abstract: The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation. In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk. Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities. It is becoming increasingly necessary to trust but verify the received devices both at production and in the field. Unauthorized hardware or firmware modifications, known as Trojans, can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices. Since Trojans may be triggered in the field at an unknown instance, it is essential to detect their presence at run-time. However, it isn't easy to run sophisticated detection algorithms on these devices due to limited computational power and energy, and in some cases, lack of accessibility. Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain. In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory. When the device enters the test mode, a predefined test application is run on the device repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes. The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

Graphene is a two-dimensional carbon based material with unique properties, such as electrical and thermal conductivity. When a textile is coated with graphene, it becomes conductive, while remaining low weight, soft, breathable, flexible, and stretchable. The purpose of this thesis is to investigate what products are suitable to be made with graphene textiles, by using the method Material Driven Design (MDD). Reflections are also made to determine how this method is affected by being applied to a two-dimensional material. To help with this, three kinds of graphene textiles from the company Grafren AB are investigated; conductive textiles, heatable textiles, and textile sensors. The product goal is to develop a portfolio containing 5-8 conceptual products based on these graphene textiles. The process includes conducting an investigation of the technical properties of the material, a user study, and a benchmarking study. This is done to understand the limitations and opportunities of the material, how it is perceived, and what similar materials there are on the market. After that, the material's characteristics are reflected upon to establish a vision for how it should be used in future applications. Then, to follow that vision, a user study is conducted to investigate how people perceive different materials and products, in order to create design guidelines to ensure that the material and product are perceived as intended. Next, concepts are developed according to the previously determined guidelines. To achieve this, idea generating workshops are conducted, where 14 concepts are selected for further development. The portfolio is then created, meant to inspire further usage of the material. It contains the following seven concepts. A heatable textile meant for cooking on camping trips. A fabric containing sensors that can notify when it is damaged. A keyboard made of fabric, for an easy and comfortable use and transportation. A stroller with sensors and heaters, for a more comfortable and safe user experience. A conductive jacket that can electrocute mosquitoes that come in contact with it. Pressure sensors in a carpet that can keep track of the people inside and provide assistance in emergencies. Gloves with sensors in them that can translate sign language live to text or speech. Since MDD heavily focuses on the sensorial qualities and physical characteristics of the material, the method needs to be adapted to become useful when working with such a versatile two-dimensional material. Fortunately, most adaptations can be made fairly easily. The timing of each step should also be considered, to ensure that the vision and guidelines can be made specific enough to be useful.

Biomechanical Big Data is of great significance to precision health applications, among which we take special interest in Physical Activity Detection (PAD). In this study, we have performed extensive research on deep learning-based PAD from biomechanical big data, focusing on the challenges raised by the need of real-time edge inference. First, considering there are many places we can place the motion sensors, we have thoroughly compared and analyzed the location difference in terms of deep learning-based PAD performance. We have further compared the difference among six sensor channels (3-axis accelerometer and 3-axis gyroscope). Second, we have selected the optimal sensor and the optimal sensor channel, which can not only provide sensor usage suggestions but also enable ultra-low-power application on the edge. Third, we have investigated innovative methods to minimize the training effort of the deep learning model, leveraging the transfer learning strategy. More specifically, we propose to pre-train a transferable deep learning model using the data from other subjects and then fine-tune the model using limited data from the target-user. In such a way, we have found that, for single-channel case, the transfer learning can effectively increase the deep model performance even when the fine-tuning effort is very small. This research, demonstrated by comprehensive experimental evaluation, have shown the potential of ultra-low-power PAD with minimized sensor stream and minimized training effort.

Although in many of the tasks in robotics, what is sought mainly includes accuracy, precision, flexibility, adaptivity, etc., yet in wearable robotics, there are some other aspects as well that could distinguish a reliable and promising approach. The three key elements that are addressed are as follows: control, actuation, and sensors. Where the goal for each of the previously mentioned objectives is to find a solution/design compatible with humans. A possible way to understand the human motor behaviours is to generate them on human-like robots. Biologically inspired action generation is promising in control of wearable robots as they provide more natural movements. Furthermore, wearable robotics shows exciting progress, also with its design. Soft exosuits use soft materials to build both sensors and actuators. This work investigates an adaptive representation model for actions in robotics. The concrete action model is composed of four modularities: pattern selection, spatial coordination, temporal coordination, and sensory-motor adaptation. Modularity in motor control might provide us with more insights about action learning and generalisation not only for humanoid robots but also for their biological counterparts. Successfully, we tested the model on a humanoid robot by learning to perform a variety of tasks (push recovery, walking, drawing, grasping, etc.). In the next part, we suggest several soft actuation mechanisms that overcome the problem of holding heavy loads and also the issue of on-line programming of the robot motion. The soft actuators use textile materials hosting thermoplastic polyurethane formed as inflatable tubes. Tubes were folded inside housing channels with one strain-limited side to create a flexor actuator. We proposed a new design to control the strained side of the actuator by adding four textile cords along its longitudinal axis. As a result, the actuator behaviour can be on-line programmed to bend and twist in several directions. In the last part of this thesis, we organised piezoresistive elements in a superimposition structure. The sensory structure is used on a sensory gripper to sense and distinguish between pressure and curvature stimuli. Next, we elaborated the sensing gripper by adding proximity sensing through conductive textile parts added to the gripper and work as capacitive sensors. We finally developed a versatile soft strain sensor that uses silicone tubes with an embedded solution that has an electrical resistance proportional to the strain applied on the tubes. Therefore, an entirely soft sensing glove exhibits hand gestures recognition. The proposed combinations of soft actuators, soft sensors, and biologically inspired action representation might open a new perspective to obtain smart wearable robots. / Obwohl bei vielen Aufgaben in der Robotik vor allem Genauigkeit, Präzision, Flexibilität, Anpassungsfähigkeit usw. gefragt sind, gibt es in der Wearable-Robotik auch einige andere Aspekte, die einen zuverlässigen und vielversprechenden Ansatz kennzeichnen. Die drei Schlüsselelemente, sind die folgenden: Steuerung, Aktuatoren und Sensoren. Dabei ist das Ziel für jedes der genannten Elemente, eine menschengerechte Lösung und ein menschengerechtes Design zu finden. Eine Möglichkeit, die menschliche Motorik zu verstehen, besteht darin, sie auf menschenähnlichen Robotern zu erzeugen. Biologisch inspirierte Bewegungsabläufe sind vielversprechend bei der Steuerung von tragbaren Robotern, da sie natürlichere Bewegungen ermöglichen. Darüber hinaus zeigt die tragbare Robotik spannende Fortschritte bei ihrem Design. Zum Beispiel verwenden softe Exoskelette weiche Materialien, um sowohl Sensoren als auch Aktuatoren zu erschaffen. Diese Arbeit erforscht ein adaptives Repräsentationsmodell für Bewegungen in der Robotik. Das konkrete Bewegungsmodell besteht aus vier Modularitäten: Musterauswahl, räumliche Koordination, zeitliche Koordination und sensorisch-motorische Anpassung. Diese Modularität in der Motorsteuerung könnte uns mehr Erkenntnisse über das Erlernen und Verallgemeinern von Handlungen nicht nur für humanoide Roboter, sondern auch für ihre biologischen Gegenstücke liefern. Erfolgreich testeten wir das Modell an einem humanoiden Roboter, indem dieser gelernt hat eine Vielzahl von Aufgaben auszuführen (Stoß-Ausgleichsbewegungen, Gehen, Zeichnen, Greifen, etc.). Im Folgenden schlagen wir mehrere weiche Aktuatoren vor, welche das Problem des Haltens schwerer Lasten und auch die Frage der Online- Programmierung der Roboterbewegung lösen. Diese weichen Aktuatoren verwenden textile Materialien mit thermoplastischem Polyurethan, die als aufblasbare Schläuche geformt sind. Die Schläuche wurden in Gehäusekanäle mit einer dehnungsbegrenzten Seite gefaltet, um Flexoren zu schaffen. Wir haben ein neues Design vorgeschlagen, um die angespannte Seite eines Flexors zu kontrollieren, indem wir vier textile Schnüre entlang seiner Längsachse hinzufügen. Dadurch kann das Verhalten des Flexors online programmiert werden, um ihn in mehrere Richtungen zu biegen und zu verdrehen. Im letzten Teil dieser Arbeit haben wir piezoresistive Elemente in einer Überlagerungsstruktur organisiert. Die sensorische Struktur wird auf einem sensorischen Greifer verwendet, um Druck- und Krümmungsreize zu erfassen und zu unterscheiden. Den sensorischen Greifer haben wir weiterentwickelt indem wir kapazitiv arbeitende Näherungssensoren mittels leitfähiger Textilteile hinzufügten. Schließlich entwickelten wir einen vielseitigen weichen Dehnungssensor, der Silikonschläuche mit einer eingebetteten resistiven Lösung verwendet, deren Wiederstand sich proportional zur Belastung der Schläuche verhält. Dies ermöglicht einem völlig weichen Handschuh die Erkennung von Handgesten. Die vorgeschlagenen Kombinationen aus weichen Aktuatoren, weichen Sensoren und biologisch inspirierter Bewegungsrepräsentation kann eine neue Perspektive eröffnen, um intelligente tragbare Roboter zu erschaffen.

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.

An increasing amount of data are collected through wearable devices during ambulatory, and long-term monitoring of biological signals, adoption of persuasive technology and dynamics of clinical trials information sharing - all of that changes the possible clinical intervention. Moreover, more and more smartphone apps are hitting the market as they become a tool in daily life for many people around the globe. All of these applications are generating a tremendous amount of data, that is difficult to process using traditional methods, and asks for engagement of advanced methods of data processing. For recruiting patients, this calls for a shift from traditional methods of engaging patients to modern communication platforms such as social media, that are providing easy access to up- to-date information on an everyday basis. These factors make the clinical study progression demanding, in terms of unified participant management and processing of connected digital resources. Some clinical trials put a strong accent on remote sensing data and patient engagement through their smartphones. To facilitate this, a direct participant messaging, where the researchers give support, guidance and troubleshooting on a personal level using already adopted communication channels, needs to be implemented. Since the...

<div>Currently, people in North America spend roughly 90% of their time indoors. Therefore, it is important to create comfortable, healthy, and productive indoor environments for the occupants. Unfortunately, our resulting indoor environments are still very poor, especially in multi-occupant rooms. In addition, energy consumption in residential and commercial buildings by HVAC systems and lighting accounts for about 41% of primary energy use in the US. However, the current methods for simulating building energy consumption are often not accurate, and various types of occupant behavior may explain this inaccuracy.</div><div>This study first developed artificial neural network models for predicting thermal comfort and occupant behavior in indoor environments. The models were trained by data on indoor environmental parameters, thermal sensations, and occupant behavior collected in ten offices and ten houses/apartments. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6 °C to 25 °C in winter and from 20.6 °C to 25.6 °C in summer. We also found that the comfortable air temperature in the residences was 1.7 °C lower than that in the offices in winter, and 1.7 °C higher in summer. The reason for this difference may be that the occupants of the houses/apartments were responsible for paying their energy bills. The comfort zone obtained by the ANN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that using behaviors was wider.</div><div>Then this study used the EnergyPlus program to simulate the energy consumption of HVAC systems in office buildings. Measured energy data were used to validate the simulated results. When using the collected behavior from the offices, the difference between the simulated results and the measured data was less than 13%. When a behavioral ANN model was implemented in the energy simulation, the simulation performed similarly. However, energy simulation using constant thermostat set point without considering occupant behavior was not accurate. Further simulations demonstrated that adjusting the thermostat set point and the clothing could lead to a 25% variation in energy use in interior offices and 15% in exterior offices. Finally, energy consumption could be reduced by 30% with thermostat setback control and 70% with occupancy control.</div><div>Because of many contextual factors, most previous studies have built data-driven behavior models with limited scalability and generalization capability. This investigation built a policy-based reinforcement learning (RL) model for the behavior of adjusting the thermostat and clothing level. We used Q-learning to train the model and validated with collected data. After training, the model predicted the behavior with R2 from 0.75 to 0.80 in an office building. This study also transferred the behavior knowledge of the RL model to other office buildings with different HVAC control systems. The transfer learning model predicted with R2 from 0.73 to 0.80. Going from office buildings to residential buildings, the transfer learning model also had an R2 over 0.60. Therefore, the RL model combined with transfer learning was able to predict the building occupant behavior accurately with good scalability, and without the need for data collection.<br></div><div><div>Unsuitable thermostat settings lead to energy waste and an undesirable indoor environment, especially in multi-occupant rooms. This study aimed to develop an HVAC control strategy in multi-occupant offices using physiological parameters measured by wristbands. We used an ANN model to predict thermal sensation from air temperature, relative humidity, clothing level, wrist skin temperature, skin relative humidity and heart rate. Next, we developed a control strategy to improve the thermal comfort of all the occupants in the room. The control system was smart and could adjust the thermostat set point automatically in real time. We improved the occupants’ thermal comfort level that over half of the occupants reported feeling neutral, and fewer than 5% still felt uncomfortable. After coupling with occupancy-based control by means of lighting sensors or wristband Bluetooth, the heating and cooling loads were reduced by 90% and 30%, respectively. Therefore, the smart HVAC control system can effectively control the indoor environment for thermal comfort and energy saving.</div><div>As for proposed studies in the future, at first, we will use more advanced sensors to collect more kinds of occupant behavior-related data. We will expand the research on more occupant behavior related to indoor air quality, noise and illuminance level. We can use these data to recognize behavior instead of questionnaire survey now. We will also develop a personalized zonal control system for the multi-occupant office. We can find the number and location of inlet diffusers by using inverse design.</div></div>

<div>Recently, energy crisis and environment pollution has become global issues and there is a great demand for developing green and renewable energy system. At the same time, advancements in materials production, device fabrication, and flexible circuit has led to the huge prosperity of wearable devices, which also requires facile and efficient approaches to power these ubiquitous electronics. Piezoelectric nanogenerators and triboelectric nanogenerators have attracted enormous interest in recent years due to their capacity of transferring the ambient mechanical energy into desired electricity, and also the potential of working as self-powered sensors. However, there still exists some obstacles in the aspect of materials synthesis, device fabrication, and also the sensor performance optimization as well as their application exploration.</div><div>Here in this research, several different materials possessing the piezoelectric and triboelectric properties (selenium nanowires, tellurium nanowires, natural polymer hydrogel) have been successfully synthesized, and also a few novel manufacturing techniques (additive manufacturing) have been implemented for the fabrication of wearable sensors. The piezoelectric and triboelectric nanogenerators developed could effectively convert the mechanical energy into electricity for an energy conversion purpose, and also their application as self-powered human-integrated sensors have also been demonstrated, like achieving a real-time monitoring of radial artery pulses. Other applications of the developed sensors, such as serving as electric heaters and infrared cloaking devices are also presented here. This research is expected to have a positive impact and immediate relevance to many societally pervasive areas, e.g. energy and environment, biomedical electronics, and human-machine interface.</div><div><br></div>

Smartwatches can collect health data, location data and other sensitive information about users, and privacy concerns arise. This thesis aimed to investigate how older people (50-80 years old) in Sweden behave when it comes to privacy and health data. The data were analyzed according to the privacy paradox, which describes the discrepancy between how people behave and how they intend to behave in relation to risk and trust. The research approach was qualitative, and twelve semi-structured interviews were conducted. The interviews were coded and thematized following the chosen theory. Among the twelve participants in the study, a majority did not see, understand, or behave consciously towards the risks of sharing health data. Instead, trust was related to both the disclosure behavior and the intentional behavior among several of the participants in this study. This study indicates that for some of the participants, there are also other factors that determine their behavior, and the privacy paradox alone is not complete. Four of the findings when it comes to participants' behavior towards their health data and privacy were: trust-based decisions, lack of knowledge, low value of personal data, and value benefits more than privacy. Among several of the participants in this study, when trust towards an actor increase, the participant’s risk awareness decreases. It can be discussed whether the participants in the study value the opportunities more than the risks, and this impacts their behavior. Most of the participants think that sharing location data infringes more on their privacy than sharing health data, and self-education might be a reason the behavior and the level of privacy differ among the participants.

Syftet med denna undersökning är att undersöka hur glanceable feedback kan implementeras i aktivitetsenheter för hundar för att skapa en bättre användarupplevelse och minska den teknologiska störningen i kommunikationen mellan människa och hund. Tidigare studier visar att utvecklingen hos aktivitetsenheter för hundar är långt ifrån lika framgångsrik jämfört med den för aktivitetsenheter för människor (Ramokapane, van der Linden &amp; Zamansky, 2019; Väätäjä et al. 2018; Zamansky; et al. 2019). Väätäjä, et al. (2018) har påpekat vikten av att teknologin bakom aktivitetsenheten, så som mobiltelefoner, inte ska komma mellan hundägaren och hunden. Den får inte agera som ett störningsmoment som distanserar kommunikation eller interaktion mellan hund och människa (Väätäjä et al. 2018). Detta är idag oundvikligt i dagens aktivitetsenheter för hundar då användaren är helt beroende av mobiltelefonens tillhörande applikation (Väätäjä et al. 2018). Genom frågeställningen (Hur kan glanceable feedback appliceras i aktivitetsenhet för hundar för att ge hundägaren direkt information om hundens aktivitet?) undersöks hur glanceable feedback kan appliceras i aktivitetsenheter för hundar och undvika att kommunikation mellan hund och ägare störs med tekniken. Resultatet är aktivitetsenheten Good Boy. Enheten är försedd med en display och ett mekaniskt hjul som byter mellan de olika kategorierna distans/steg, puls/kroppstemperatur, info/kontaktuppgifter och led/lampa. / The purpose of this study is to investigate how glanceable feedback can be implemented in activity units for dogs to create a better user experience and reduce the technological disruption in human-dog communication. Previous studies show that the development of activity units for dogs is far from as successful as activity units for humans (Ramokapane, van der Linden &amp; Zamansky, 2019; Väätäjä et al. 2018; Zamansky et al. 2019). Väätäjä, et al. (2018) have pointed out the importance of the technology behind the activity, such as the mobile phone, unit not coming between the dog owner and the dog. It must not act as a disturbance that distances communication or interaction between dog and human (Väätäjä et al. 2018). This is inevitable in today's activity units for dogs as the user is completely dependent on the mobile phone's associated application (Väätäjä et al. 2018).  The question (How can glanceable feedback be applied in an activity unit for dogs to give the dog owner direct information on the dog's activity?) Investigates how glanceable feedback can be applied in activity units for dogs and avoid that communication between dog and owner is disturbed by technology. The result is the Good Boy activity unit. The unit is equipped with a display and a mechanical wheel that switches between the different categories distance / step, pulse / body temperature, info /contact information and LED/lamp.