Experiences of aiding autobiographical memory using the sensecam

Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

Serious Games Application for Memory Training Using Egocentric Images

Mild cognitive impairment is the early stage of several neurodegenerative diseases, such as Alzheimer's. In this work, we address the use of lifelogging as a tool to obtain pictures from a patient's daily life from an egocentric point of view. We propose to use them in combination with serious games as a way to provide a non-pharmacological treatment to improve their quality of life. To do so, we introduce a novel computer vision technique that classifies rich and non rich egocentric images and uses them in serious games. We present results over a dataset composed by 10,997 images, recorded by 7 different users, achieving 79% of F1-score. Our model presents the first method used for automatic egocentric images selection applicable to serious games.Comment: 11 page

What are the limits to time series based recognition of semantic concepts?

Most concept recognition in visual multimedia is based on relatively simple concepts, things which are present in the image or video. These usually correspond to objects which can be identified in images or individual frames. Yet there is also a need to recognise semantic con- cepts which have a temporal aspect corresponding to activities or com- plex events. These require some form of time series for recognition and also require some individual concepts to be detected so as to utilise their time-varying features, such as co-occurrence and re-occurrence patterns. While results are reported in the literature of using concept detections which are relatively specific and static, there are research questions which remain unanswered. What concept detection accuracies are satisfactory for time series recognition? Can recognition methods perform equally well across various concept detection performances? What affecting factors need to be taken into account when building concept-based high-level event/activity recognitions? In this paper, we conducted experiments to investigate these questions. Results show that though improving concept detection accuracies can enhance the recognition of time series based concepts, they do not need to be very accurate in order to characterize the dynamic evolution of time series if appropriate methods are used. Experimental results also point out the importance of concept selec- tion for time series recognition, which is usually ignored in the current literature

Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns

First-person stories can be analyzed by means of egocentric pictures acquired throughout the whole active day with wearable cameras. This manuscript presents an egocentric dataset with more than 45,000 pictures from four people in different environments such as working or studying. All the images were manually labeled to identify three patterns of interest regarding people's lifestyle: socializing, eating and sedentary. Additionally, two different approaches are proposed to classify egocentric images into one of the 12 target categories defined to characterize these three patterns. The approaches are based on machine learning and deep learning techniques, including traditional classifiers and state-of-art convolutional neural networks. The experimental results obtained when applying these methods to the egocentric dataset demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing and Beyond, 19th International Conference on Image Analysis and Processing (ICIAP), September 201

Untangling the ATR-CHEK1 network for prognostication, prediction and therapeutic target validation in breast cancer

Background: ATR-Chk1 signalling network is critical for genomic stability. ATR-Chk1 may be deregulated in breast cancer and have prognostic, predictive and therapeutic significance. Patients and methods: We investigated ATR and phosphorylated CHK1Ser345 protein (pChk1) expression in 1712 breast cancers (Nottingham Tenovus series). ATR and Chk1 mRNA were evaluated in 1950 breast cancers (METABRIC cohort). Pre-clinically, biological consequences of ATR gene knockdown or ATR inhibition by small molecule inhibitor (VE-821) were investigated in MCF-7 and MDA-MB-231 breast cancer cell lines and in non-tumorigenic breast epithelial cells (MCF10A). Results: High ATR and high cytoplasmic pChk1 expression was significantly associated with higher tumour stage, higher mitotic index, pleomorphism and lymphovascular invasion. In univariate analysis, high ATR and high cytoplasmic pChk1 protein expression was associated with shorter breast cancer specific survival (BCSS). In multivariate analysis, high ATR remains an independent predictor of adverse outcome. At the mRNA level, high Chk1 remains associated with aggressive phenotypes including lymph node positivity, high grade, Her-2 overexpression, triple-negative phenotype and molecular classes associated with aggressive behaviour and shorter survival.. Pre-clinically, Chk1 phosphorylation at serine 345 following replication stress (induced by gemcitabine or hydroxyurea treatment) was impaired in ATR knockdown and in VE-821 treated breast cancer cells. Doxycycline inducible knockdown of ATR suppressed growth, which was restored when ATR was re-expressed. Similarly, VE-821 treatment resulted in a dose dependent suppression of cancer cell growth and survival (MCF7 and MDA-MB-231) but had no effect on non-tumorigenic breast epithelial cells (MCF10A). Conclusions: We provides evidence that ATR and Chk1 are promising biomarkers and rational drug target for personalized therapy in breast cancer

Everyday concept detection in visual lifelogs: validation, relationships and trends

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept. We conduct further analysis on the temporal consistency, co-occurance and trends within the detected concepts to more extensively investigate the robustness of the detectors within this novel domain. We additionally present future applications of concept detection within the domain of lifelogging

Exploring the context of sedentary behaviour in older adults (what, where, why, when and with whom)

BACKGROUND: Older adults are the most sedentary segment of the population. Little information is available about the context of sedentary behaviour to inform guidelines and intervention. There is a dearth of information about when, where to intervene and which specific behaviours intervention should target. The aim of this exploratory study was to obtain objective information about what older adults do when sedentary, where and when they are sedentary and in what social context. METHODS: The study was a cross-sectional data collection. Older adults (Mean age = 73.25, SD ± 5.48, median = 72, IQR = 11) volunteers wore activPAL monitors and a Vicon Revue timelapse camera between 1 and 7 days. Periods of sedentary behaviour were identified using the activPAL and the context extracted from the pictures taken during these periods. Analysis of context was conducted using the Sedentary Behaviour International Taxonomy classification system. RESULTS: In total, 52 days from 36 participants were available for analysis. Participants spent 70.1 % of sedentary time at home, 56.9 % of sedentary time on their own and 46.8 % occurred in the afternoon. Seated social activities were infrequent (6.9 % of sedentary bouts) but prolonged (18 % of sedentary time). Participants appeared to frequently have vacant sitting time (41 % of non-screen sedentary time) and screen sitting was prevalent (36 % of total sedentary time). CONCLUSIONS: This study provides valuable information to inform future interventions to reduce sedentary behaviour. Interventions should consider targeting the home environment and focus on the afternoon sitting time, though this needs confirmation in a larger study. Tackling social isolation may also be a target to reduce sedentary time

Association of genetic liability for psychiatric disorders with accelerometer-assessed physical activity in the UK Biobank.

Levels of activity are often affected in psychiatric disorders and can be core symptoms of illness. Advances in technology now allow the accurate assessment of activity levels but it remains unclear whether alterations in activity arise from shared risk factors for developing psychiatric disorders, such as genetics, or are better explained as consequences of the disorders and their associated factors. We aimed to examine objectively-measured physical activity in individuals with psychiatric disorders, and assess the role of genetic liability for psychiatric disorders on physical activity. Accelerometer data were available on 95,529 UK Biobank participants, including measures of overall mean activity and minutes per day of moderate activity, walking, sedentary activity, and sleep. Linear regressions measured associations between psychiatric diagnosis and activity levels, and polygenic risk scores (PRS) for psychiatric disorders and activity levels. Genetic correlations were calculated between psychiatric disorders and different types of activity. Having a diagnosis of schizophrenia, bipolar disorder, depression, or autism spectrum disorders (ASD) was associated with reduced overall activity compared to unaffected controls. In individuals without a psychiatric disorder, reduced overall activity levels were associated with PRS for schizophrenia, depression, and ASD. ADHD PRS was associated with increased overall activity. Genetic correlations were consistent with PRS findings. Variation in physical activity is an important feature across psychiatric disorders. Whilst levels of activity are associated with genetic liability to psychiatric disorders to a very limited extent, the substantial differences in activity levels in those with psychiatric disorders most likely arise as a consequences of disorder-related factors

Coordination cages as permanently porous ionic liquids

Porous materials are widely used in industry for applications that include chemical separations and gas scrubbing. These materials are typically porous solids, although the liquid state can be easier to manipulate in industrial settings. The idea of combining the size and shape selectivity of porous domains with the fluidity of liquids is a promising one and porous liquids composed of functionalized organic cages have recently attracted attention. Here we describe an ionic-liquid, porous, tetrahedral coordination cage. Complementing the gas binding observed in other porous liquids, this material also encapsulates non-gaseous guests—shape and size selectivity was observed for a series of isomeric alcohols. Three gaseous chlorofluorocarbon guests, trichlorofluoromethane, dichlorodifluoromethane and chlorotrifluoromethane, were also shown to be taken up by the liquid coordination cage with an affinity that increased with their size. We hope that these findings will lead to the synthesis of other porous liquids whose guest-uptake properties may be tailored to fulfil specific functions

Developing a Method to Test the Validity of 24 Hour Time Use Diaries Using Wearable Cameras: A Feasibility Pilot

Self-report time use diaries collect a continuous sequenced record of daily activities but the validity of the data they produce is uncertain. This study tests the feasibility of using wearable cameras to generate, through image prompted interview, reconstructed 'near-objective' data to assess their validity. 16 volunteers completed the Harmonised European Time Use Survey (HETUS) diary and used an Autographer wearable camera (recording images at approximately 15 second intervals) for the waking hours of the same 24-hour period. Participants then completed an interview in which visual images were used as prompts to reconstruct a record of activities for comparison with the diary record. 14 participants complied with the full collection protocol. We compared time use and number of discrete activities from the diary and camera records (using 10 classifications of activity). In terms of aggregate totals of daily time use we found no significant difference between the diary and camera data. In terms of number of discrete activities, participants reported a mean of 19.2 activities per day in the diaries, while image prompted interviews revealed 41.1 activities per day. The visualisations of the individual activity sequences reveal some potentially important differences between the two record types, which will be explored at the next project stage. This study demonstrates the feasibility of using wearable cameras to reconstruct time use through image prompted interview in order to test the concurrent validity of 24-hour activity time-use budgets. In future we need a suitably powered study to assess the validity and reliability of 24-hour time use diaries