Lijuan Marissa Zhou

User Interaction Templates for the Design of Lifelogging Systems

A variety of life-tracking devices are being created to give opportunity to track our daily lives accurately and automatically through the application of sensing technologies. Technology allows us to automatically and passively record life activities in previously unimaginable detail, in a process called lifelogging. Captured materials may include text, photos/video, audio, location, Bluetooth logs and information from many other sensing modalities, all captured automatically by wearable sensors. Experience suggests that it can be overwhelming and impractical to manually scan through the full contents of these lifelogs. A promising approach is to apply visualization to large-scale data-driven lifelogs as a means of abstracting and summarizing information. In this chapter, we outline various UI templates that support different visualization schemes.

ZhiWo: activity tagging and recognition system for personal lifelogs

With the increasing use of mobile devices as personal recording, communication and sensing tools, extracting the semantics of life activities through sensed data (photos, accelerometer, GPS etc.) is gaining widespread public awareness. A person who engages in long-term personal sensing is engaging in a process of lifelogging. Lifelogging typically involves using a range of (wearable) sensors to capture raw data, to segment into discrete activities, to annotate and subsequently to make accessible by search or browsing tools. In this paper, we present an intuitive lifelog activity recording and management system called ZhiWo. By using a supervised machine learning approach, sensed data collected by mobile devices are automatically classified into different types of daily human activities and these activities are interpreted as life activity retrieval units for personal archives.

ShareDay: A Novel Lifelog Management System for Group Sharing

Lifelogging is the automatic capture of daily activities using environmental and wearable sensors such as MobilePhone/SenseCam. The potential to capture such a large data collection presents many challenges, including data analysis, visualisation and motivating users of different ages and technology experience to lifelog. In this paper, we present a new generation of lifelog system to support reminiscence through incorporating event segmentation and group sharing.

MemLog, an Enhanced Lifelog Annotation and Search Tool

As of very recently, we have observed a convergence of technologies that have led to the emergence of lifelogging as a potentially pervasive technology with many real-world use cases. While it is becoming easier to gather massive lifelog data archives with wearable cameras and sensors, there are still challenges in developing effective retrieval systems. One such challenge is in gathering annotations to support user access or machine learning tasks in an effective and efficient manner. In this work, we demonstrate a web-based annotation system for sensory and visual lifelog data and show it in operation on a large archive of nearly 1 million lifelog images and 27 semantic concepts in 4 categories.

Helping the helpers: How video retrieval can assist special interest groups

Given the increasing broadcasting data and the ever decreasing spare time that we can spend on consuming this data, systems are required that assist us in identifying important content. Following a use case of a fictional social worker, we introduce a video retrieval system that is designed to assist special interest groups in their information gathering task.

MemoryMesh: lifelogs as densely linked hypermedia

Lifelogs are huge archives of multimedia data and consequently, they need to incorporate organization methodologies to fully exploit their potential. Early work in organizing lifelogs based on either video-style playback or event segmentation with browsing or basic search. In this work we propose that lifelogs can be represented as a densely linked hypermedia archive, called a MemoryMesh. We introduce how this can be constructed and the potential to improve retrieval performance.