Las enfermedades cardiacas hoy en día representan la principal causa de muerte de la población adulta a nivel nacional, según informa el Seguro Social de Salud (EsSalud). En base a información estadística, EsSalud indica que solo en la capital cada día se producen entre 4 y 5 infartos de miocardio, de los cuales más de dos tercios de ellos se presenta en varones. El proyecto elaborado brinda una herramienta para la medicina, que optimiza los procesos de monitorización del estrés cardiaco en los pacientes de forma permanente. Así mismo, la detección de anomalías en el comportamiento cardiaco. Adicionalmente, el modelo permite dar a conocer a los médicos la información de los pacientes en tiempo real, lo que permite que se tomen decisiones oportunas respecto a la salud de los pacientes. El presente proyecto se enfocará en el desarrollo de un modelo tecnológico para el monitoreo y análisis del nivel de estrés cardiaco utilizando wearables. El modelo tecnológico presente se subdivide entre el input, el output, una capa de soporte y 4 fases principales: 1. Captura; 2. Transmisión; 3. Procesamiento; 4. Destino. Se decidió por aplicar el modelo en tecnologías de código abierto, para que una vez demostrado su funcionamiento, esta pueda replicada en cualquier plataforma y tenga el mismo resultado, sin necesidad de encasillarlo a una única solución. La toma biométrica de pacientes en tiempo real, de un total de 72 horas monitoreadas por paciente, demostró que en promedio 93.67% del tiempo los pacientes estuvieron monitoreados y que el 12.50% de los pacientes presentaron una anomalía cardiaca. / Heart disease today represents the leading cause of death in the adult population nationwide, according to the Seguro Social de Salud (EsSalud). Based on statistical information, EsSalud indicates that only in the capital each day between 4 and 5 myocardial infarctions occur, of which more than two thirds of them occur in men. The project developed provides a tool for medicine, which optimizes the monitoring processes of cardiac stress in patients permanently. Likewise, the detection of abnormalities in cardiac behavior. Additionally, the model makes it possible to make patient information known to physicians in real time, which allows timely decisions to be made regarding the health of patients. This project will focus on the development of a technological model for the monitoring and analysis of the level of cardiac stress using wearables. The present technological model is subdivided between input, output, a support layer and 4 main phases: 1.Capture; 2.Transmission; 3.Processing; 4.Destination. It was decided to apply the model in open source technologies, so that once its operation has been demonstrated, it can be replicated on any platform and have the same result, without the need to lock it into a single solution. The biometric taking of patients in real time, out of a total of 72 hours monitored per patient, showed that on average 93.67% of the time the patients were monitored and that 12.50% of the patients had a cardiac abnormality. / Tesis

There is an increasing trend of mental health problems both globally and in Sweden. Moreover, in recent decades there has been an increase in screen time among adolescents. The present study aimed to examine the associations between screen time (i.e., smartphone, computer, and TV) and mental health problems among adolescents in Västmanland and to investigate if the association was different due to gender. A quantitative method with a cross-sectional design was applied. The study used secondary data from the Survey of Adolescent Life in Västmanland 2020. The sample consisted of 3880 adolescents from 9th grade in compulsory school and 2nd grade in upper secondary school. The results showed that high screen time on smartphone was associated with an increased probability for mental health problems in the total sample. In contrast, screen time on TV and computer showed no significant association with mental health problems. Thus, smartphone use was a significant contributor to mental health problems. Furthermore, for girls, high screen time on the smartphone, computer, and TV was associated with increased probability of mental health problems, whereas no significant associations were found among boys. In brief, this study’s findings suggest developing Swedish guidelines to regulate harmful effects from screen time.

Syftet med denna studie var att undersöka samband mellan stress, sömn och skärmtid på smarttelefon. Urvalet bestod av totalt 81 personer på ett universitet i södra Sverige. Två hypoteser testades. 1: hög grad av faktisk skärmtid är relaterat till en hög subjektiv stressupplevelse och hög grad av sömnbesvär. 2: Låg grad av stress är associerat med låg grad av sömnbesvär. Vid insamlingen av data användes, Perceived stress scale 10 (PSS-10) och Karolinska sleep questionnaire (KSQ). För att mäta skärmtid designades två frågor, en med syfte att mäta individers subjektiva upplevelse av mängden skärmtid och en för att mäta den faktiska mängden som kan utläsas via en funktion på individers smarttelefon. Resultatet för hypotes 1 visade att faktisk skärmtid varken samvarierade med stress eller sömnbesvär. Vidare jämfördes också gruppen hög faktisk skärmtid som översteg 29 h/vecka med stress och sömnbesvär. Resultatet indikerade att en hög faktisk skärmtid och stress inte korrelerade och hög faktisk skärmtid och sömnbesvär inte korrelerade. Resultatet för hypotes 2 visade en negativ signifikant korrelation mellan variablerna stress och sömnbesvär. Det innebar att höga värden på total KSQ, mindre sömnbesvär, korrelerar med låga värden på PSS-10, låg nivå av upplevd stress. Sammanfattningsvis visade studiens resultat att faktisk skärmtid inte samvarierade med varken stress eller sömnbesvär. Dock återfanns en korrelation mellan variablerna stress och sömnbesvär. / The purpose of this study was to investigate the relationship between stress, sleep and screen time on smartphones. The sample consisted of a total of 81 people at a university in southern Sweden. Two hypotheses were tested. 1: High degree of actual screen time is related to a high subjective stress experience and high degree of sleep disturbance. 2: Low levels of stress are associated with low levels of sleep disturbance. In the data collection, Perceived stress scale 10 (PSS-10) and Karolinska sleep questionnaire (KSQ) were used. To measure screen time, two questions were designed, one aimed at measuring individual's subjective experience of the amount of screen time and one to measure the actual amount that can be read through a function on an individual's smartphone. The result for Hypothesis 1 showed that actual screen time did not co-vary with stress or sleep disturbance. Furthermore, the group, high actual screen time exceeding 29 h / week was also compared with the variables stress and sleep disturbance. The result indicated that a high actual screen time and stress did not correlate, and high actual screen time and sleep disturbances did not correlate either. The result for Hypothesis 2 showed a negatively significant correlation between the variables stress and sleep disorders. This meant that high values of total KSQ, less sleep disturbances, correlated with low values of PSS-10, low levels of stress. In summary, the study's results showed that actual screen time did not correlate with neither stress nor sleep disturbance. However, a correlation was found between the variables stress and sleep disturbance.

The increasingly ubiquitous role of smartphones in our everyday lives causes concerns regarding our relationship with the devices. While some raise the question whether smartphones are addictive (Alter 2017; Lopez-Fernandez 2019), others regard this concern as the most recent manifestation of moral panics (Cashmore, Cleland & Dixon 2018; Leick 2019). Meanwhile advocates of the attention economy argument claim that the problem is the design of technology occupying users’ attention (CHT 2019a-d). Somewhere in between, media and communication studies search for empirical evidence. From this vantage point of ideas this study explores the role of Screen Time, shaping and being shaped by this discourse. As a feature of Apple’s iOS software it is supposed to support users in regulating their smartphone use. Applying the walkthrough method as proposed by Light, Burgess & Duguay (2018) combined with an analysis of user experiences, shows how the technology company shapes a concept of self-regulation for users to adopt to. A concept, which first and foremost follows corporate and not the users’ best interest. This thesis poses the the question whether we are slaves to our screens, but arrives at the conclusion that we carry chains of self-regulation. The question remains, how we can create more sustainable and meaningful environments for protecting our attention.

Tekniken hos mobiler och utvecklingen av programvaror för digitalafotogrammetriska tjänster har haft en snabb utveckling, vilket har gjort detmöjligt att enkelt kunna skapa 3D-modeller.Syftet med denna studie är att undersöka om det är möjligt att med hjälp avbildunderlag från en mobiltelefon kunna volymberäkna objekt så som t.ex.grushögar och diken. Den teknik som används är en automatiskbildjusteringsalgoritm som heter Structure from Motion (SfM). Från en seriedigitalafoton, som täcker objektet från olika vinklar och djup kan SfMidentifiera gemensamma punkter i fotona och sammanfläta dessa till ettpunkmoln som kan användas för att skapa en 3D-modell. Utifrån 3Dmodellen kan sedan en volymbestämning ske från mjukvaran AgisoftMetashape.Denna studie utför ett flertal olika tester för att utvärdera Structure fromMotion med bildunderlag från mobiltelefon för volymberäkning. Vissa objekthar att filmats med olika typer av telefoner och sedan jämförts mot terresterlaserskanning (TLS) som är en vanlig metod vid insamling av data förvolymberäkning. Volymberäkningarna har gjorts för både positiva ochnegativa volymer. Metoden har enbart använt sig av referensmått frånmåttband ute i fält. Detta gör att ingen avancerad mätutrustning har använts.Ett ljustest genomfördes för att se hur olika ljusförhållanden viddatainsamlandet kan påverka databearbetningen och volymberäkningen.Genom att beräkna skillnader i volym mellan 3D-modellerna kommer ettresultat tas fram.Resultaten från de 3D-modeller som skapats med dataunderlag från SfM gervolymskillnader i jämförelsen mot terrester laserskanning mellan 2,2% och6,8%. Dessa resultat är från en mur och ett dike som användes i studien,skillnaden motsvarar 0,031 m3 och 15,086 m3. Den negativa volym somberäknades, dvs. diket, är det objekt som ger störst avvikelse i volym, delsmellan de olika mobiltelefonerna, dels jämfört med TLS. Metoden ger enindikation på att det finns en stor möjlighet att kunna effektivisera processenför volymbestämning då metoden inte kräver speciella mätverktyg ellersärskilda förkunskaper vilket kan både spara tid och pengar. / The technology of smartphones and the development of software for digitalphotogrammetric services have developed rapidly, which has made it possibleto easily create 3D models.The purpose of this study is to investigate whether it is possible with the helpof image data from a mobile phone to be able to calculate the volume ofobjects such as e.g. gravel piles and ditches. The technology used is anautomatic image adjustment algorithm called Structure from Motion (SfM).From a series of digital photos, which cover the object from different anglesand depths, SfM can identify common points in the photos and intertwinethese into a point cloud that can be used to create a 3D model. Based on the3D model, a volume determination can then be made from the AgisoftMetashape software.This study performed a number of different tests to evaluate Structure fromMotion with image data from a smartphone for volume calculation. Someobjects have been filmed with different types of phones and then compared toterrestrial laser scanning (TLS) which is a common method of collecting datafor volume calculation. The volume calculations have been made for bothpositive and negative volumes. The method has only used referencemeasurements from measuring tapes in the field. This means that no advancedmeasuring equipment has been used. A light test was performed to see howdifferent light conditions during the data collection can affect the dataprocessing and volume calculation. By calculating differences in volumebetween the 3D models, a result will be produced.The results from 3D modeling created with data from SfM give a volumedifferences in the comparison with terrestrial laser scanning between 2.2%and 6.8%. These results are from a small stone wall and a ditch used in thestudy, the difference corresponds to 0.031 m3and 15.086 m3. The negativevolume that was calculated, ie. the ditch, is the object that gives the largestdeviation in volume, partly between the different mobile phones, partlycompared to TLS. The method gives an indication that there is a greatopportunity to be able to streamline the work process for volume calculationas the method does not require special measurement tools or special priorknowledge which can save both time and money.

The global average screen time on smartphones reaches up to 4 hours, revealed App Annie. In recent years, there have also been increasing concerns about the problematic use of smartphones and its impact on public health, such as sleep hygiene, cognitive capacity, and mental health. According to Tristan Harris, a former Design Ethicist at Google, enabling grayscale is a possible way to reduce screen time, because it makes the smartphone less attractive. Therefore, the aim of this thesis is to perform an experiment to confirm or reject the functionality of this recommendation and at the same time, through in-depth interviews with its participants, to describe how they perceive it and how it affects them.

No description available.

Many medical devices have been shifting to personal platforms such as smartphones due to its ubiquitous availability, variety of included sensors, robust communication, and user-friendliness. By utilizing smartphones as a medical sensing device should improve the early detection of abnormalities and the long-term monitoring of health conditions. Tissue abnormalities will be detected by touch sensation due to mechanical property changes within the tissue. However, touch sensation is unquantifiable and subjective. We integrate the smartphone with a tactile sensor to build a portable and personalized tissue assessment device based on changes in mechanical properties. The Smartphone Tactile Imaging Probe (STIP) is developed to quantify the mechanical properties of the tissue. The proposed system has a dual-sensing mode: compression-based sensing (STIP-C) and indentation-based sensing (STIP-I). STIP–C is designed to detect and measure the size and hardness of the inclusion. It assesses mechanical property changes caused by the tumor inside the tissue. STIP–I is designed to measure the pitting parameters and viscoelastic properties of the tissue. This system will assess the viscoelasticity changes caused by fluid retention within the tissue. STIP estimates mechanical and viscoelastic behavior changes in the tissue and provides the risk evaluation of an underlying health problem. Breast cancer risk assessment and edema severity level classification are the main applications of STIP. We estimate the breast cancer risk by incorporating the patient’s personal risk value into the STIP-C data associated with the tumor mechanical properties to improve the risk assessment accuracy. To classify the edema severity level, the STIP-I measures the pitting parameters and viscoelastic properties of the tissue. From these parameters, we build a Viscoelastic Pitting Recovery (VPR) model. The model illustrates the changes in tissue viscoelastic behavior associated with the edema severity level. Using the VPR model, we use the thresholding method to classify the edema cases. We also developed customized phantoms representing the different amounts of fluid retention in the tissue. The experimental result found a relationship between the amounts of pitted depth from STIP-I and the fluid amount of a phantom. In this dissertation, we developed and tested a portable tissue mechanical property estimation system. The interchangeable dual-mode STIP sensing probe and risk assessment methods were developed for the breast tumor malignancy and edema severity applications. / Electrical and Computer Engineering

The construction industry has lagged behind other industries in productivity growth rate. Earth-moving sites, and other practices where dumpers are used, are no exceptions. Such projects lack convenient and accurate solutions for utilization mapping and tracking of mass flows, which both currently and mainly rely on manual activity tracking. This study intends to provide insights of how autonomous systems for activity tracking of dumpers can contribute to the productivity at earthmoving sites. Autonomous systems available on the market are not implementable to dumper fleets of various manufacturers and model year, whereas this study examines the possibilities of using activity recognition by machine learning for a system based on smartphones mounted in the driver’s cabin. Three machine learning algorithms (naive Bayes, random forest and feed-forward backpropagation neural network) are trained and tested on data collected by smartphone sensors. Conclusions are that machine learning models, and particularly the neural network and random forest algorithms, trained on data from a standard smartphone, are able to estimate a dumper’s activities at a high degree of certainty. Finally, a market analysis is presented, identifying the innovation opportunity for a potential end-product as high. / Byggnadsbranschen har halkat efter andra branscher i produktivitetsökning. Markarbetesprojekt och andra arbeten där dumprar används är inga undantag. Sådana projekt saknar användarvänliga system för att kartlägga maskinutnyttjande och massaflöde. Nuvarande lösningar bygger framförallt på manuellt arbete. Denna studie syftar skapa kännedom kring hur autonoma system för aktivitetsspårning av dumprar kan öka produktiviteten på markarbetesprojekt. Befintliga autonoma lösningar är inte implementerbara på maskinparker med olika fabrikat eller äldre årsmodeller. Denna studie undersöker möjligheten att applicera aktivitetsigenkänning genom maskininlärning baserad på smartphones placerade i förarhytten för en sådan autonom lösning. Tre maskininlärningsalgoritmer (naive Bayes, random forest och backpropagation neuralt nätverk) tränas och testas på data från sensorer tillgängliga i vanliga smartphones. Studiens slutsatser är att maskininlärningsmodeller, i synnerhet neuralt nätverk och random forest-algoritmerna, tränade på data från vanliga smartphones, till hög grad kan känna igen en dumpers aktiviteter. Avslutningsvis presenteras en marknadsanalys som bedömer innovationsmöjligheten för en eventuell slutprodukt som hög.

This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS). At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms. Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication. The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure.