Industrie 4.0 und aktuelle Entwicklungen in dem Bereich der produzierenden Unternehmen erfordern hohe Anpassungsleistungen von Menschen und von Maschinen gleichermaßen. In Smart Factories werden Produktionsmitarbeiter zu Wissensarbeitern. Dazu bedarf es neben neuen, intelligenten, technischen Lösungen auch neuer Ansätze für Arbeitsorganisation, Trainings- und Qualifizierungskonzepte, die mit adaptierbaren technischen Systemen flexibel zusammenarbeiten. Das durch die EU geförderte Projekt Factory2Fit entwickelt Lösungen für die Mensch-Technik-Interaktion in automatisierten Produktionssystemen, welche eine hohe Anpassungsfähigkeit an die Fähigkeiten, Kompetenzen und Präferenzen der individuellen Mitarbeiter bieten und damit gleichzeitig den Herausforderungen einer höchst kundenindividuellen Produktion gewachsen sind. Im vorliegenden Beitrag werden die grundlegenden Ziele und Ideen des Projektes vorgestellt sowie die Ansätze des Quantified-self im Arbeitskontext, die adaptive Automatisierung inklusive der verschiedenen Level der Automation sowie die spezifische Anwendung des partizipatorischen Designs näher beleuchtet. In den nächsten Arbeitsschritten innerhalb des Projektes gilt es nun, diese Konzepte um- und einzusetzen sowie zu validieren. Die interdisziplinäre Arbeitsweise sowie der enge Kontakt zwischen Wissenschafts-, Entwicklungs- und Anwendungspartnern sollten dazu beitragen, den Herausforderungen bei der Realisierung erfolgreich zu begegnen und zukunftsträchtige Smart Factory-Lösungen zu implementieren. Das Projekt Factory2Fit wird im Rahmen von Horizon 2020, dem EU Rahmenprogramm für Forschung und Innovation (H2020/2014-2020), mit dem Förderkennzeichen 723277 gefördert.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this paper, we propose a technological solution integrated to a wearable device that allows measuring some physiological variables such as body mass index (BMI), steps walked in a determined day, burned calories, blood pressure and other risk factors associated with the Framingham´s score. The objective of this article is to identify the evolutionary pattern of the Framingham’s score each day in order to determine a presumptive diagnosis of high blood pressure. The technological solution was validated in the social insurance of a public hospital in Lima, Perú. The preliminary results obtained in a diagnostic test show a sensitivity level of 83.33%, a level of precision better than a traditional Framingham´s score for presumptive diagnosis of high blood pressure. Our proposal contributes to the patient’s awareness about the bad routine habits related to lifestyle and promotes the empowerment of data in order to make some changes that influence on the reduction of cardiovascular disease risk. / Revisión por pares

Regular physical activity, timely medication, controlled diet, and blood glucose monitoring is crucial for any diabetic patient. Laxity on following these treatment regimens can cause severe health complexity. Moreover, A physician’s surveillance on a patient, based on the patient’s real-time progress is difficult with the existing health care system. This research aims to provide a more accurate objective data in real-time to the physicians to help both patients and providers. The data being generated is mined later to investigate interesting questions regarding diabetic care. The resultant system is a mobile healthcare monitoring system for type – 2 diabetic patients that traces patients daily progress. Although many mobile apps provide self-monitoring tools for the patient, an interactive platform for monitoring all relevant parameters of diabetes where patients and physicians both are end users is unique. The Android app is designed with 3 major modules and two submodules: 1. Carb Intake Tracker (CIT), 2. Blood Glucose Tracker (BGT), 3. Physical Activity Tracker (PAT), 4. Medicine and 5. Blood Glucose (BG) reading reminder. Since Carb is an important factor for a diabetic patient’s meals, the CIT provides a platform to record daily meals from which the patient can see the total carb intake. Through BGT, patients can record their fasting or non-fasting blood glucose reading. The PAT collects a patient’s movement data via Bluetooth from a pair of wearable insole devices, and processing the data identifies and records the current activity. The PAT can detect if the patient is active in sedentary, as well as the type of exercise done by the patient. Using BG reminder and medicine reminder, the patient can set reminders which supports the apps self-monitoring aspect. All the data collected by CIT, BGT, and PAT are stored in Microsoft Azure cloud database, an authorized physician can access the database and see graphical statistics of a patient’s diet, physical activity, and glycemic index level. The app portrays statistics of carbs taken over a period, calories burned, and Glucose level trends through graphical representation. This has two advantages: 1. Patients can improve lifestyle observing records and following reminders, 2. Physicians can prescribe actions perceiving a patient’s trends over time. This research presents unique collaborative interaction between diabetic patients and physicians to create a real time patient portal based on android APIs and wearable devices.

當今運動健身蔚為風潮,加上網路資源普及,許多人藉由數位教學影片鍛鍊體 魄,不但能自由安排時間,也能在家參與課程,成為新世代的學習方式。傳統上,運 動健身較好的方式為教練在旁協助,除了口頭給予即時的指示,還能直接以身體觸碰 學員姿勢不良的部位,引導其肢體伸展、調整身體重心、提醒放鬆過於緊繃之部位, 然而新世代的學習方式透過教學影片無法立即對運動者當下的肢體動作做出反應,過 程中全倚賴運動者本身對於肢體的認知,往往和影片中教練的動作有落差而不自知, 因此,本研究期望藉由觸覺回饋輔助使用者做出正確的姿勢或動作。 過去針對觸覺回饋的研究相當地多,甚至可回溯至1950年代,然而將觸覺回饋應 用於運動指引的研究近年才漸漸出現,其應用層面僅限於提示作用,無法引導使用者 該如何動作,且目前回饋方式仍需倚賴受測者記憶動作與觸覺回饋的對應關係,無法 直覺做出反應。本研究模仿肌肉群收縮帶動肢體運動之方式,設計人工外部肌肉引導 手臂旋前旋後動作,人工外部肌肉包含步進馬達產生拉力、魚線及鬆緊帶模擬肌肉分 佈、收縮以及袖套包覆手臂帶動旋轉,系統設計歷經三版本的演進,最終設計出一套 具引導效果的觸覺回饋穿戴式裝置。 系統評估共邀請10位受測者進行實驗,結果證實此套裝置能有效提供方向性指示 (正確率98%),且受測者普遍反應裝置提供的回饋方式相當直覺,手臂會有被帶動 的感覺,能馬上知道該如何轉動手臂。實驗更進一步測試引導手臂轉動特定角度,實 驗結果效果也相當好,平均誤差在3度以內,此外,亦探討實驗過程中受測者對觸覺回 饋的行為反應,作為日後系統改良或觸覺回饋設計的參考。 / Nowadays, exercise and fitness have become a growing trend. Since the access to the internet resources is very easy and popular, many people choose to do exercise through digital online videos, which not only they can arrange their own exercising schedule, but also they can learn the courses at home. Traditionally, a better way for exercise learning is getting assistance from a professional coach, who can give instruction immediately, and adjust by direct body contact right away while the exercisers act incorrectly. However, the online video can not accomplish the purpose. On the condition that the exercisers rely only on the cognition of their own bodies they might not notice their posture different from the video. This research aimed to provide guidelines to do the correct posture or movement through tactile feedback. From past till now, the researches of tactile feedback are of considerable numbers, we can find the related researches back to 1950s. Recently it starts to be applied in exercising guiding. However, the applications only provide passive instructions, which require users to memorize the relationship between the tactile feedbacks and the correspond actions. Users are unable to react by instinct. In this research, we imitate the way of body movement driven by the muscles contraction. We design artificial external muscles on a sleeve to guide forearm pronation and supination. The wearable tactile feedback sleeve consists of stepper motors to provide pulling force, fishing wires and elastics to imitate muscle contraction to drive the forearm to roll. This system design has been revised three times, and we finally established a wearable tactile feedback device which has guiding effect. 10 participants are recruited for the experiments. The result showed that this device can guide forearm rolling successfully (the accuracy is 98%). The participants commented that the feedback is very close to instinct. They felt their arm was guided by the device, and knew the exact moment to roll their forearm. In the second experiment, we tried to guide the forearm rolling for several target angles and the result was quite promising. The mean error is within 3 degrees. We also reported the participants’ reactions through our tactile feedback system. We will expand the system to guide the other parts of human body in the future.

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>

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.

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...

In this research, we aimed to develop thin-film devices on a polymer substrate and an alternative 3D-printed device with macroelectrodes for treating gastrointestinal (GI) conditions. First, the fabrication of thin-film devices was demonstrated on a softening thiol-ene/acrylate polymer utilizing titanium nitride (TiN) as electrode material. This was achieved by utilizing cleanroom fabrication processes such as photolithography, wet and dry etching. The functionality of the device was shown by performing electrochemical characterization tests, mainly cyclic voltammetry, electrochemical impedance spectroscopy, and voltage transient. We synthesized a novel thiol-ene/acrylate polymer based on 1,3,5-triallyl-1,3,5-triazine-2,4,6(1H,3H,5H)-trione (TATATO), trimethylolpropanetris (3-mercaptopropionate) (TMTMP), and polyethylene glycol diacrylate (PEGDA). We show that this stretchable shape memory polymer substrate is well suited for cleanroom processes. Finally, for the high throughput of the wearable devices with electrodes size 10 mm in diameter, we implemented single electrode fabrication using printed circuit boards (PCBs) and depositing gold (Au) and TiN on the plated side of PCBs utilizing the sputtering tool. This step was followed by the assembly of those single electrodes on the flexible 3D printed device. We showed that the TiN electrode material performed better in terms of charge storage capacity and charge injection capacity than the widely used stainless steel electrode material for wearables.

Industrie 4.0 und aktuelle Entwicklungen in dem Bereich der produzierenden Unternehmen erfordern hohe Anpassungsleistungen von Menschen und von Maschinen gleichermaßen. In Smart Factories werden Produktionsmitarbeiter zu Wissensarbeitern. Dazu bedarf es neben neuen, intelligenten, technischen Lösungen auch neuer Ansätze für Arbeitsorganisation, Trainings- und Qualifizierungskonzepte, die mit adaptierbaren technischen Systemen flexibel zusammenarbeiten. Das durch die EU geförderte Projekt Factory2Fit entwickelt Lösungen für die Mensch-Technik-Interaktion in automatisierten Produktionssystemen, welche eine hohe Anpassungsfähigkeit an die Fähigkeiten, Kompetenzen und Präferenzen der individuellen Mitarbeiter bieten und damit gleichzeitig den Herausforderungen einer höchst kundenindividuellen Produktion gewachsen sind. Im vorliegenden Beitrag werden die grundlegenden Ziele und Ideen des Projektes vorgestellt sowie die Ansätze des Quantified-self im Arbeitskontext, die adaptive Automatisierung inklusive der verschiedenen Level der Automation sowie die spezifische Anwendung des partizipatorischen Designs näher beleuchtet. In den nächsten Arbeitsschritten innerhalb des Projektes gilt es nun, diese Konzepte um- und einzusetzen sowie zu validieren. Die interdisziplinäre Arbeitsweise sowie der enge Kontakt zwischen Wissenschafts-, Entwicklungs- und Anwendungspartnern sollten dazu beitragen, den Herausforderungen bei der Realisierung erfolgreich zu begegnen und zukunftsträchtige Smart Factory-Lösungen zu implementieren. Das Projekt Factory2Fit wird im Rahmen von Horizon 2020, dem EU Rahmenprogramm für Forschung und Innovation (H2020/2014-2020), mit dem Förderkennzeichen 723277 gefördert.