A growing number of factors, including costs, technological advancements, ageing populations, and medical errors, are leading industrialized countries to invest in research on alternative solutions to improve their health-care systems and increase patients’ quality of life. Personal health systems (PHS) examplify the use of information and communication technologies that enable a paradigm shift from the traditional hospital-centered healthcare delivery model toward a preventive and person-centered approach. PHS offer the means to monitor a patient’s health using wearable, portable or implantable systems that offer ubiquitous, unobtrusive biodata acquisition, allowing remote monitoring of treatment and access to the patient’s status. Electrical bioimpedance (EBI) technology is non-invasive, quick and relatively affordable technique that can be used for assessing and monitoring different health conditions, e.g., body composition assessments for nutrition. When combined with state-of-the-art advances in sensors and textiles, EBI technologies are fostering the implementation of wearable bioimpedance monitors that use functional garments for personalized healthcare applications. This research work is focused on the development of wearable EBI-based monitoring systems for ubiquitous health monitoring applications. The monitoring systems are built upon portable monitoring instrumentation and custom-made textile electrode garments. Portable EBI-based monitors have been developed using the latest material technology and advances in system-on-chip technology. For instance, a portable EBI spectrometer has been validated against a commercial spectrometer for total body composition assessment using functional textile electrode garments. The development of wearable EBI-based monitoring units using functional garments and dry textile electrodes for body composition assessment and respiratory monitoring has been shown to be a feasible approach. The availability of these measurement systems indicates progress toward the real implementation of personalized healthcare systems. / <p>QC 20170517</p>

This thesis in the field of microdata analysis aims to introduce dose optimizing algorithms for the pharmacological management of Parkinson’s disease (PD). PD is a neurodegenerative disease that mostly affects the motor functions of the patients and it is characterized as a movement disorder. The core symptoms of PD are: bradykinesia, postural instability, rigidity, and tremor. There is no cure for PD and the use of levodopa to manage the core symptoms is considered the gold standard. However, long term use of levodopa causes reduced medication efficacy, and side effects, such as dyskinesia, which can also be attributed to overmedication. When that happens precise individualized dosing schedules are required. The goal of this thesis is to examine if algorithmic methods can be used to find dosing schedules that treat PD symptoms and minimize manifestation of side effects. Data from three different sources were used for that purpose: data from a clinical study in Uppsala University hospital in 2015, patient admission chart data from Uppsala University hospital during 2011-2015, and data from a clinical study in Gothenburg University during 2016-2017. The data were used to develop the methods and evaluate the performance of the proposed algorithms.The first algorithm that was developed was a sensor-based method that derives objective measurements (ratings) of PD motor states. The construction of the sensor index was based on subjective ratings of patients’ motor functions made by three movement disorder experts. This sensor-based method was used when deriving algorithmic dosing schedules. Afterwards, a method that uses medication information and ratings of the patients’ motor states to fit individual patient models was developed. This method uses mathematical optimization to individualize specific parameters of dose-effects models for levodopa intake, through minimizing the distance between motor state ratings and dose-effect curves. Finally, two different dose optimization algorithms were developed and evaluated, that had as input the individual patient models. The first algorithm was specific to continuous infusion of levodopa treatment, where the patient’s state was set to a specific target value and the algorithm made dosing adjustments to keep that patients motor functions on that state. The second algorithm concerned oral administration of microtables of levodopa. The ambition with this algorithm was that the suggested doses would find the right balance between treating the core symptoms of PD and, at the same time, minimizing the side effects of long term levodopa use, mainly dyskinesia. Motor state ratings for this study were obtained through the sensor index. Both algorithms followed a principle of deriving a morning dose and a maintenance dose for the patients, with maintenance dose being an infusion rate for the first algorithm, and oral administration doses at specific time points for the second algorithm.The results showed that the sensor-based index had good test-retest reliability, sensitivity to levodopa treatment, and ability to make predictions in unseen parts of the dataset. The dosing algorithm for continuous infusion of levodopa had a good ability to suggest an optimal infusion rating for the patients, but consistently suggested lower morning dose than what the treating personnel prescribed. The dosing algorithm for oral administration of levodopa showed great agreement with the treating personnel’s prescriptions, both in terms of morning and maintenance dose. Moreover, when evaluating the oral medication algorithm, it was clear that the sensor index ratings could be used for building patient specific models.

When moving into a more connected world together with machines, a mutual understanding will be very important. With the increased availability in wear- able sensors, a better understanding of human needs is suggested. The Dart- mouth Research study at the Psychiatric Research Center has examined the viability of detecting and further on predicting human behaviour and complex tasks. The field of smoking detection was challenged by using the Q-sensor by Affectiva as a prototype. Further more, this study implemented a framework for future research on the basis for developing a low cost, connected, device with Thayer Engineering School at Dartmouth College. With 3 days of data from 10 subjects smoking sessions was detected with just under 90% accuracy using the Conditional Random Field algorithm. However, predicting smoking with Electrodermal Momentary Assessment (EMA) remains an unanswered ques- tion. Hopefully a tool has been provided as a platform for better understanding of habits and behaviour.

Soft materials have gained increasing prominence in science and technology over the last few decades. This shift from traditional rigid materials to soft, compliant materials have led to the emergence of a new class of devices which can interact with humans safely, as well as reduce the disparity in mechanical compliance at the interface of soft human tissue and rigid devices.<br><br>One of the largest application of soft materials has been in the field of flexible electronics, especially in wearable sensors. While wearable sensors for physical attributes such as strain, temperature, etc. have been popular, they lack applications and significance from a healthcare perspective. Point-of-care (POC) devices, on the other hand, provide exceptional healthcare value, bringing useful diagnostic tests to the bedside of the patient. POC devices, however, have been developed for only a limited number of health attributes. In this dissertation I propose and demonstrate wireless, wearable POC devices to measure and communicate the level of various analytes in and the properties of multiple biofluids: blood, urine, wound exudate, and sweat.<br><br>Along with sensors, another prominent area of soft materials application has been in actuators and robots which mimic biological systems not only in their action but also in their soft structure and actuation mechanisms. In this dissertation I develop design strategies to improve upon current soft robots by programming the storage of elastic strain energy. This strategy enables us to fabricate soft actuators capable of programmable and low energy consuming, yet high speed motion. Collectively, this dissertation demonstrates the use of soft compliant materials as the foundation for developing new sensors and actuators for human use and interaction.

Les systèmes de navigation par satellites permettent les applications de positionnement en extérieur, dont la navigation routière. Dans les environnements contraints, comme l'intérieur des bâtiments où ces signaux satellitaires sont dégradés, la continuité du service de positionnement est nécessaire. Les applications adaptées aux citoyens modernes avec leurs appareils nomades posent des contraintes fortes de mobilité, de coûts et de limitations des infrastructures existantes. Les larges possibilités de déplacements dans des environnements hétérogènes accroissent les difficultés. Un état de l'art alimenté par une décennie de travaux académiques et industriels présente un ensemble de technologies qui visent disponibilité et performance. L'accent porte ensuite sur les systèmes inertiels pédestres à bas coût, avec une première contribution permettant d'abandonner la détection de pas au profit d'une mobilité facilitée, mais reste limitée par la connaissance de la distance parcourue pendant une phase de calibration. Cette approche nouvelle est confrontée à celle classique au pied, puis éprouvée pour différents capteurs et piétons au travers d'expérimentations répétées en conditions réalistes. Une seconde contribution décline une constellation radio locale pour estimer la distance avec une infrastructure allégée à deux émetteurs. Elle s’inspire d’une conception satellitaire sur radio programmable pour faciliter sa compatibilité avec l’existant et explorer ses performances. Une surveillance du rapport signal à bruit inter-canal améliore la précision du positionnement. Le couplage de ces systèmes asynchrones et distribués est évalué en intérieur sur une plateforme automatisée / The global navigation satellite systems allow outdoor positioning applications, including car navigation. In challenging environments, such as the buildings where satellite signals are mitigated, georeferenced points of interest or navigation applications require a continuity of the positioning service. The applications adapted to modern citizens and their mobile devices raise strong constraints on mobility, costs and limitations of the existing infrastructure. The wide variety of displacements in heterogeneous environments increases the challenge. A state of the art fed by a decade of academic and industrial works presents a set of technologies that target availability and performance. The emphasis follows on the low cost pedestrian inertial systems, with a first contribution allowing to give up the step detection for the benefit of an easier mobility, but remains limited to the knowledge of the distance traveled during a calibration phase. This new approach is compared with the classical foot-mounted approach, and then benchmarked with several sensors and pedestrians through repeated experiments in real conditions. A second contribution operates a local radio constellation to estimate the distance with a minimal infrastructure with two emitters. The signals and the algorithm are based on a reproduction of satellite systems to ease the compatibility but are implemented on a programmable radio to explore the performances. A monitoring of the difference of carrier to noise ratio between the radio channels improves the distance estimation. The hybridization of these distributed, asynchronous and multi-rates inertial and radio systems is evaluated indoor on a motorized platform

Apple WatchTM huserar funktionaliteter inom medicinteknik. Detta är ett examensarbete inom medicinteknik som ställer frågan om hur pass bra mätningar den utför i förhållande till motsvarande medicinteknisk produkt. Smartklockan kan identifiera R-toppar i EKG mycketväl, och därigenom också förmaksflimmer. I förhållande till pulsoximeter presenteras syremättnad några procentenheter för högt och följsamheten är något stel. Apple WatchTM mäter puls bra vid högre hjärtslagsfrekvenser, men har kraftigt begränsad träffsäkerhet och sämre följsamhet vid mätningar i vila. Det kan inte styrkas att Apple WatchTM kan användas som verktyg för att ställa någon medicinsk diagnos, utifrån testerna i detta arbetet, där datamängd och urvalsgrupp samtidigt varit för liten för att kunna ge riktig statistisk signifikans. / The Apple WatchTM has functionalities related to the field of medical technology. This is a degree project in medical engineering that raises the question how adequate measurements it can acquire, in relation to a corresponding medical device. The Apple WatchTM can identify R waves in ECG very well and thereby also atrial fibrillation. In relation to pulse oximeters, oxygen saturation is given a few percentage points too high and the compliance is somewhat rigid. Apple WatchTM measures heart rate well at higher heart rates, but has severely limited accuracy and has poorer compliance when measuring at rest. The tests of this work shows that the Apple WatchTM should not be used as a tool for making any medical diagnosis, but the amount of data and test subjects were not sufficient enough to make it statistically significant.

Occupational upper extremity disorders have become a major issue in modern society. Poorly designed workplaces, high job demands, and incorrect work-habits can lead to the development of upper extremity disorders (UEDs) in the workplace. This issue not only causes health-relatedproblems for the individual but also forms a significant economic burden on society due to sickleaves, healthcare and untimely exit of affected individuals from the workforce. The risk of developing occupational UEDs varies with different professions. The European Agency for Safety and Health at Work (EU-OSHA) has recognized significant occupational health risks associated with the hairdressing profession. It has been estimated that UEDs are five times more prevalent amongst hairdressers than other professions. Qualitative risk assessment tools based on self-reports and observation have been used to identify the risks of developing UEDs with hairdressing profession before. However, a quantitative risk assessment tool that provides objective data on work posture is more precise and objective than self-report and observation. This data can help to identify the risks of developing UEDs associated with each hairdressing task. Furthermore, it can enable self-assessment of workload and posture awareness by providing feedback to the user. Inertial Measurement Units (IMU) as part of a wearable system developed at KTH were used in this study to investigate the risks of developing UEDs for hairdressing students. The feasibility of using a feedback function for providing posture awareness was also evaluated by comparing the measurements obtained with and without using the feedback function. Twelve hairdresser students were enrolled in the study. The percentage of time for elevated angles above 30°, 60° and 90° for arms, and above 45° or less than 0° for the trunk flexion is presented. In addition, 10th and 90th percentiles (°) of arms and trunk angular distribution is presented. The result of a statistical analysis performed on data with and without feedback was used to evaluate the effectivity of using the feedback function in preventing the development of occupational UEDs. A System Usability Scale (SUS) questionnaire was used to evaluate the overall usability of the system. The result of this study confirms that the hairdressing profession falls in the high-risk category for developing UEDs. The use of this technical system has enabled a precise risk assessment evaluation of each hairdressing task. Such data can be used as a foundation for improving the ergonomic design of the workplace. The feasibility of using the feedback function as a prevention tool on the individual level is highly dependent on the individuals’ motivation and their attitude towards changing their work habits. However, the results in general, indicate a decrease in the abduction angle (°) for both left and right arm when the feedback function is used. For example, the 90th percentile abduction angles (°) for left arm (all 12 subjects) during the drying part of one fundamental work-cycle decreased from a value of 60.4° to 58.2° when the feedback function was used. The 90th percentile abduction angles (°) for the right arm during the same part of the fundamental work-cycle decreased from an angle of 53.1° to 51.4°. The SUS score of 75.6 indicates good overall usability for the system. / Besvär i det muskuloskeletala systemet i överkroppen som uppkommer på grund av påfrestande arbetsställningar och icke-optimala arbetsvanor blir allt vanligare i det moderna samhället. Besvären orsakar inte bara hälsorelaterade problem för individen utan även en avsevärd ekonomisk börda för samhället. Risken för att utveckla skador i överkroppen varierar med olika yrken. Europeiska arbetsmiljöbyrån (EU-OSHA) har identifierat betydande hälsorisker i samband med frisörarbete. Det har uppskattats att besvär i överkroppen är 5 gånger mer förekommande hos frisörer jämfört med andra yrken. Det finns många forskningsprojekt som har använt kvalitativa riskbedömningsverktyg, baserade på självrapportering och observation, som identifierar riskerna med att utveckla skador i överkroppen bland frisörer. Ett kvantitativt riskbedömningsverktyg som ger objektiva data om arbetsställning är dock mer exakt än självrapportering och observation. Ett sådant verktyg kan hjälpa till att identifiera risken för skadeutveckling i överkroppen. Inertial Measurement Units (IMUs) är en del av ett bärbart mätsystem som har utvecklats påKTH. Systemet användes i denna studie för att identifiera risken för skadeutveckling i överkroppen bland frisörer. Riskidentifieringen gjordes genom att mäta vinkel på armar och rygg. Mätsystemet har även en inbyggd återkopplingsfunktion som uppmärksammar användaren om deras kroppsställning. Effektiviteten av att använda återkopplingsfunktionen för att förebygga jobbrelaterade skador utvärderades genom jämförelse av mätningar som erhållits med och utan återkopplingsfunktion. Tolv frisörstudenter deltog i studien. Överkroppspositionen definierades av vinklar över 45° eller mindre än 0° från en position där ryggen är rak. Abduktionsvinklar över 30°, 60° och 90° mättes för armar. Tidsperioden för dessa vinklar d.v.s. hur lång tid överkroppen hölls i dessa vinklar räknades. Armar och överkroppvinklar för 10:e och 90:e percentilen (°) samt resultat av en statistisk analys som utfördes på data samlade med och utan återkopplingsfunktionen presenterades. Analysen utfördes för att utvärdera hur effektiv återkopplingsfunktionen är för att förhindra arbetsskadeutveckling. En System Usability Scale (SUS) frågeformulär användes för att utvärdera systemets övergripande användbarhet.Resultatet av denna studie bekräftar att frisöryrket faller i högriskkategorin för arbetsskadeutveckling. Användningen av detta bärbara mätsystem har möjliggjort en exakt riskbedömning för olika arbetsuppgifter. En sådan information kan användas som grund för att förbättra ergonomiska förhållanden på arbetsplatser. Effektiviteten av återkopplingsfunktionen som ett förebyggande verktyg på individnivå är starkt beroende av individernas motivation och deras inställning till att ändra sina arbetsvanor. Emellertid anger resultaten en generell minskning av armvinklar (°) för både vänster och höger arm när återkopplingsfunktionen används. Till exempel har den 90:e percentil vinklarna (°) för vänsterarm (alla 12 personer) under hög belastning minskat från ett värde av 60.4° till 58.2 °. Den 90:e percentil vinklarna (°)för höger arm under hög belastning har också minskat från ett värde av 53.1° till 51.4°. SUS-poängen på 75.6 indikerar en bra användbarhet för systemet

<p dir="ltr">Construction workers often suffer from excessive stress from working in dynamic and complex hazard-rich environments. These workers are subject to experiencing diverse external stressors, which can increase their involvement in risk-taking behaviors by increasing human error, referring to individuals’ misperceptions and misjudgment. Task, social, and environmental stressors are the most common external stressors that can negatively impact workers’ safety performance. Task stressors mainly occur when the projects fall behind schedule which puts workers under productivity and mental demand. In addition, workers are exposed to social stressors due to the inherently social environment of construction job sites requiring collaborative efforts. Such workers also suffer from environmental stressors as they mainly need to perform construction tasks outdoors in extreme environments. There is a paucity of research to empirically examine how such external stressors may affect workers’ situational awareness and risk-taking behaviors. Therefore, the overall goal of this dissertation is to <i>examine the theoretical foundations and empirical evidence of changes in workers’ decision dynamic in the construction industry when exposed to task (e.g., productivity pressure and mental demand), social (e.g., peer pressure), and environmental (e.g., heat stress) stressors.</i></p><p dir="ltr">To accomplish this, a series of studies were conducted to investigate the effects of task, social, and environmental stressors on workers’ situational awareness and hazard identification skills. To do so, taking advantage of novel technologies, this study developed immersive mixed reality (MR) and augmented virtuality (AV) simulating high-risk construction tasks. Such environments were integrated with several wearable sensing technologies to measure individuals’ cognitive responses and decision dynamics while completing the tasks under different stressors. The findings demonstrated that external stressors reduce worker situational awareness, impair their cognitive processes, and negatively affect their safety performance.</p><p dir="ltr">Such findings were then utilized to develop an intelligent and comprehensive AI-based predictive system to identify at-risk workers imposed to external stressors. This system translates physiological, cognitive, and biomechanical metrics into AI-identified predictors of three types of external stressors; localizes workers, and assesses risks of being injured in real-time which will then dictate the urgency of providing any intervention. These analyses are then used to identify and propose tailored safety interventions.</p><p dir="ltr">This dissertation contributes to the existing body of knowledge by adopting innovative approaches to empirically study the extent to which external stressors may affect workers’ decision dynamics by examining the changes in their situational awareness, risk-taking, and safety performance measures. In addition, this work contributes to practice by raising awareness about the adverse effects of several cognitive biases due to such stressors, such as risk compensation, cognitive tunneling, and impaired attentional distribution, which can undermine the efficacy of safety interventions in the construction industry. It highlights the critical role of these cognitive biases in safety practices and the necessity of educating safety professionals and workers about how psychological factors can impact safety on the job site and potential ways to mitigate these potential negative impacts. Further, the developed AI-based predictive system breaks new ground by identifying at-risk workers, assessing potential risks, and recommending safety interventions.</p>

This work aims to introduce a methodology for the virtual assessment of inertial measurement unit (IMU)-based Body-Attached Sensor Networks (BASNs) for gesture recognition. This design framework consists of three main parts: (i) multibody modelling of the human upper body, (ii) simulation of human gestures along with data acquisition from modelled IMUs, and (iii) determination of the optimal number of sensors to include in the network along with their placements on the body. By using simulation, the boundary conditions of BASNs can be assessed in a short time without the need for costly and time-consuming user studies.

Wireless power transfer (WPT) systems are important in many areas, such as medical, communication, transportation, and consumer electronics. The underlying WPT system is comprised of a transmitter (TX) and receiver (RX). For biomedical applications, such systems can be implemented on rigid or flexible substrates and can be implanted or wearable. The efficiency of a WPT system is based on power transfer efficiency (PTE). Many WPT system optimization techniques have been explored to achieve the highest PTE possible. These are based on either a figure-of-merit (FOM) approach, quality factor (Q-factor) maximization, or by sweeping values for coil geometries. Four WPT systems for biomedical applications are implemented with inductive coupling. The thesis later presents an optimization technique for finding the maximum PTE of a range of frequencies and coil shapes through frequency, geometry and shape sweeping. Five optimized TX coil designs for different operating frequencies are fabricated for three shapes: square, hexagonal, and octagonal planar-spirals. The corresponding RX is implemented on polyimide tape with ink-jet-print (IJP) silver. At 80 MHz, the maximum measured PTE achieved is 2.781% at a 10 mm distance in the air for square planar-spiral coils.