Investigating older and younger peoples’ motivations for lifelogging with wearable cameras

People have a natural tendency to collect things about themselves, their experiences and their shared experiences with people important to them, especially family. Similar to traditional objects such as photographs, lifelogs have been shown to support reminiscence. A lifelog is a digital archive of a person’s experiences and activities and lifelog devices such as wearable cameras can automatically and continuously record events throughout a whole day. We were interested in investigating what would motivate people to lifelog. Due to the importance of shared family reminiscence between family members we focused our study on comparing shared or personal motivations with ten older and ten younger family members. We found from our results that both older and younger adults were more likely to lifelog for the purposes of information sharing and that reviewing lifelog images supported family reminiscence, reflection and story-telling. Based on these findings, recommendations are made for the design of a novel intergenerational family lifelog system

A quantified past : fieldwork and design for remembering a data-driven life

PhD ThesisA ‘data-driven life’ has become an established feature of present and future technological visions. Smart homes, smart cities, an Internet of Things, and particularly the Quantified Self movement are all premised on the pervasive datafication of many aspects of everyday life. This thesis interrogates the human experience of such a data-driven life, by conceptualising, investigating, and speculating about these personal informatics tools as new technologies of memory. With respect to existing discourses in Human-Computer Interaction, Memory Studies and Critical Data Studies, I argue that the prevalence of quantified data and metrics is creating fundamentally new and distinct records of everyday life: a quantified past. To address this, I first conduct qualitative, and idiographic fieldwork – with long-term self-trackers, and subsequently with users of ‘smart journals’ – to investigate how this data-driven record mediates the experience of remembering. Further, I undertake a speculative and design-led inquiry to explore context of a ’quantified wedding’. Adopting a context where remembering is centrally valued, this Research through Design project demonstrates opportunities and develops considerations for the design of data-driven tools for remembering. Crucially, while speculative, this project maintains a central focus on individual experience, and introduces an innovative methodological approach ‘Speculative Enactments’ for engaging participants meaningfully in speculative inquiry. The outcomes of this conceptual, empirical and speculative inquiry are multiple. I present, and interpret, a variety of rich descriptions of existing and anticipated practices of remembering with data. Introducing six experiential qualities of data, and reflecting on how data requires selectivity and construction to meaningfully account for one’s life, I argue for the design of ‘Documentary Informatics’. This perspective fundamentally reimagines the roles and possibilities for personal informatics tools; it looks beyond the current present-focused and goal-oriented paradigm of a data-driven life, to propose a more poetic orientation to recording one’s life with quantified data

Designing and evaluating a user interface for continous embedded lifelogging based on physical context

PhD ThesisAn increase in both personal information and storage capacity has encouraged people to store and archive their life experience in multimedia formats. The usefulness of such large amounts of data will remain inadequate without the development of both retrieval techniques and interfaces that help people access and navigate their personal collections. The research described in this thesis investigates lifelogging technology from the perspective of the psychology of memory and human-computer interaction. The research described seeks to increase my understanding of what data can trigger memories and how I might use this insight to retrieve past life experiences in interfaces to lifelogging technology. The review of memory and previous research on lifelogging technology allows and support me to establish a clear understanding of how memory works and design novel and effective memory cues; whilst at the same time I critiqued existing lifelogging systems and approaches to retrieving memories of past actions and activities. In the initial experiments I evaluated the design and implementation of a prototype which exposed numerous problems both in the visualisation of data and usability. These findings informed the design of novel lifelogging prototype to facilitate retrieval. I assessed the second prototype and determined how an improved system supported access and retrieval of users’ past life experiences, in particular, how users group their data into events, how they interact with their data, and the classes of memories that it supported. In this doctoral thesis I found that visualizing the movements of users’ hands and bodies facilitated grouping activities into events when combined with the photos and other data captured at the same time. In addition, the movements of the user's hand and body and the movements of some objects can promote an activity recognition or support user detection and grouping of them into events. Furthermore, the ability to search for specific movements significantly reduced the amount of time that it took to retrieve data related to specific events. I revealed three major strategies that users followed to understand the combined data: skimming sequences, cross sensor jumping and continued scanning

Human activity recognition using a wearable camera

Tesi en modalitat cotutela Universitat Politècnica de Catalunya i Queen Mary, University of London. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA n° 2010-0012]Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad.Postprint (published version

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Case studies in therapeutic SenseCam use aimed at identity maintenance in early stage dementia

In the absence of a medical cure for memory loss new technologies specialised in pervasive imaging are being incorporated into interventions for dementia. The practice of lifelogging is a digital capture of life experiences typically through mobile devices such as SenseCam. The lightweight wearable digital camera passively captures about 3,000 images a day. Lifelogging results in personal, recent prompts, potentially encouraging sharing of personal memories. This research investigated the incorporation of lifelogging technology into a therapeutic approach aimed to support people with dementia by using the Case Study method, an exploratory and descriptive approach. The case study is a method of empirical inquiry that enables investigation of phenomenon within its real life context. SenseCam therapy aimed to stimulate the cognition of a person with dementia, with support of their personal identity as its primary goal. SenseCam images were used as cues to meaningful discussions about the person’s recent memories. The images enabled a construction of a particular version of the participants’ identities mainly based in their recent past. On the contrary participants seemed to valorise their identity of their distant past. The SenseCam identity also contained uncensored details from participants’ lives as revealed by review of SenseCam images. The exposing nature of SenseCam images posed risks to the users’ privacy and showed the potential ethical risks of using lifelogging technology with people with dementia. There is limited literature on the practical recommendations on how to use lifelogging devices and how they affect people with dementia. The results from this research indicate that a number of factors should be considered when using lifelogging technology with people with dementia. Firstly the contextual factors of people with dementia including the level of cognitive impairment, existing coping mechanisms and the interaction patterns with the carer need to be considered. Secondly the technology should be used within a therapeutic framework and tailored to suit the individual needs of both people with dementia and their carers. Lastly intimate and unexpected details from the participant’s life should be discussed in an ethical and sensitive manner. Implications of not working within these boundaries show clear potential for undermining the human rights and potentially the wellbeing of people with dementia

Towards a Practitioner Model of Mobile Music

This practice-based research investigates the mobile paradigm in the context of electronic music, sound and performance; it considers the idea of mobile as a lens through which a new model of electronic music performance can be interrogated. This research explores mobile media devices as tools and modes of artistic expression in everyday contexts and situations. While many of the previous studies have tended to focus upon the design and construction of new hardware and software systems, this research puts performance practice at the centre of its analysis. This research builds a methodological and practical framework that draws upon theories of mobile-mediated aurality, rhetoric on the practice of walking, relational aesthetics, and urban and natural environments as sites for musical performance. The aim is to question the spaces commonly associated with electronic music – where it is situated, listened to and experienced. This thesis concentrates on the creative use of existing systems using generic mobile devices – smartphones, tablets and HD cameras – and commercially available apps. It will describe the development, implementation and evaluation of a self-contained performance system utilising digital signal processing apps and the interconnectivity of an inter-app routing system. This is an area of investigation that other research programmes have not addressed in any depth. This research’s enquiries will be held in dynamic and often unpredictable conditions, from navigating busy streets to the fold down shelf on the back of a train seat, as a solo performer or larger groups of players, working with musicians, nonmusicians and other participants. Along the way, it examines how ubiquitous mobile technology and its total access might promote inclusivity and creativity through the cultural adhesive of mobile media. This research aims to explore how being mobile has unrealised potential to change the methods and experiences of making electronic music, to generate a new kind of performer identity and as a consequence lead towards a practitioner model of mobile music