Abstract
Vehicular lifelogging provides us with the opportunity to digitally record life associated with our journeys on the road. Driving on the road is an integral part of life, and it should be given unparalleled attention to log life bits on the road for many purposes, including safety, entertainment, memory augmentation, and analyzing driving behaviors. Various technologies have been used to assist people in capturing live events such as wearable devices, biometric devices, fitness devices, non-visual wearable, and unwearable devices. These devices have played a vital role in capturing the life events of individuals. However, little attention has been given to capturing and recording events related to a person’s life on the road. By using a vehicle’s integrated sensors along with other auxiliary devices, a vehicle can very conveniently capture personal as well as vehicle-related data of the driver. This research aims to propose a framework, termed as AutoLog, which is implemented in the Android platform and can be installed on any android-based smartphones/tablets or a vehicle’s infotainment system. AutoLog is capable of logging the driver activities and vehicle dynamics along with several varying environmental contexts. The proposed solution is evaluated through an empirical study by collecting data from 63 drivers. Different tests, i.e., descriptive tabulation, Cronbach alpha, Kendall’s tau-b, and Principal Component Factor Analysis (PCFA) have been carried out to analyze the data. These tests are significant, and the findings show that the use of AutoLog application has a positive perception in terms of attitude, ease of use, user satisfaction, and intention to use. Besides, the findings also prove that the proposed solution helped the drivers in improving risky driving behavior.
Highlights
The term Lifelogging refers to the use of computer technologies to capture and store information about a person’s daily life experiences in multimedia format for different usecases [1]
We have reported principal component factor analysis (PCFA) and found comparatively better than others
The following sub-sections are about the overall results obtained from our analysis and their discussion
Summary
The term Lifelogging refers to the use of computer technologies to capture and store information about a person’s daily life experiences in multimedia format for different usecases [1]. The advancements in capturing, processing, storing, and networking technologies have enabled the development of lifelogging systems to record unobtrusively and passively both lifelog and contextual information without requiring explicit user’s efforts and interventions. Lifelogging is the indiscriminate capturing of the totality of life experiences from various sources without having any understanding of use-cases or insights at the outset of lifelogging. Selective capture lifelogging is a domain-focused effort of logging experiences, information about specific aspects, or desired situations of a person with a clear prior understanding of the goals. Scope, use of ubiquitous and pervasive technologies, and passive and automatic capturing of the required lifelog data makes lifelogging different from PIM [5]
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