Computer Vision for Lifelogging

Wang, Peng; Sun, Lifeng; Smeaton, Alan F.; Gurrin, Cathal

doi:10.1016/b978-0-12-813445-0.00009-5

Cited by 5 publications

(1 citation statement)

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“…Lifelogging applications collect a variety of data streams about an individual, which has the potential to offer unique insights into human behavior. There are many visual lifelogging applications and projects which aim to understand the user's life and activities by using the images taken by a wearable camera (E.g., GoPro) over a long period and the data collected could be used to identify events and daily activities happening in user's life for varied purposes [47][15][14] [46]. The daily activity recognition could be further enhanced by merging the visual log with other multi-sensory data that can be collected using smartphones, wearable devices, and different IoT systems [7].…”

Section: Related Work 21 Daily Events Aggregation: Lifelogsmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources and retrieve complex biological events, such as cardiovascular volume overload, using measured lifestyle events. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on the relevant biological systems; they are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using the event operator language. The interface events can then be utilized to provide a continuous estimate of the biological variables relevant to the user's health state. The event-based framework also makes it easier to estimate which event is causally responsible for a particular change in the individual's health state.

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Section: Related Work 21 Daily Events Aggregation: Lifelogsmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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Keystroke Dynamics as Part of Lifelogging

Smeaton

Krishnamurthy

Suryanarayana

2021

MultiMedia Modeling

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In this paper we present the case for including keystroke dynamics in lifelogging. We describe how we have used a simple keystroke logging application called Loggerman, to create a dataset of longitudinal keystroke timing data spanning a period of more than 6 months for 4 participants. We perform a detailed analysis of this data by examining the timing information associated with bigrams or pairs of adjacently-typed alphabetic characters. We show how there is very little day-on-day variation of the keystroke timing among the top-200 bigrams for some participants and for others there is a lot and this correlates with the amount of typing each would do on a daily basis. We explore how daily variations could correlate with sleep score from the previous night but find no significant relationship between the two. Finally we describe the public release of this data as well including as a series of pointers for future work including correlating keystroke dynamics with mood and fatigue during the day.

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