Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Quality of experience in affective pervasive environments

The confluence of miniaturised powerful devices, widespread communication networks and mass remote storage has caused a fundamental shift in the user interaction design paradigm. The distinction between system and user in pervasive environments is evolving into an increasingly integrated loop of interaction, raising a number of opportunities to provide enhanced and personalised experiences. We propose a platform, based on a smart architecture, to address the identified opportunities in pervasive computing. Smart systems aim at acting upon an environment for improving quality of experience: a subjective measure that has been defined as an emotional reaction to products or services. The inclusion of an emotional dimension allows us to measure individual user responses and deliver personalised services with the potential to influence experiences positively. The platform, Cloud2Bubble, leverages pervasive systems to aggregate user and environment data with the goal of addressing personal preferences and supra-functional requirements. This, combined with its societal implications, results in a set of design principles as a concrete fruition of design contractualism. In particular, this thesis describes: - a review of intelligent ubiquitous environments and relevant technologies, including a definition of user experience as a dynamic affective construct; - a specification of main components for personal data aggregation and service personalisation, without compromising privacy, security or usability; - the implementation of a software platform and a methodological procedure for its instantiation; - an evaluation of the developed platform and its benefits for urban mobility and public transport information systems; - a set of design principles for the design of ubiquitous systems, with an impact on individual experience and collective awareness. Cloud2Bubble contributes towards the development of affective intelligent ubiquitous systems with the potential to enhance user experience in pervasive environments. In addition, the platform aims at minimising the risk of user digital exposure while supporting collective action.Open Acces

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating has been the subject of extensive research within the fields of nutrition and behavioral science, indicating a strong connection between the two. Further, phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. However, limitations within the current body of literature include: i) the lack of investigation around the generalization of mood inference models trained with passive sensor data from a range of everyday life situations, to specific contexts such as eating, ii) no prior studies that use sensor data to study the intersection of mood and eating, and iii) the inadequate examination of model personalization techniques within limited label settings, as we commonly experience in mood inference. In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 329K mood reports incl. 24K mood-while-eating reports), containing both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. Additionally, we found that population-level (non-personalized) and hybrid (partially personalized) modeling techniques were inadequate for the commonly used three-class mood inference task (positive, neutral, negative). Furthermore, we found that user-level modeling was challenging for the majority of participants due to a lack of sufficient labels and data from the negative class. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user

NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture

Harnessing digital phenotyping to deliver real-time interventional bio-feedback

With the decreasing cost and increasing capability of sensor and mobile technology along with the proliferation of data from social media, ambient environment and other sources, new concepts for digital prognostic and technological quantification of wellbeing are emerging. These concepts are referred to as Digital Phenotyping. One of the main challenges facing these technologies development is connecting how to design an easy to use and personalized devices which benefits from interventional feedback by leveraging on-device processing in real-time. Tangible interfaces designed for wellbeing possess the capabilities to reduce anxiety or manage panic attacks, thus improving the quality of life of the general population and vulnerable members of society. Real-time Bio-feedback presents new opportunities in Artificial Intelligence (AI) with the possibility for mental wellbeing to be inferred automatically allowing interventional feedback to be automatically applied and for the feedback to be individually personalised. This research explores future directions for Bio-feedback including the opportunity to fuse multiple AI enabled feedback mechanisms that can then be utilised collectively or individually

Using wearables and user behavior on smartphones to help cope stress

Increasing stress levels in today's society is a subject about which we all have heard about. With stress affecting our behavior, thoughts, feelings and ultimately our health, we can use technological advances of today to help detect and cope its effects. In this context, wearables analysing person's state, in interaction with smartphones can help optimizing their welfare, and an approach of this will be the aim of this Bachelor's degree final thesis.Stress per se is not a negative fact, actually it is a survival mechanism in response to danger; we need it. Stress can show our ability to handle pressure without breaking, it provides us challenges and tests our adaptability to face them. However, prolonged and high stress levels might cause serious illnesses. In modern life, negative stress has become an extremely common problem. In this Bachelor thesis we propose the design of a stress detection and monitoring app, Shama, which uses data collected from the smartphone to infer the user?s daily stress state. If the user wears a wristband, Shama also extracts Heart Rate Variability (HRV) features to enhance the stress detection. The app proposal is partially validated by building a stress classification model with a Sequential Minimal Optimization (SMO) classifier, which provides an accuracy of almost 73%. We also present the user interface of Shama, with all its functionalities for stress coping and management, as well as for encouraging the user to incorporate daily healthy habits to reduce unnecessary and unhealthy stress levels.El estrés, de por sí, no es algo totalmente negativo, sino más bien un mecanismo de supervivencia que nos permite reaccionar delante del peligro. Así que de hecho, lo necesitamos. Además, puede ser constructivo y mostrarnos lo capaces que somos de soportar cierta presión, retándonos y poniendo a prueba nuestra flexibilidad frente a desafíos. Sin embargo, altos niveles de estrés o períodos prolongados bajo sus efectos, pueden tener consecuencias muy perjudiciales para nuestra salud, tanto la mental como la física. Y, desafortunadamente, hoy en día el estrés en un problema excesivamente extendido. En este trabajo de fin de grado proponemos el diseño teórico de Shama, una aplicación móvil capaz de detectar diariamente el estrés del usuario mediante información extraída a través de su móvil o de su pulsera inteligente, en caso que se disponga de una. Esta propuesta teórica es parcialmente validada construyendo un modelo de machine learning mediante Sequential Minimal Optimization (SMO) capaz de alcanzar una precisión de aproximadamente el 73% enfrente a un problema de clasificación binaria. Asimismo, Shama también proporciona monitorización al usuario para ayudarlo a lidiar con el estrés cuando se encuentra bajo sus efectos, y le motiva a incorporar hábitos diarios para mejorar su bienestar general.L'estrès, en si mateix, no és absolutament negatiu, sinó que és un mecanisme de resposta davant del perill i, de fet, el necessitem. També demostra la nostra capacitat de suportar certa pressió, ens aporta reptes i posa a prova la nostra flexibilitat a l'hora d'enfrontar-los. Tanmateix, alts nivells d'estrès en períodes prolongats poden tenir conseqüències altament prejudicials per a la nostra salut, tant mental com física. I, malauradament, avui dia l'estrès és un problema excessivament comú. En aquest treball de fi de grau proposem el disseny teòric de Shama, una aplicació mòbil capaç de detectar l'estrès diari de l'usuari mitjançant informació extreta del seu mòbil, així com d'una polsera intel·ligent en cas que l'usuari en disposi. Aquesta proposta d'app és parcialment validada amb la construcció d'un model de machine learning mitjançant Sequential Minimal Optimization (SMO), que té una precisió del 73% en un problema de classificació binària. Shama també monitora l'usuari per ajudar-lo a lidiar amb l'estrès un cop detectat i li proporciona eines per incorporar en el seu dia a dia hàbits per millorar el seu benestar general

Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing