EMIR: A novel emotion-based music retrieval system

Music is inherently expressive of emotion meaning and affects the mood of people. In this paper, we present a novel EMIR (Emotional Music Information Retrieval) System that uses latent emotion elements both in music and non-descriptive queries (NDQs) to detect implicit emotional association between users and music to enhance Music Information Retrieval (MIR). We try to understand the latent emotional intent of queries via machine learning for emotion classification and compare the performance of emotion detection approaches on different feature sets. For this purpose, we extract music emotion features from lyrics and social tags crawled from the Internet, label some for training and model them in high-dimensional emotion space and recognize latent emotion of users by query emotion analysis. The similarity between queries and music is computed by verified BM25 model

A survey on life logging data capturing

With the recent availability of inexpensive wearable sensing technologies, the emergence and of both off-line and on-line digital-storage capacity and an acceptance of personal data gathering and online social sharing (timeline), life logging has become a mainstream research topic and is being embraced by early adaptors. For example, currently we have the ability to gather and store large volumes of personal data (location, photos, motion, orientation, etc.) in a very cheap manner, using an inexpensive smartphone. However, with many available lifelogging tools, the question of which ones to use has not been seriously addressed in literature. In this work, we report on a survey of various approaches to capturing lifelog data, which includes the SenseCam/Vicon Revue, wearable smartphones, wearable video cameras, location loggers using GPS, bluetooth device loggers, human body biological state monitors (temperature/heart rate etc.) and so on. We compare these devices and analyze the advantages and disadvantages of different capture methods, including the consistency and integrity of capture, the ‘life coverage’ of the captured data, as well as people’s attitude and feeling to these data capture devices, which we do through user studies and surveys. To complete this work, we provide our opinion of the most suitable model of data capture for personal life logging in a variety of domains of use

Lifelog access modelling using MemoryMesh

As of very recently, we have observed a convergence of technologies that have led to the emergence of lifelogging as a technology for personal data application. Lifelogging will become ubiquitous in the near future, not just for memory enhancement and health management, but also in various other domains. While there are many devices available for gathering massive lifelogging data, there are still challenges to modelling large volume of multi-modal lifelog data. In the thesis, we explore and address the problem of how to model lifelog in order to make personal lifelogs more accessible to users from the perspective of collection, organization and visualization. In order to subdivide our research targets, we designed and followed the following steps to solve the problem: 1. Lifelog activity recognition. We use multiple sensor data to analyse various daily life activities. Data ranges from accelerometer data collected by mobile phones to images captured by wearable cameras. We propose a semantic, density-based algorithm to cope with concept selection issues for lifelogging sensory data. 2. Visual discovery of lifelog images. Most of the lifelog information we takeeveryday is in a form of images, so images contain significant information about our lives. Here we conduct some experiments on visual content analysis of lifelog images, which includes both image contents and image meta data. 3. Linkage analysis of lifelogs. By exploring linkage analysis of lifelog data, we can connect all lifelog images using linkage models into a concept called the MemoryMesh. The thesis includes experimental evaluations using real-life data collected from multiple users and shows the performance of our algorithms in detecting semantics of daily-life concepts and their effectiveness in activity recognition and lifelog retrieval

ShareDay:A memory enhancing lifelogging system based on group sharing

Lifelogging is the automatic capture of daily activities using environmental and wearable sensors such as MobilePhone/SenseCam. Lifelogging produces enormous data collections that present many organization and retrieval challenges, including semantic analysis, visualization and motivating users of different ages and technology experience to lifelog. In this paper, we present a new generation of lifelogging system to support reminiscence through incorporating event segmentation and group sharing

ZhiWo: Activity tagging and recognizing system from personal lifelogs

With the increasing use of mobile devices as personal record- ing, communication and sensing tools, extracting the seman- tics of life activities through sensed data (photos, accelerom- eter, GPS etc.) is gaining widespread public awareness. A person who engages in long-term personal sensing is engag- ing 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 subse- quently to make accessible by search or browsing tools. In this paper, we present an intuitive lifelog activity record- ing and management system called ZhiWo. By using a su- pervised machine learning approach, sensed data collected by mobile devices are automatically classified into different types of daily human activities and these activities are inter- preted as life activity retrieval units for personal archives

A realtime lifelogging solution for iOS devices

Prior lifelogging work was done using specially developed devices such as the Microsoft Sensecam / Vicon Revue or using specially developed Android Phone apps, such as SenseSeer from DCU, FUNF from MIT or Deja-view from Univ. Southampton. In this work, we have developed a first prototype lifelogging tool for use with Apple iOS enabled devices. This tool gathers data using onboard sensors in a non-intrusive manner and sends the sampled life-activities to a server for storage and interaction using a WWW interface

From lifelog to diary: a timeline view for memory reminiscence

As digital recording sensors and lifelogging devices become more prevalent, the suitability of lifelogging tools to act as a reminiscence supporting tool has become an important research challenge. This paper aims to describe a rst- generation memory reminiscence tool that utilises lifelog- ging sensors to record a digital diary of user activities and presents it as a narrative description of user activities. The automatically recognised daily activities are shown chronologically in the timeline view

Lifelogging in the home: evaluating a family SenseCam browser

Automatically capturing images through wearable lifelog devices can allow us to create rich collections depicting our experiences. These collections can be used to support story-telling and reminiscence, either of personal experiences or shared experiences. We propose that a family lifelog browser situated in the home would encourage family sharing. We developed a prototype of the system which we evaluated for use and usability in a home environment with a family consisting of older and younger adult

ShareDay: A new lifelogging brower system for group sharing

SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor data(temperature, accelerometer, magnitude, infrared ray etc.) mainly for the use of reminiscence. But big amount of SenseCam data analysis and constructing for better application in human daily life is a big challenge. In this paper, we rep- resent a new generation of personal life log application system for group sharing based on user study. Dierent from similar lifelogging browsers, our system incorporate event segmentation and group sharing