Internet of Things(IoT) has in recent years become a topic of broad and current interest. The purpose of this thesis is to anticipate weather conditions by constructing a system for collecting information about atmospheric pressure. The development of the system will solve the following problem: it should be possible to implement a system that allows for the collection of information from sensors through a mobile phone. The problem was solved through an iOS application together with a Micro Controller Unit (MCU) and a sensor. To collect weather data, the BME280, with its atmospheric pressure, temperature and humidity sensor, was used. Bluetooth was chosen for the interaction between the Automat and the iOS application. This proved to be a possible solution to a problem in a growing area of application. An advantage to this hardware solution is the mobility and flexibility of the Automat, making it ideal for mobile IoT solutions. Arduino is, however, the better choice for developers, as it has a larger community and clear documentation. / Internet of Things (IoT) har på senare år blivit ett alltmer omtalat område. Syftet med tesen är att förutspå väderförhållanden genom att konstruera ett IoT system som samlar in information om lufttryck, detta för att besvara frågeställningen: Det bör gå att samla in sensordata med hjälp av mobiltelefon. För att besvara detta följdes Ekholms modell för teknisk forskning och arbetsmetoden Scrum. Frågestallningen löstes genom en iOS applikation med tillhörande Microcontroller Unit(MCU) och sensor. För att samla in väderdata användes sensorn BME280, som har lufttrycks-, temperaturoch luftfuktighetssensorer, tillsammans med MCU:n Automat. För interaktionen mellan Automat och iOS applikationen tillämpades bluetooth-kommunikation. Detta var en möjlig lösning på ett problem i ett växande tillämpningsområde. Fördelar med denna lösning av hårdvara är att den är välanpassad till mobila IoT lösningar tack vare Automats minimala storlek i förhållande till funktionalitet. I många fall är däremot Arduino ett bättre val för utvecklaren, då den har större samfund och tydligare dokumentation.

<div>As wearable devices, like smartwatches and fitness monitors, gain popularity and are being touted for clinical purposes, evaluating the resilience and security of wearable operating systems (OSes) and their corresponding ecosystems becomes essential. One of the most dominant OSes for wearable devices is Wear OS, created by Google. Wear OS and Android (its counterpart OS for mobile devices) share similar features, but the unique characteristics and uses of wearable devices posses new challenges. For example, wearable applications are generally more dependent on device sensors, have complex communication patterns (both intra-device and inter-device), and are context-aware. Current research efforts on the Wear OS are more focused on the efficiency and performance of the OS itself, overlooking the resilience or security of the OS or its ecosystem.</div><div> </div><div>This dissertation introduces a systematic analysis to evaluate the Wear OS's resilience and security. The work is divided into two main parts. First, we focus our efforts on developing novel tools to evaluate the robustness of the wearable OS and uncover vulnerabilities and failures in the wearable ecosystem. We provide an assessment and propose techniques to improve the system's overall reliability. Second, we turn our attention to the security and privacy of smart devices. We assess the privacy and security of highly interconnected devices. We demonstrate the feasibility of privacy attacks under these scenarios and propose a defense mechanism to mitigate these attacks.</div><div> </div><div>For the resilience part, we evaluate the overall robustness of the Wear OS ecosystem using a fuzz testing-based tool [DSN2018]. We perform an extensive fault injection study by mutating inter-process communication messages and UI events on a set of popular wearable and mobile applications. The results of our study show similarities in the root cause of failures between Wear OS and Android; however, the distribution of exception differ in both OSes. Further, our study evidence that input validation has improved in the Android ecosystem with respect to prior studies. Then, we study the impact of the state of a wearable device on the overall reliability of the applications running in Wear OS [MobiSys2020]. We use distinguishable characteristics of wearable apps, such as sensor activation and mobile-wearable communication patterns, to derive a state model and use this model to target specific fuzz injection campaigns against a set of popular wearable apps. Our experiments revealed an abundance of improper exception handling on wearable applications and error propagation across mobile and wearable devices. Furthermore, our results unveiled a flawed design of the wearable OS, which caused the device to reboot due to excessive sensor use.</div><div><br></div><div>For the security and privacy part, we assess user awareness toward privacy risks under scenarios with multiple interconnected devices. Our results show that a significant majority of the users have no reservation while granting permission to their devices. Furthermore, users tend to be more conservative while granting permission on their wearables. Based on the results of our study, we demonstrate the practicability of leaking sensitive information inferred from the user by orchestrating an attack using multiple devices. Finally, we introduce a tool based on NLP (Natural Language Processing) techniques that can aid the user in detecting this type of attack.</div>

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

In partnership with iMonSys - an emerging company in the passive care field - a new system, 'Verity', is being developed to fulfil the role of a passive behaviour monitoring and alert detection device, providing an unobtrusive level of care and assessing an individual's changing behaviour and health status whilst still allowing for independence of its elderly user. In this research, a Hidden Markov Model incorporating Fuzzy Logic-based sensor fusion is created for the behaviour detection within Verity, with a method of Fuzzy-Rule induction designed for the system's adaptation to a user during operation. A dimension reduction and classification scheme utilising Curvilinear Distance Analysis is further developed to deal with the recognition task presented by increasingly nonlinear and high dimension sensor readings, and anomaly detection methods situated within the Hidden Markov Model provide possible solutions to identification of health concerns arising from independent living. Real-time implementation is proposed through development of an Instance Based Learning approach in combination with a Bloom Filter, speeding up the classification operation and reducing the storage requirements for the considerable amount of observation data obtained during operation. Finally, evaluation of all algorithms is completed using a simulation of the Verity system with which the behaviour monitoring task is to be achieved.

<p>  </p> <p>In this study, we present advancements in smart contact lenses, highlighting their potential as minimally or non-invasive diagnostic and drug delivery platforms. The eyes, rich in physiological and diagnostic data, make contact lens sensors an effective tool for disease diagnosis. These sensors, particularly smart contact lenses, can measure various biomolecules like glucose, urea, ascorbate, and electrolytes (Na+, K+, Cl-, HCO3-) in ocular fluids, along with physical biomarkers such as movement of the eye, intraocular pressure (IOP) and ocular surface temperature (OST).</p> <p>The study explores the use of continuous, non-invasive contact lens sensors in clinical or point-of-care settings. Although promising, their practical application is hindered by the developmental stage of the field. This thesis addresses these challenges by examining the integration of contact lens sensors, covering their working principle, fabrication, sensitivity, and readout mechanisms, with a focus on monitoring glaucoma and eye health conditions like dry eye syndrome and inflammation.</p> <p>Our design adapts these sensors to fit various corneal curvatures and thicknesses. The lenses can visually indicate IOP through microfluidic channels' mechanical deformation under ambulatory conditions. We also introduce a colorimetric hydrogel tear fluid sensor that detects pH, electrolytes, and ocular surface temperature, indicating conditions like dry eye disease and inflammation.</p> <p>The evaluation of these contact lens sensors includes in vivo/vitro biocompatibility, ex vivo functionality studies, and in vivo safety assessments. Our comprehensive analysis aims to enhance the practicality and effectiveness of smart contact lenses in ophthalmic diagnostics and therapeutics.</p>

Ergonomic assessments to determine risks of work-related musculoskeletal disorders as well as to compare designs of work-tasks and workstations, are imperative for high sustainability and productivity in any given industry. Hence, assessment tools that can objectively and reliably capture postures, joint angles and muscle activities play very important role in properly determining risks relating to work and various tasks. The introduction of direct measurement instruments/tools has been helping and continue to help improve upon observational assessment methods to attain objectivity and reliability. This project aimed at contributing to future improvements of industrial risk assessment measures in ergonomics by identifying and testing direct measurement instruments/tools that can enhance observational risk assessment methods and introduce a new way of signal processing, hence, reducing assessment time while increasing objectivity and reliability. Several candidate instruments were identified and out of the identified, ten were selected as potential candidates. Two out of the ten, Wergonic and ErgoHandMeter were then selected and tested on common observation risk assessment factors that could be measured and answers provided directly or by analyses. The Wergonic instrument was modified to enhance its measuring capability from one fully and partially two factors to six factors. New algorithms were also employed to analyse measurements of the ErgoHandMeter in order to answer questions regarding repetitive movements. The two instruments tested, are able to measure and provide results for six commonly and one rarely assessed biomechanical risk factors. By combining selected potential candidates, many of the commonly targeted biomechanical risk factors in observational instruments can be measured by the selected direct measurement instruments. However, some factors especially force measurement remain a challenge for measuring by direct wearable sensor instruments.

<p dir="ltr">Developing functional metallic nanostructured surfaces has seen significant growth in various applications, including sensors, electronics, and biomedical devices. However, conventional fabrication techniques for these nanostructures face limitations such as complexity, high costs, and unstable coatings. Laser-assisted surface processing has emerged as a promising solution to address these challenges by enabling localized processing and modification without altering bulk properties. This dissertation focuses on the development of multifunctional devices using selective laser processing of metallized surfaces, categorized into three routes. The first part explores the utilization of laser-induced oxides (LIO) for simple processing and formation of functional metal oxide nanostructures as electrochemical sensing elements. Different laser processing conditions were systematically studied for cost-effective metals like copper and nickel, evaluating their potential as non-enzymatic glucose sensors. The second part investigates laser selective processing for removing metal coatings on temperature-sensitive substrates, providing a cost-effective and scalable alternative to conventional photolithography and etching processes. Various laser processing conditions were examined to achieve selective patterning of metalized fabric structures for wearable electronics production. The third part explores localized laser processing to create intermetallic nanotexturing mixtures without altering bulk properties. The study involved silver spray- coating onto titanium implants, followed by a post-laser processing. The aim was to achieve simultaneous texturing and intermixing of silver in titanium alloy structures, enhancing antibacterial properties and bone mineralization while preserving mechanical properties.</p><p dir="ltr">Through the comprehensive examination of these three routes, this dissertation demonstrates the immense potential of commercial laser processing systems in the design, fabrication, and characterization of functional metallic nanostructured surfaces. It emphasizes the often-overlooked aspect of chemical alterations in laser-assisted surface processing, bridging the gap between physical and chemical modifications. The research opens new avenues for the development and optimization of multifunctional devices in electronics and biomedical applications.</p>

<p>  </p> <p>Hazard identification is one of the most significant components in safety management at construction jobsites to prevent undesired fatalities and injuries of construction workers. The current practice, which relies on a limited number of safety managers’ manual and subjective inspections, and existing research efforts analyzing workers’ physical and physiological signals have achieved limited success, leaving many hazards unidentified at the jobsites. Motivated by this critical need, this research aims to develop a human sensing network that allows for ubiquitous hazard identification in the construction workplace.</p> <p>To attain this overarching goal, this research analyzes construction workers’ collective EEG signals collected from wearable EEG sensors based on machine learning, virtual reality (VR), and advanced signal processing techniques. Three specific research objectives are: (1) establishing a relationship between EEG signals and the existence of construction hazards, (2) identifying correlations between EEG signals/physiological states (e.g., emotion) and different hazard types, and (3) developing an integrated platform for real-time construction hazard mapping and comparing the results developed based on VR and real-world experimental settings.</p> <p>Specifically, the first objective establishes the relationship by investigating the feasibility of identifying construction hazards using a binary EEG classifier developed in VR, which can capture EEG signals associated with perceived hazards. In the second objective, correlations are discovered by testing the feasibility of differentiating construction hazard types based on a multi-class classifier constructed in VR. In the first and second objectives, the complex relationships are also analyzed in terms of brain dynamics and EEG signal components. In the third objective, the platform is developed by fusing EEG signals with heterogeneous data (e.g., location), and the discrepancies in VR and real-world environments are quantitatively assessed in terms of hazard identification performance and human behavioral responses.</p> <p>The primary outcome of this research is that the proposed approach can be applied to actual construction jobsites and used to detect all potential hazards, which was challenging to be achieved based on the current practice and existing research efforts. Also, the human cognitive mechanisms revealed in this research discover new neurocognitive knowledge in construction workers’ hazard perception. As a result, this research contributes to enhancing current hazard identification capability and improving construction workers’ safety and health.</p>

Ankle exoskeletons are designed to help people with movement weakness to restore the walking ability . However, people with gait pathology, for instance, drop foot, usually have difficulties in lifting the front part of foot during gait. Thus, different from health subjects, both plantarflexion and dorsiflexion assistance are needed for them to walk better. The purpose of this thesis is to implement an EMG-­driven control strategy for a cable­driven ankle exoskeleton while exploring the use of reinforcement learning in exoskeleton control. The work uses an EMG­-driven musculoskeletal model to predict ankle joint torque. The model uses EMG signals from 4 lower­-limb muscles related to plantarflexion and dorsiflexion to obtain ankle torque and stiffness. The dynamic model for an ankle exoskeleton is built for simulation. The reinforcement learning controller is designed for the ankle exoskeleton tracking the desired ankle joint torques. Based on simulation results, two main conclusions can be drawn, one is that the proposed control strategy can provide precise torque assistance; the other is that using reinforcement learning to track the desired assistive trajectories is effective. / Ankel exoskeletons är utformade för att hjälpa människor med rörelsessvaghet att återställa gångförmågan. Men personer med gångpatologi, till exempel faller fot, har vanligtvis svårt att lyfta den främre delen av foten under gång. Således, annorlunda än hälsoämnen, behövs både plantarflexion- och dorsiflexionshjälp för att de ska kunna gå bättre. Syftet med denna avhandling är att implementera en EMG­-driven kontrollstrategi för ett kabeldrivet vristexoskelet samtidigt som man utforskar användningen av förstärkningsinlärning vid exoskeletskontroll. Arbetet använder en EMG­-driven muskuloskeletal modell för att förutsäga fotledets vridmoment. Modellen använder EMG-­signaler från 4 nedre extremiteter muskler relaterade till plantarflexion och dorsiflexion för att uppnå vridmoment och styvhet. Den dynamiska modellen för ett fotoskeleton är byggd för simulering. Förstärkningsinlärningskontrollern är utformad för fotledets exoskelett som spårar önskade vridmoment i fotleden. Baserat på simuleringsresultat kan två huvudsakliga slutsatser dras, en är att den föreslagna kontrollstrategin kan ge exakt momenthjälp; den andra är att det är effektivt att använda förstärkningslärande för att spåra de önskade hjälpbanorna.

The loss of mobility among the elderly has become a significant health and socio-economic concern worldwide. Poor mobility due to gradual deterioration of the musculoskeletal system causes older adults to be more vulnerable to serious health risks such as joint injuries, bone fractures and traumatic brain injury. The costs associated with the treatment and management of these injuries are a huge financial/social burden on the government, society and family. Knee is one of the key joints that bear most of the body weight, so its proper function is essential for good mobility. Further, Continuous monitoring of the knee joint can potentially provide important quantitative information related to knee health and mobility that can be utilized for health assessment and early diagnoses of mobility-related problems. In this research work, we developed an easy-to-use, low-cost, multi-sensor-based wearable device to monitor and assess the knee joint and proposed an analysis system to characterize and classify an individual’s knee joint features with respect to the baseline characteristics of his/her peer group. The system is composed of a set of different miniaturized sensors (inertial motion, temperature, pressure and galvanic skin response) to obtain linear acceleration, angular velocity, skin temperature, muscle pressure and sweat rate of a knee joint during different daily activities. A database is constructed from 70 healthy adults in the age range from 18 to 86 years using the combination of all signals from our knee joint monitoring system. In order to extract relevant features from the datasets, we employed computationally efficient methods such as complementary filter and wavelet packet decomposition. Minimum redundancy maximum relevance algorithm and principal component analysis were used to select key features and reduce the dimension of the feature vectors. The obtained features were classified using the support vector machine, forming two distinct clusters in the baseline knee joint characteristics corresponding to gender, age, body mass index and knee/leg health condition. Thus, this simple, easy‐to‐use, cost-effective, non-invasive and unobtrusive knee monitoring system can be used for real-time evaluation and early diagnoses of joint disorders, fall detection, mobility monitoring and rehabilitation. / Thesis / Master of Applied Science (MASc)