1,796 research outputs found

    Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

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    Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements

    Characterizing Sleep Patterns in Youth with CP and its Impact on Mood

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    Background. Cerebral palsy (CP) is a lifelong neurodevelopmental condition characterized by limitations in movement and posture (Oskoui et al., 2013; Rosenbaum et al., 2007). There is a growing consensus that sleep difficulties are common and life-long in individuals with CP (Lélis et al., 2016; Newman et al., 2006; Simard-Tremblay et al., 2011). These difficulties encompass various aspects such as sleep duration, sleep quality, staying asleep, and experiencing more difficulty getting up in the morning (Lélis et al., 2016; Newman et al., 2006); however, much remains unknown about the specific sleep patterns in CP and whether they are distinct from those observed in other conditions such as autism or fetal alcohol spectrum disorder (FASD). Additionally, the link between sleep and mood in CP is not well understood (Gadie et al., 2017). While in neurotypical youth, better sleep has been linked to improvements in social, emotional, and psychological well-being (e.g., mood), the extent to which sleep may impact mood within the context of CP remains uncertain (Hamilton et al., 2007). This manuscript-based thesis aims to address these significant gaps in knowledge by examining the sleep patterns in youth with CP and investigate the subsequent temporal association between sleep and mood. Methods. For this exploratory manuscript-based thesis, we analyzed secondary data from baseline questionnaires and weekly data (accelerometers and daily sleep diaries) collected from a larger study that examined the associations between physiological factors and mental health in youth with CP. In the first study, we investigated the sleep patterns of 45 youth with CP using caregiver and youth reports, the Child/Adolescent Sleep-Wake Scale (CSWS/ASWS), Insomnia Severity Index (ISI), and measurements from actigraphs that youth wore for one week. First, the sleep characteristics were described in relation to available demographic variables (e.g., sex, age, Gross Motor Functioning Classification System level [GMFCS]), using descriptive statistics. Second, to determine the impact of the presence of a mental health diagnosis on sleep patterns and problems, a hierarchical regression analyses was conducted. In the second study, we focused on a subsample of youth (n = 32) who had sufficient daily diaries of sleep and mood. In paper 2, the impact of intraindividual variability (IIV) in sleep patterns on mood (i.e., positive and negative affect) was examined using a series of fixed-effects multi-level modelling. Analyses included age, sex, and GMFCS as covariates as these factors contribute to sleep and mood. Results. In the first study of 45 youth, the average sleep duration was 10 hours per night (SD = 0:59), ranging from 7.5 to 12.85 hours. Youth experienced an average of 14 awakenings (>5 min) per night (SD = 5.3), which is substantially higher than previous literature in youth without CP. Most youth reported poor sleep quality based on sleep quality scores from the combined CSWS and ASWS (M = 3.67, SD = 1.24). Hierarchical linear regression analysis revealed a significant positive association between mental health diagnosis and insomnia severity, even after controlling for participant demographics (age, sex, GMFCS) (p = .010). For the second study, fixed-effect models were used to examine the association between IIV sleep duration and quality and next-day negative and positive affect over a 7-day period. While controlling for covariates, higher within-subjects variability of sleep quality was related to lower next-day negative mood (b = -.03, p < .001) and increased next-day positive mood (b = .05, p = .018). To determine the directionality of this association, mood variability predicting next day sleep was examined; however, only higher within-subject variability of negative mood was related to next-day sleep quality (b = -1.12, p = .011). Conclusions. This thesis is the first of its kind to examine the group and individual characteristics of sleep patterns among youth with CP (Study 1) and the temporal impact of IIV sleep on daily positive and negative affect (Study 2). Sleep is a complex phenomenon, and further investigation is necessary to understand the influence of various other factors, which were not available for this thesis. Nevertheless, sleep timing and sleep consistency may be important characteristics of sleep health. Overall, more research is needed to help inform prevention of mental health issues in this already vulnerable population and to help inform the development of supports for sleep

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Running to Your Own Beat:An Embodied Approach to Auditory Display Design

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    Personal fitness trackers represent a multi-billion-dollar industry, predicated on devices for assisting users in achieving their health goals. However, most current products only offer activity tracking and measurement of performance metrics, which do not ultimately address the need for technique related assistive feedback in a cost-effective way. Addressing this gap in the design space for assistive run training interfaces is also crucial in combating the negative effects of Forward Head Position, a condition resulting from mobile device use, with a rapid growth of incidence in the population. As such, Auditory Displays (AD) offer an innovative set of tools for creating such a device for runners. ADs present the opportunity to design interfaces which allow natural unencumbered motion, detached from the mobile or smartwatch screen, thus making them ideal for providing real-time assistive feedback for correcting head posture during running. However, issues with AD design have centred around overall usability and user-experience, therefore, in this thesis an ecological and embodied approach to AD design is presented as a vehicle for designing an assistive auditory interface for runners, which integrates seamlessly into their everyday environments

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    Human Activity Recognition and Fall Detection Using Unobtrusive Technologies

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    As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people

    Exploration of peripheral electrical stimulation adapted as a modulation tool for reciprocal inhibition through the activation of afferent fibers during gait

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    The most accessible manner to perform physical activity and allow locomotion in human beings is walking. This activity is allowed thanks to reciprocal Ia inhibition mechanism, controlled by the spinal and supraspinal inhibitory circuits. The idea of this mechanism is to deactivate the antagonist muscle while the agonist is being contracted, allowing the proper muscle coordination necessary to walk. The interruption of spinal fibers produced after Spinal Cord Injury, disrupt this control on reciprocal Ia inhibition. The result of this lack of control is a co-activation of antagonist muscles generating spasticity of lower limbs which induce walking impairments. The importance of walking recovery for the independence and society re-integration of patient, raise the quantity of emerging walking rehabilitation therapies. One of these therapies, the application of peripheral nerve stimulation, has demonstrated promising results although more studies are necessary. This theory is the base of this Master Thesis which aim is to develop and validate a gait neuromodu- lation platform that induce neuroplasticity of spinal circuits, improving reciprocal Ia inhibition. The idea of the platform is to deliver afferent stimulation into the Common Peroneal Nerve innervating Tibialis Anterior muscle, to induce reciprocal Ia inhibition onto the antagonist Soleus muscle. This platform has been validated in 20 healthy volunteers in order to assess its effectiveness. The first part of the experimental protocol is an off-line analysis of Gait Cycle to evaluate the activation of mus- cles during the different phases of this cycle. Then, there is an assessment of the activity of antagonist muscle previous to the stimulation intervention by using the analysis of soleus H-reflex. Posteri- orly, the afferent stimulation is applied during a 10 minutes treadmill training using three different strategies depending on patient: In-phase stimulation during swing phase, Out-of-phase stimulation during stance phase, and Control strategy to check if stimulation has a real effect. The final processes of experimental protocol are two different assessments of the soleus activity, one immediately after the intervention and other 30 minutes after to evaluate the duration of effects. The results obtained demonstrate that afferent electrical stimulation has a real effect on modulation of reciprocal Ia inhibition. On the one hand, when electrical stimulation is applied during the swing phase, there is an improvement of reciprocal Ia inhibition. On the other hand, when stimulation is delivered during the stance phase, there is a worsening of reciprocal Ia inhibition. These results conclude that afferent electrical stimulation, applied at the swing phase of gait cycle, is a promising strategy to induce reciprocal Ia inhibition in Spinal Cord Injury patients. The induc- tion of this inhibitory circuit will lead to the proper activation of muscles during walking, recovering impaired walkingLa forma más accesible de locomoción y actividad física en los seres humanos es caminar. Esta activi- dad se realiza gracias al mecanismo de inhibición recíproca, controlado por los circuitos inhibitorios espinales y supraespinales. La idea de este mecanismo es desactivar el músculo antagonista mientras se contrae el agonista, permitiendo la adecuada coordinación muscular durante la marcha. La interrupción de las fibras espinales tras una Lesión de la Médula Espinal desajusta el control de la inhibition reciprocal. El resultado de esta falta de control es una co-activación de los músculos antago- nistas generando espasticidad en las extremidades inferiores, lo que genera alteraciones en la marcha. La importancia de la recuperación de la marcha para lograr la independencia y la reintegración del paciente en la sociedad, ha incrementado el número de terapias emergentes en rehabilitación de la marcha. Una de estas terapias, la estimulación del nervio periférico, ha demostrado resultados prom- etedores. Esta teoría es la base de esta Tesis de Máster cuyo objetivo es desarrollar y validar una plataforma de neuromodulación de la marcha que induzca la neuroplasticidad de los circuitos espinales, mejorando los valores de inhibición recíproca. La idea es aplicar estimulación aferente en el Nervio Peroneo Común que inerva el músculo Tibial Anterior para inducir la inhibición recíproca en su músculo antagonista Soleo. Esta plataforma ha sido validada en 20 voluntarios sanos con el fin de evaluar su eficacia. La primera parte del protocolo experimental es un análisis del ciclo de la marcha para evaluar la activación de cada músculo durante las diferentes fases de este ciclo. Luego, previo a la intervención de estimu- lación, hay una evaluación de la actividad del músculo antagonista analizando el reflejo H del soleo. La intervención de estimulación aferente se aplica durante un entrenamiento de marcha con una du- ración de 10 minutos, utilizando tres estrategias diferentes dependiendo del paciente: estimulación ’In-phase’ durante la fase de oscilación, estimulación ’Out-of-phase’ durante la fase de postura, y ’Control’ para comprobar si la estimulación tiene un efecto real. Los procesos finales del protocolo son dos evaluaciones de la actividad del soleo, una inmediatamente después de la intervención y otra 30 minutos después para evaluar la duración de los efectos. Los resultados obtenidos demuestran que la estimulación eléctrica aferente tiene un efecto real en la modulación de la inhibición recíproca. Por un lado, cuando la estimulación eléctrica se aplica durante la fase de oscilación, hay una mejora de la inhibición recíproca. Por otro lado, cuando la estimulación se administra durante la fase de postura, hay un empeoramiento de la inhibición recíproca. Estos resultados concluyen que la estimulación eléctrica aferente, administrada en la fase de oscilación del ciclo de la marcha, es una estrategia prometedora para inducir la inhibición recíproca en pacientes con Lesión de la Médula Espinal. La inducción de este circuito inhibidor generará la adecuada acti- vación de los músculos durante la marcha, recuperando el ciclo de marcha normalLa manera més accessible de locomoció i activitat física en els éssers humans és caminar. Aquesta ac- tivitat es realitza gràcies al mecanisme d’inhibició recíproca, controlat pels circuits inhibitoris espinals i supraespinals. La idea d’aquest mecanisme és desactivar el múscul antagonista mentre es contrau l’agonista, permetent la coordinació muscular adequada durant la marxa. La interrupció de les fibres espinals després d’una lesió medul·lar desajusta el control de la inhibició reciprocal. El resultat d’aquesta manca de control és una coactivació dels músculs antagonistes gen- erant espasticitat a les extremitats inferiors, cosa que genera alteracions a la marxa. La importància de la recuperació de la marxa per a la independència i la reintegració del pacient a la societat, ha incrementat el nombre de teràpies emergents de rehabilitació de la marxa. Una daquestes teràpies, lestimulació del nervi perifèric, ha demostrat resultats prometedors. Aquesta teoria és la base dáquesta Tesi de Màster que té com a objectiu desenvolupar una plataforma de neuromodulació de la marxa que indueixi la neuroplasticitat dels circuits espinals, millorant els valors de inhibició recíproca. La idea és aplicar una estimulació aferent al Nervi Peroneal Comú que inerva el múscul Tibial Anterior per induir la inhibició recíproca al múscul antagonista Soli. Aquesta plataforma ha estat validada en 20 voluntaris sans per avaluar-ne l’eficàcia. La primera part del protocol experimental és una anàlisi del cicle de marxa per avaluar l’activació de cada múscul durant les diferents fases del cicle de la marxa. Després, amb la intervenció d’estimulació prèvia, hi ha una avaluació de l’activitat del múscul antagonista analitzant el reflex H del soli. La inter- venció d’estimulació aferent s’aplica durant un entrenament de marxa amb una durada de 10 min- uts, utilitzant tres estratègies diferents depenent del pacient: estimulació ’In-phase’ durant la fase d’oscil·lació, estimulació ’Out-of-phase’ durant la fase de postura, i ’Control’ per comprovar si la es- timulació té un efecte real. Els processos finals del protocol són dues avaluacions de l’activitat de soli, una immediatament després de la intervenció i una altra 30 minuts després per avaluar la durada dels efectes. Els resultats obtinguts demostren que l’estimulació elèctrica aferent té un efecte real en la modulació de la inhibició recíproca. D’una banda, quan s’aplica l’estimulació elèctrica durant la fase d’oscil·lació, hi ha una millora de la inhibició recíproca. D’altra banda, quan s’administra l’estimulació durant la fase de postura, hi ha un empitjorament de la inhibició recíproca. Aquests resultats conclouen que l’estimulació elèctrica aferent, a la fase d’oscil·lació del cicle de la marxa, és una estratègia prometedora per induir la inhibició recíproca en pacients amb lesió medul·lar. La inducció d’aquest circuit inhibidor generarà a l’activació adequada dels músculs durant la marxa, recuperant el cicle de marxa norma

    Reconstruction and Synthesis of Human-Scene Interaction

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    In this thesis, we argue that the 3D scene is vital for understanding, reconstructing, and synthesizing human motion. We present several approaches which take the scene into consideration in reconstructing and synthesizing Human-Scene Interaction (HSI). We first observe that state-of-the-art pose estimation methods ignore the 3D scene and hence reconstruct poses that are inconsistent with the scene. We address this by proposing a pose estimation method that takes the 3D scene explicitly into account. We call our method PROX for Proximal Relationships with Object eXclusion. We leverage the data generated using PROX and build a method to automatically place 3D scans of people with clothing in scenes. The core novelty of our method is encoding the proximal relationships between the human and the scene in a novel HSI model, called POSA for Pose with prOximitieS and contActs. POSA is limited to static HSI, however. We propose a real-time method for synthesizing dynamic HSI, which we call SAMP for Scene-Aware Motion Prediction. SAMP enables virtual humans to navigate cluttered indoor scenes and naturally interact with objects. Data-driven kinematic models, like SAMP, can produce high-quality motion when applied in environments similar to those shown in the dataset. However, when applied to new scenarios, kinematic models can struggle to generate realistic behaviors that respect scene constraints. In contrast, we present InterPhys which uses adversarial imitation learning and reinforcement learning to train physically-simulated characters that perform scene interaction tasks in a physical and life-like manner

    Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

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    General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

    The 2023 wearable photoplethysmography roadmap

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    Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
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