Team name

SenseCam

 

Corresponding author
Na Li

Affiliation
Dublin City University, Ireland

Abstract

Technologies for Ambient Assisted Living (AAL) combine new Information and Communication Technologies (ICT) to improve and increase the quality of the life cycle of the elderly. SenseCamTM is a wearable, automatic camera which can support memory recall when used as a lifelogging device. The main issue is that, while SenseCamTM produces a sizeable collection of images over the time period, the vast quantity of captured data contains mainly routine events, which are of little interest to review. Recent and continuing work in Dublin City University’s SCI-SYM centre to apply several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyze the multiple time series generated by the SenseCam have proved useful in detecting “Significant Events”. We suggest that some wavelet scales (e.g. 8 minutes -16 minutes) have to potential to identity of distinct events or activities.


Team name

AMEVA

   

Corresponding author
Luis Miguel Soria Morillo

Affiliation
Universidad de Sevilla, Spain

Abstract

This article aims to develop a minimally intrusive system of care and monitoring. Furthermore, the goal is to get a cheap, comfortable and, especially, efficient system which controls the physical activity carried out by the user. For this purpose an innovative approach to physical activity recognition is presented, based on the use of discrete variables which employ data from accelerometer sensors. To this end, an innovative discretization and classification technique to make the recognition process in an efficient way and at low energy cost, is presented in this work based on the χ^2 distribution. Entire process is executed on the smartphone, by means of taking the system energy consumption into account, thereby increasing the battery lifetime and minimizing the device recharging frequency.


 

Team name

 

Corresponding author
Myagmarbayar Nergui

Affiliation
Chiba University, Japan

Abstract

Our ultimate goal is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients’ motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs), and meanwhile, relieve therapists from great burden in canonical rehabilitation. In this study, we have developed control algorithms for a mobile robot to track and follow human by 3 different viewpoints: side view, front view and middle angle of human, algorithms for measurements of lower limb joints angle and algorithms for recognizing gait gesture. The accuracy of joint measurement was also investigated. Due to the skeleton point mixing-up and frame flying, the error was very big. However, after applying a colored mark compensation algorithm and missing point correction algorithm, the error could be corrected to a certain extent. This shows the feasibility of joint trajectory measurement through the mobile robots in real time. Recognizing and monitoring human behaviors is very attracting attention at home healthcare system. Human behavior analysis is not based on static process (not depending on only one situation), but needs dynamic time sequences data. Hidden Markov models are especially known in temporal pattern recognition such as speech, handwriting, gesture recognition. We have proposed a method of human walking behavior recognition applying HMM based on lower limb joint angles and body angle without any attached sensors to human body. It is shown that the proposed method brings the high rate of recognition of human walking behavior and is effective in indoor environment using by a human tracking and following mobile robot.


 

 

Team name

  

Corresponding author
Simon Kozina

Hristijan Gjoreski

Affiliation
Jozef Stefan Institute, Department of Intelligent Systems, Slovenia

Abstract

Activity recognition is a prerequisite for numerous AAL tasks. A sensor system capable of automatically recognizing activities would allow many potential applications. In this paper an approach to activity recognition system using wearable inertial sensors is presented. The described activity recognition algorithms and preprocessing techniques are efficient and are able to recognize the user's activity in real-time. Additionally, the sensors use wireless communication, therefore the user can freely perform his/hers everyday activities.


 

Team name

 

 

Corresponding author
Julian Andres Ramos Rojas

Jin-Hyuk Hong

Affiliation
Carnegie Mellon University - Human computer Interaction, USA

Abstract

Ambient assisted living facilities provide assistance and care for the elderly, where it is useful to infer their daily activity for ensuring their safety and successful aging. In this work, we present a system that classifies a set of common daily activities. The system is designed to be comfortable and non-intrusive, and is comprised of commercial, robust and well known devices while maintaining a high recognition accuracy and fast response by exploiting machine learning techniques.


 

 

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