A Life-Log Search Model Based on Bayesian Network

Ushiama, Taketoshi; Watanabe, Toshio

doi:10.1109/mmse.2004.11

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The increasing amount of life-logged data and the need to integrate it has been one of the main issues of IoT in the last decade. Authors in [28] analyzed in 2004 the usage of lifelogging activities for different applications and media such as sending and receiving emails, TV programs, GPS records, visited web pages and photographs, focusing on the problem of combining life-log activities from different media types. A few years ago, someone could have only a couple of networkconnected devices compared to today and the near future when each person will have access to and control quite more networkconnected devices.…”

Section: Life-loggingmentioning

confidence: 99%

A lightweight framework for secure life-logging in smart environments

Petroulakis

Τράγος

Fragkiadakis

et al. 2013

Information Security Technical Report

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractAs the world becomes an interconnected network where objects and humans interact with each other, new challenges and threats appear in the ecosystem. In this interconnected world, smart objects have an important role in giving users the chance for life-logging in smart environments. However, smart devices have several limitations with regards to memory, resources and computation power, hindering the opportunity to apply well-established security algorithms and techniques for secure life-logging on the Internet of Things (IoT) domain. The need for secure and trustworthy life-logging in smart environments is vital, thus, a lightweight approach has to be considered to overcome the constraints of smart objects. The purpose of this paper is to present in details the current topics of life-logging in smart environments, while describing interconnection issues, security threats and suggesting a lightweight framework for ensuring security, privacy and trustworthy life-logging. In order to investigate the efficiency of the lightweight framework and the impact of the security attacks on energy consumption, an experimental test-bed was developed including two interconnected users and one smart attacker, who attempts to intercept transmitted messages or interfere with the communication link. Several mitigation factors, such as power control, channel assignment and AES-128 encryption were applied for secure life-logging. Finally, research into the degradation of the consumed energy regarding the described intrusions is presented.

show abstract

Section: Life-loggingmentioning

confidence: 99%

A lightweight framework for secure life-logging in smart environments

Petroulakis

Τράγος

Fragkiadakis

et al. 2013

Information Security Technical Report

View full text Add to dashboard Cite

show abstract

“…One of characteristic features of life log is that they reflect the same activity with several different aspects such as photographs, GPS records and so on [19]. It means that activities or emotions are inferred from life log which are fragments of life.…”

Section: Landmark Detection Using Bayesian Networkmentioning

confidence: 99%

Exploiting mobile contexts for Petri-net to generate a story in cartoons

Lee

Cho

2009

Appl Intell

View full text Add to dashboard Cite

Recently, various personal information in daily life is stored in mobile devices with sensors. This information reflects heterogeneous aspects of personal life history. People have a tendency to record precious memories from the information in their life. However, it is difficult to extract and summarize the memories from the information. There are many useful traditional ways, such as photograph, video and diary, to record important memories. Especially, writing a diary is beloved as an effective method for a long time because of its effectiveness, remembrance, and empathy of storytelling. This paper proposes a Petri-net based method that organizes mobile contexts to an understandable and interesting story in cartoons. Petri-net based storytelling approach reduces the uncertainty in mobile environment and increases the diversity and causality of a story. A generated story from mobile contexts is compared with personal life history for confirming the usefulness. Also, it is compared with the other method in previous work.

show abstract

“…, logs) such as a human's daily activities performed in indoor environments detected via HAR. Usually various sensors, such as motion sensors, video sensors, or RFID, are utilized in life-logging HAR [ 14 – 16 ]. The life- logging HAR system provides continuous monitoring and recording of resident's daily activities which is used for future reference to improve the quality of life by assigning life habits and patterns to users.…”

Section: Introductionmentioning

confidence: 99%

A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

Jalal

Kamal

Kim

2014

Sensors

211

101

View full text Add to dashboard Cite

Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

show abstract

A Life-Log Search Model Based on Bayesian Network

Cited by 6 publications

References 10 publications

A lightweight framework for secure life-logging in smart environments

A lightweight framework for secure life-logging in smart environments

Exploiting mobile contexts for Petri-net to generate a story in cartoons

A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

Contact Info

Product

Resources

About