Creating Human Digital Memories for a Richer Recall of Life Experiences

Human digital memories focus on documenting our lifetime. This outlet allows us to capture and bring together information that is related to almost any aspect of our lives. Creating these memories allows us to recall experiences, seamlessly; and to re-live specific events, using detailed information about those experiences. The evolution of smart devices enables any object to provide us with information. With all of this data at our disposal, new opportunities are arising to incorporate this data into our digital memories. Consequently, the challenge is to develop a platform, capable of linking captured information together, to form feature rich digital memories of human experiences. This paper presents DigMem, a platform for creating human digital memories, using pervasive devices and linked data. Information is semantically structured to create temporal “memory boxes”. A working prototype has been successfully developed, which demonstrates the approach

A User-Centred Approach to Reducing Sedentary Behaviour

The use of digital technologies in the administration of healthcare is growing at a rapid rate. However, such platforms are often expensive. As people are living longer, the strain placed on hospitals is increasing. It is evident that a usercentric approach is needed, which aims to prevent illness before a hospital visit is required. As such, with the levels of obesity rising, preventing this illness before such resources are required has the potential to save an enormous amount of time and money, whilst promoting a healthier lifestyle. New and novel approaches are needed, which are inexpensive and pervasive in nature. One such approach is to use human digital memories. This outlet provides visual lifelogs, composed of a variety of data, which can be used to identify periods of inactivity. This paper explores how the DigMem system is used to successfully recognise activity and create temporal memory boxes of human experiences, which can be used to monitor sedentary behaviour

A User-Centred Approach to Reducing Sedentary Behaviour

The use of digital technologies in the administration of healthcare is growing at a rapid rate. However, such platforms are often expensive. As people are living longer, the strain placed on hospitals is increasing. It is evident that a usercentric approach is needed, which aims to prevent illness before a hospital visit is required. As such, with the levels of obesity rising, preventing this illness before such resources are required has the potential to save an enormous amount of time and money, whilst promoting a healthier lifestyle. New and novel approaches are needed, which are inexpensive and pervasive in nature. One such approach is to use human digital memories. This outlet provides visual lifelogs, composed of a variety of data, which can be used to identify periods of inactivity. This paper explores how the DigMem system is used to successfully recognise activity and create temporal memory boxes of human experiences, which can be used to monitor sedentary behaviour

A User-Centred Approach to Reducing Sedentary Behaviour

The use of digital technologies in the administration of healthcare is growing at a rapid rate. However, such platforms are often expensive. As people are living longer, the strain placed on hospitals is increasing. It is evident that a usercentric approach is needed, which aims to prevent illness before a hospital visit is required. As such, with the levels of obesity rising, preventing this illness before such resources are required has the potential to save an enormous amount of time and money, whilst promoting a healthier lifestyle. New and novel approaches are needed, which are inexpensive and pervasive in nature. One such approach is to use human digital memories. This outlet provides visual lifelogs, composed of a variety of data, which can be used to identify periods of inactivity. This paper explores how the DigMem system is used to successfully recognise activity and create temporal memory boxes of human experiences, which can be used to monitor sedentary behaviour

The Big Data Obstacle of Lifelogging

Living in the digital age has resulted in a data rich society where the ability to log every moment of our lives is now possible. This chronicle is known as a human digital memory and is a heterogeneous record of our lives, which grows alongside its human counterpart. Managing a lifetime of data results in these sets of big data growing to enormous proportions; as these records increase in size the problem of effectively managing them becomes more difficult. This paper explores the challenges of searching such big data sets of human digital memory data and posits a new approach that treats the searching of human digital memory data as a machine learning problem

Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach

The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internet-enabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of ‘understanding’ the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively

Monitoring and Reducing Sedentary Behavior in the Elderly with the Aid of Human Digital Memories

A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem. The effect of leading an increasing sedentary lifestyle is also not evident straightaway. Effects of this behavior often occur over years and decades, as opposed to days or months. Therefore, there is very little willingness to change, if instant results are not seen. There is a need to provide a mechanism that is able to monitor an individual and provide a visual indication of his or her behavior. It is envisioned that the area of human digital memories is capable of providing such a system. This article explores how sedentary behavior and journey information can be collected, from different environments, so that an illustration of a user's habits can be seen and changes can occur. A successful prototype has also been developed that evaluates the applicability of the approach. Copyright © 2013, Mary Ann Liebert, Inc. 2013

Runners’ Engagement and Social Support Practices: Exploring the Uses and Role of Online Activities

Tracking technologies that monitor exercise and health have grown in popularity, while online communities have emerged to encourage healthier lifestyles. Runners display high engagement levels with these technologies, however, studies are inconclusive about the effectiveness of these tools, and half of tracking device owners stop use within three months. This exploratory study analyses the engagement practices of UK based long distance runners with tracking technologies and online communities in order to explore how these provide social support for runners. Data were derived from nine interviews. Findings identify distinct use practices associated with social networking sites, running club online communities and tracking applications. Social support is not as prominent as social comparison, identity formation, motivation and information gathering for runners. The nature of the community, co-present connections between individuals and running experience have implications for engagement and support practices, while emotional attachment to data represents an opportunity for development

Creating human digital memories for a richer recall of life experiences

Human digital memories focus on documenting our lifetime. This outlet allows us to capture and bring together information that is related to almost any aspect of our lives. Creating these memories allows us to recall experiences, seamlessly; and to re-live specific events, using detailed information about those experiences. The evolution of smart devices enables any object to provide us with information. With all of this data at our disposal, new opportunities are arising to incorporate this data into our digital memories. Consequently, the challenge is to develop a platform, capable of linking captured information together, to form feature rich digital memories of human experiences. This paper presents DigMem, a platform for creating human digital memories, using pervasive devices and linked data. Information is semantically structured to create temporal 'memory boxes'. A working prototype has been successfully developed, which demonstrates the approach