Automatically detecting "significant events" on SenseCam

SenseCam™ is a wearable, automatic camera with support for memory recall used as a lifelogging device. Recent and continuing work in Dublin City University to apply sophisticated time series analysis methods to the multiple time series generated on a Microsoft SenseCam™ have proved useful in detecting ‘‘Significant Events’’

Periodicity detection in lifelog data with missing and irregularly sampled data

Lifelogging is the ambient, continuous digital recording of a person’s everyday activities for a variety of possible applications. Much of the work to date in lifelogging has focused on developing sensors, capturing information, processing it into events and then supporting event-based access to the lifelog for applications like memory recall, behaviour analysis or similar. With the recent arrival of aggregating platforms such as Apple’s HealthKit, Microsoft’s HealthVault and Google’s Fit, we are now able to collect and aggregate data from lifelog sensors, to centralize the management of data and in particular to search for and detect patterns of usage for individuals and across populations. In this paper, we present a framework that detects both lowlevel and high-level periodicity in lifelog data, detecting hidden patterns of which users would not otherwise be aware. We detect periodicities of time series using a combination of correlograms and periodograms, using various signal processing algorithms. Periodicity detection in lifelogs is particularly challenging because the lifelog data itself is not always continuous and can have gaps as users may use their lifelog devices intermittingly. To illustrate that periodicity can be detected from such data, we apply periodicity detection on three lifelog datasets with varying levels of completeness and accuracy

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users