has become an opportunity to researchers to conduct research with large-scale person-generated wearable device data without having to collect data in a costly and time-consuming way. However, there are known challenges of wearable device data such as missing data or inaccurate data which raises the need to assess the quality of data before conducting research. Currently, there is a lack of in-depth understanding on data quality challenges of using person-generated wearable device data for research purposes, and how data quality assessment should be conducted. Data quality assessment could be especially a burden to those without the domain knowledge on a specific data type, which might be the case for emerging biomedical data sources. The goal of this dissertation is to advance the knowledge on data quality challenges and assessment of person-generated wearable device data and facilitate data quality assessment for those without the domain knowledge on the emerging data type. The dissertation consists of two aims: (1) identifying data quality dimensions important for assessing the quality of person-generated wearable device data for research purposes, (2) designing and evaluating an interactive data quality characterization tool that supports researchers in assessing the fitness-for-use of fitness tracker data. In the first aim, a multi-method approach was taken, conducting literature review, survey, and focus group discussion sessions. We found that intrinsic data quality dimensions applicable to electronic health record data such as conformance, completeness, and plausibility are applicable to person-generated wearable device data. In addition, contextual/fitness-for-use dimensions such as breadth and density completeness, and temporal data granularity were identified given the fact that our focus was on assessing data quality for research purposes. In the second aim, we followed an iterative design process from understanding informational needs to designing a prototype, and evaluating the usability of the final version of a tool. The tool allows users to customize the definition of data completeness (fitness-for-use measures), and provides data summarization on the cohort that meets that definition. We found that the interactive tool that incorporates fitness-for-use measures and allows customization on data completeness, can support assessing fitness-for-use assessment more accurately and in less time than a tool that only presents information on intrinsic data quality measures.