Machine Learning Methods for functional Near Infrared Spectroscopy

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain. We propose machine learning methods that capture this spatial nature of the human brain activity using a novel preprocessing method that uses `Region of Interest\u27 based feature extraction. Experiments show that this method outperforms the F1 score achieved previously in classifying `low\u27 vs `high\u27 valence state of a user. We further our analysis by applying a Convolutional Neural Network (CNN) to the fNIRS data, thus preserving the spatial structure of the data and treating the data similar to a series of images to be classified. Going further, we use a combination of CNN and Long Short-Term Memory (LSTM) to capture the spatial and temporal behavior of the fNIRS data, thus treating it similar to a video classification problem. We show that this method improves upon the accuracy previously obtained by valence classification methods using EEG or fNIRS devices. Finally, we apply the above model to a problem in classifying combined task-load and performance in an across-subject, across-task scenario of a Human Machine Teaming environment in order to achieve optimal productivity of the system

Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Improving and Scaling Mobile Learning via Emotion and Cognitive-state Aware Interfaces

Massive Open Online Courses (MOOCs) provide high-quality learning materials at low cost to millions of learners. Current MOOC designs, however, have minimal learner-instructor communication channels. This limitation restricts MOOCs from addressing major challenges: low retention rates, frequent distractions, and little personalization in instruction. Previous work enriched learner-instructor communication with physiological signals but was not scalable because of the additional hardware requirement. Large MOOC providers, such as Coursera, have released mobile apps providing more flexibility with “on-the-go” learning environments. This thesis reports an iterative process for the design of mobile intelligent interfaces that can run on unmodified smartphones, implicitly sense multiple modalities from learners, infer learner emotions and cognitive states, and intervene to provide gains in learning. The first part of this research explores the usage of photoplethysmogram (PPG) signals collected implicitly on the back-camera of unmodified smartphones. I explore different deep neural networks, DeepHeart, to improve the accuracy (+2.2%) and robustness of heart rate sensing from noisy PPG signals. The second project, AttentiveLearner, infers mind-wandering events via the collected PPG signals at a performance comparable to systems relying on dedicated physiological sensors (Kappa = 0.22). By leveraging the fine-grained cognitive states, the third project, AttentiveReview, achieves significant (+17.4%) learning gains by providing personalized interventions based on learners’ perceived difficulty. The latter part of this research adds real-time facial analysis from the front camera in addition to the PPG sensing from the back camera. AttentiveLearner2 achieves more robust emotion inference (average accuracy = 84.4%) in mobile MOOC learning. According to a longitudinal study with 28 subjects for three weeks, AttentiveReview2, with the multimodal sensing component, improves learning gain by 28.0% with high usability ratings (average System Usability Scale = 80.5). Finally, I show that technologies in this dissertation not only benefit MOOC learning, but also other emerging areas such as computational advertising and behavior targeting. AttentiveVideo, building on top of the sensing architecture in AttentiveLearner2, quantifies emotional responses to mobile video advertisements. In a 24-participant study, AttentiveVideo achieved good accuracy on a wide range of emotional measures (best accuracy = 82.6% across 9 measures)

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Open-Source Face Recognition Frameworks: A Review of the Landscape

publishedVersio

Identity as a compass for understanding media choice

The changes to our socio-technological media environment over the past 30 years have heightened the interest in identity across the social sciences. The spread of networked digital communication technologies and mobile media have increased the urgency for media scholars to better understand how and why individuals consume media as they do. Several media choice scholars have recently started considering how individuals’ identity and self-concept relate to media choice, but have not yet systematically addressed how identity might be related. This dissertation takes the first steps toward advancing an identity-based approach to understanding individual media choice in the 21st century by: 1) Providing a thorough theoretical and conceptual review of identity theory (Burke & Stets, 2009) and the identity process; 2) By discussing media research in the context of identity theory and applying identity theory directly to media research, and; 3) By empirically testing multiple elements of identity theory in two original experimental designs. Results indicate that identity not only affects media choice, it also affects how individuals ascribe meaning to media content

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems