Background: The rapid growth and increased competition in today’s technology industry leads to a growth in consumers’ expectations on new presented products. One of the growing markets within the technology sector are wearable devices – especially smartwatches. Almost all major IT and electronic giants such as Apple, Samsung, Microsoft and Google offer smartwatches – competition is increasingly growing. Consumers benefit from the wide variety of choices while selecting a smartwatch – but what are the factors that influence them to purchase such a device?                                     Purpose: This thesis investigates the intention of German consumers to purchase smartwatches and examines the influencing factors.                  Method: In order to meet the purpose of this thesis, the authors conducted a quantitative study. The data was collected by means of an online questionnaire among German consumers and was distributed via the messenger application WhatsApp. To ensure the collection of enough responses the authors chose to apply a non-probability snowball sampling approach. Beside demographical questions and two introductory questions concerning the knowledge and the usage of smartwatches, the questionnaire consisted of eight question blocks that have been developed based on two well-established models to predict human behavior and technology adoption: Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM).                     Conclusion:             The results of this study provide empirical evidence that the attitude towards using was the strongest predictor for the intention to purchase smartwatches. The outcomes further show that the attitude is influenced by the two hedonic factors perceived enjoyment and design aesthetics as well as by the utilitarian factor perceived usefulness. Out of those three factors perceived enjoyment was found to exert the strongest influence on attitude. Contrary to previous research, the results of this study could not reveal a significant influence of subjective norms on purchase intention. However, beside the attitude, perceived behavioral control was also found to influence purchase intention. The findings of this research allowed to draw a variety of theoretical and managerial implications as well as to develop possible research opportunities for future studies.

nuSense is the result of a degree work on master level at Umeå Institute of Design exploring why wearable technology oftentimes tread a rather narrow path, with many different companies releasing essentially the same product with a new shell, and innovation being slow. Through research, interviews and user research, hardware prototyping and testing it became clear that developing for wearable technology is a very complicated task, for many reasons. Being able to build quick exploratory prototypes was nigh impossible if you do not have a grasp of hardware developing platforms and programming. Further, those outside the industry who just want to explore wearable technology lack a platform to do so easily, aside from buying ready-made solutions made to do one single prepackaged thing. Based on this a concept was developed to provide a platform to explore wearable technology, through modular building-blocks and an easy to grasp interface.

Uppsatsens syfte är att utröna om wearables som motivationsverktyg kan tillämpas för att öka engagemanget i organisations friskvårdsprogram. Basen i uppsatsen utgörs av litteraturstudier i aktuell forskning gällande anställdas engagemang i friskvårdsprogram, hur friskvårdsprogram på olika sätt kan bedrivas samt hur modern teknologi kan implementeras i sådana miljöer. Med en metod som inspirerats av grundad teori har faktorer i litteraturen, vilka fungerar som möjliggörare och begränsare för deltagande i friskvårdsprogram, kunnat urskiljas och struktureras.  Struktureringen med likartade faktorer bildade s.k. dimensioner, som sedan kategoriserades under mer övergripande teman. Dessa teman analyserades utifrån deras ansats att agera möjliggörande och begränsande med fokus på wearables. Undersökningen har visat att framgångsrikt bedrivet friskvårdsprogram består av fyra teman: förstudie, design, implementation och information. För att i största mån möjliggöra deltagande för anställda i friskvårdsprogram måste dessa fyra teman beaktas, både i en generell friskvårdssatsning men även om wearables ska implementeras. Under en förstudie behöver ledningen fastställa vilken roll wearables ska ha i friskvårdsprogrammet där dess användningsområde behöver vara starkt förankrat i anställdas behov, önskemål och åsikter. Friskvårdsprogrammets design bygger på wearables anknytning till de erbjudna aktiviteterna som måste vara väl förankrade. Under implementationen är det viktigt att anställda erhåller ett aktivt stöd från ledande element i organisationens alla hierarkier. Wearables kan agera möjliggörande i det avseendet att ledningens engagemang kan tydligt presenteras för anställda genom ledarbrädor eller tävlingar. En viktig del av wearables roll i friskvårdsprogram är att samla information om användarnas fysiska aktivitet. Här skänks värde både för individen men även organisationen som med lätta medel tillåts mäta framgången hos friskvårdsprogrammet.

Novel advances in engineering and data analytics are revolutionizing both our ability to monitor Parkinson’s disease (PD) patient symptoms and our understanding of neuropathology. Despite promise, key challenges exist before patient monitoring technologies become standard in clinical settings, including 1) industry standardization of sensor-based analytical approaches; 2) validation of endpoint sensitivity to degree of impairment and medication state; and 3) consensus regarding appropriate devices, algorithms, data requirements, and statistical analysis requirements for symptom measurement outside of the clinic. In addition to the need for better patient monitoring, no disease-modifying therapeutics currently exist and thorough understanding of the neuropathology of PD remains elusive. To this end, large network brain simulations that leverage efficient computational frameworks are beginning to provide insight into mechanisms that facilitate pathological oscillations and may serve to identify new therapeutic targets. To address current limitations in patient monitoring and our understanding of neuropathology, in this dissertation I 1) develop and evaluate validity and reliability of an open-source, wearable sensor-based algorithm for measuring gait in PD patients, 2) evaluate and compare sensitivity of at-home measurements relative to in-clinic measurements, 3) evaluate sensitivity of wearable-derived features for measuring degree of gait impairment and treatment response in PD patients, and 4) investigate the effect of synaptic parameters on beta synchrony and entrainment in a large-scale spiking model of the subthalamic nucleus-globus pallidus externa (STN-GPe) network of the basal ganglia. Importantly, I find that sensor-derived features derived from the at-home environment differ from and are more sensitive to small changes compared to in-clinic, traditional assessments. Furthermore, I demonstrate the capacity for a single, lower back sensor-based algorithm to estimate gait features with sufficient sensitivity to detect degree of gait impairment and treatment effect in a mild-to-moderate PD population. Lastly, I demonstrate that weak synaptic connections between STN and GPe allows the STN-GPe network to entrain to a wide range of frequencies outside of the beta range, thus elucidating constraints on conditions required for beta production. Together, my work provides new insights into the feasibility and benefits of sensor-based symptom monitoring and PD-related neuropathology.

This thesis analyses the domain of wearables and its current developments, and the notion of Hertzian Space, under the consideration of the emerging areas of the internet of things and ubiquitous computing. It introduces wearables as an opening into Hertzian Space and presents a possible approach to how a wearable could be designed to function as a touchpoint between human body and Hertzian Space. It thereby looks into what electronic intimacy can look like and how it can be created and used to bridge an enstrangement between person and device.This thesis illustrates a whole process of shaping this wearable device, including experimentation, digital sketching, and different testing methods. From the making of this prototype it is concluded that enhancing electronic characteristics and opening the Hertzian Space for interaction can create a stronger awareness of our electronic surrounding and the invisible fields around us, which thereby also facilitates a better understanding of our position within Hertzian Space and offers a new personal perspective. This study provides an opening into the eld of exploration of the Hertzian Space through wearables and should be seen as a generative design contribution, which ties aspects in the fields of wearables, Hertzian Space and interaction design together and also offers openings for further research.

This thesis analyses the domain of wearables and its current developments, and the notion of hertzian space, under the consideration of the emerging areas of the internet of things and ubiquitous computing. It introduces wearables as an opening into hertzian space and presents a possible approach to how a wearable could be designed to function as a touchpoint between human body and hertzian space. It thereby looks into what electronic intimacy can look like and how it can be created and used to bridge an enstrangement between person and device.This thesis illustrates a whole process of shaping this wearable device, including experimentation, digital sketching, and different testing methods. From the making of this prototype it is concluded that enhancing electronic characteristics and opening the hertzian space for interaction can create a stronger awareness of our electronic surrounding and the invisible fields around us, which thereby also facilitates a better understanding of our position within hertzian space and offers a new personal perspective. This study provides an opening into the eld of exploration of the hertzian space through wearables and should be seen as a generative design contribution, which ties aspects in the fields of wearables, hertzian space and interaction design together and also offers openings for further research.

The overarching goal of this work was to develop novel data collection and analysismethods to better understand how soldier burden affects the soldier survivability tradespace (i.e.,performance, musculoskeletal health, and susceptibility to enemy action). To achieve this goal,three studies were completed: 1) a mobile inertial measurement unit (IMU) suit was validatedagainst an optical motion capture (OPT) system; 2) data from the IMU suit was used to develop aframework for morphing movement patterns to represent intermediary body-borne load massesand personal characteristics; and 3) a single IMU was used to develop a human activity recognitionalgorithm and calculate tradespace metrics.In study one, a whole-body IMU suit (MVN Link, Xsens, Netherlands) was validatedagainst an OPT system (Vantage V5, Vicon, United Kingdom) for military-based movementsusing the root mean squared error (RMSE) of joint angles and Pearson correlation coefficients ofprincipal component (PC) scores. During a standard implementation (i.e., using differentbiomechanical models and not attempting to align them; VOPT vs. XIMU), average RMSE valuesacross all tasks were less than 9° for the lower limbs but up to 40.5° for the upper limbs. Whenusing the same biomechanical model and applying an alignment procedure (VOPT vs. VIMU-CAL),RMSE values decreased to an average of 2.5º and 17.5º for the lower and upper limbs, respectively.Of the 48 retained PCs, 38 (79%) had scores with a high or very high positive correlation (> +0.70)between the OPT and IMU systems, 15 (31%) of which had scores with a very high correlation (>+0.90). The average Pearson correlation coefficient was 0.81 (SD = 0.14). Given these results, theIMU system was deemed appropriate for collecting military-based movement patterns.In study two, principal component analysis (PCA) and linear discriminant analysis (LDA)were used to generate whole-body morphable movement patterns to represent intermediary body-ixborne loads and personal characteristics (sex, body mass, military experience). Reconstructedmovements were used for animation, musculoskeletal modelling, exposure time calculations, andsusceptibility calculations; all calculated values were comparable to previous research. Thisproject displayed that a relatively small representative dataset can be used to simulate the changein whole-body movement patterns caused by many different body-borne loads and personalcharacteristics not originally collected. By implementing this framework, defence scientists canreduce the amount and complexity of data collections needed to better understand the impact onthe survivability tradespace caused by all types of soldier burden.Study three focused on developing a deployable method for calculating tradespace metricsin the field. Three deep neural network (DNN) architectures were trained to identify eleven classlabels using data from a single IMU on the upper back. Data were collected during an indoorlaboratory-based protocol and an outdoor simulated two-person section attack. The predictionsmade by the DNNs were processed through a two-step logical algorithm to apply real-worldconstraints and expand the predictions to 19 class labels. The deep convolutional long short-termneural network architecture outperformed the convolutional neural network and fully-connectedneural network for all three approaches: indoor only, section attack only, and general. Movementswere identified with a high degree of accuracy (> 87% for accuracy and weighted F1-score), andtradespace metrics were calculated within 0.17 seconds, 0.21 shots, and 1.25% susceptibilitycompared to the tradespace metrics calculated from the ground truth labels.Overall, the data-driven methods developed throughout this dissertation can be used bydefence scientists and military leaders to improve the understanding of the survivabilitytradespace, which has the potential to improve the quality of life of soldiers, making them more fitand ready to fight, thus increasing the likelihood of mission success.

With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression. / I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test,  Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 %  på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.

Inertial measurement units (IMUs) are being recognized as a portable and cost-effective alternative to motion analysis systems and have the potential to be introduced into clinical settings for the assessment of functional movement quality of the spine in patients with low back pain. However, uncertainties regarding sensor accuracy and reliability are limiting the widespread use and acceptance of IMU-based assessments into routine clinical practice. The objective of this work was to assess the performance of inexpensive wearable IMUs (Mbientlab MetaMotionR IMUs; Mbientlab Inc., San Francisco, USA; product specifications available in Appendix C) relative to conventional optical motion capture equipment (Vicon Motion Systems Ltd., Oxford, UK) in: 1) a controlled environment, and 2) an uncontrolled environment. The first study evaluated the performance of 2 IMUs in a controlled environment during simulated repetitive spine motion carried out by means of a motorized gimbal. Root mean square error (RMSE) and mean absolute measurement differences between cycle-to-cycle minimum, maximum, and range of motion values, as well as correlational analyses within IMUs and between IMUs and Vicon, in all movement directions (i.e., simulated flexion-extension (FE), lateral bending (LB), and axial twisting (AT)), were compared. Measurement error was low in all axes during all tests (i.e., ≤ 1.54°); however, low-to-moderate correlational results were found in one non-primary axis, and this axis changed depending on the direction of the movement (i.e., LB during FE-motion (0.244 ≤ R ≤ 0.515), AT during LB-motion (0.594 ≤ R ≤ 0.795), and FE during AT-motion (0.002 ≤ R ≤ 0.255)). The second study was designed to assess the performance of the IMUs in an uncontrolled environment during repetitive spine FE in human participants. Absolute angles and local dynamic stability were compared for individual IMUs (which were placed over T10-T12 spinous processes, and the pelvis) as well as for relative motion between IMUs. Maximum finite-time Lyapunov exponents (λmax) were used to quantify local dynamic stability and were calculated using both FE and the sum of squares (SS) from measured spine kinematics. It was found that the IMUs have acceptable performance in all axes when tracking motion (RMSE ≤ 2.43°); however, low-to-moderate correlational results were found in one non-primary axis (0.987 ≤ RFE ≤ 0.998; 0.746 ≤ RLB ≤ 0.978; 0.343 ≤ RAT ≤ 0.679). In addition, correlations between λmax estimates were high; therefore, local dynamic stability can be accurately estimated using both FE and SS data (0.807 ≤ 〖ICC〗_2,1^FE ≤ 0.919; 0.738 ≤ 〖ICC〗_2,1^SS ≤ 0.868). Correlation between λmax estimates was higher when using FE data for individual sensors/rigid-body marker clusters; however, correlation was higher when using SS data for relative motion. In general, the results of these studies show that the MetaMotionR IMUs have acceptable performance in all axes when considering absolute angle orientation and motion tracking, and measurement of local dynamic stability; however, there is low-to-moderate correlation in one non-primary axis, and that axis changes depending on the direction of motion. Future research will investigate how to optimize performance of the third axis for motion tracking; it will also focus on understanding the significance of the third axis performance when calculating specific outcome measures related to spine movement quality.

Wearable technologies are being adopted in increasing numbers and the market space appears poised for continued growth in virtually all areas, from medicine, to self-quantification, to sports. While the overwhelming majority of work on wearables has been done on their medical applications and their role in shaping identity, this dissertation examines the roles that wearable technologies play on the decision-making processes in athletic contexts. Using new materialism and Actor Network Theory as lenses, I attempt to break from the Cartesian model that places human subjectivity and intentionality at the center of a rhetorical situation and, rather, allow that non-human actants are agentive. I examine the interactions that age-group triathletes have with their wearable technologies and the shifting agencies that accompany those interactions. These interactions call on disparate human and non-human actors in forming a series of temporary, shifting networks that utilize a distributed agency in the decision making process.