Dem@Care Datasets

Dem@Care is providing the following datasets, which are collected during lab and home experiments. The data collection took place in the Greek Alzheimer’s Association for Dementia and Related Disorders in Thessaloniki, Greece and in participants' homes. The datasets include video and audio recordings as well as data from physiological sensors. Moreover, they include data from sleep, motion and plug sensors.
These datasets are available to the research community under specific terms of use.


Dem@Care Datasets

#

Dataset Name

Description

1

Ds1

Video files from static, color-depth cameras (Asus RGB-D sensor, Kinect)

2

Ds2

Video files from wearable camera (GoPro)

3

Ds3

Audio files from microphone

4

Ds4

Data from physiological sensors

5 Ds5 Audio files from microphone from Dem@Care short @Lab protocol
6 Ds6 Video files from static, color-depth cameras (Asus RGB-D sensor, Kinect) from Dem@Care short @Lab protocol
7 Ds7 Audio files from microphone from Dem@Care long @Lab protocol
8 Ds8 Video files from static, color-depth cameras (Asus RGB-D sensor, Kinect) from Dem@Care long @Lab protocol
9 Ds9 A) data from motion sensor on objects B) data from plug sensor C) data from physiological sensor (DTI-2) from Dem@Care long @Lab protocol 
10 Ds10 A) data from motion sensor on objects B) data from plug sensor C) data from physiological sensor (DTI-2) from Dem@Care short @Lab protocol
11 Ds11 A) data from motion sensor on objects B) data from plug sensor C) data from physiological sensor (UP24) D) data from sleep sensor (AURA)  from Dem@Care 1st home pilot 
12 Ds12 A) data from motion sensor on objects B) data from plug sensor C) data from physiological sensor (UP24) D) data from sleep sensor (AURA)  from Dem@Care 2nd home pilot




In order to provide you part of the Dem@Care datasets you have to follow the following steps: 

  1. Read the datasets Terms of Use below which oblige you, among other things, to:
       not share this data with anyone else

       not attempt to identify any of the individuals

  1. Choose which datasets you are going to need in your research.
  2. Prepare a title and a short description (abstract) of your project.
  3. Send us an email (based on the following template) using your institution’ s email. In order to grant you access to the data we need it to contain the template statements.


Before you apply:
  1. Be as specific as possible about why you want each data table. 
  2. In the case of undergraduate and postgraduate students, you should ask your supervisor to complete the form below, and mention you as a supervisee. We cannot collaborate with researchers who do not have a full-time academic contract.
  3. If you intend to ask for an alternative collaboration agreement from the default one, explain why you think this is appropriate.
  4. The datasets Terms of Use are serious. Please read them all. If you do not follow them we will require you to delete all Dem@Care data, and may ask journals to retract any publications from the period when you did not follow them. In extreme cases we will contact university departments where collaborators work.


Email template:

I would like to become a registered collaborator of the Dem@Care project to pursue the following research: 

(INCLUDE THE TITLE AND A SHORT ABSTRACT OF YOUR RESEARCH). 


I require access to the following datasets:

(LIST THE TABLES YOU INTEND TO USE FROM THE DOWNLOAD SECTION).


I am planning to use the following variables: 

(LIST THE VARIABLES YOU INTEND TO USE AND TELL US HOW DO YOU PLAN DO ANALYSE THEM).

I agree with the Dem@Care datasets Terms of Use, and will take responsibility for the use of the data by any students in my research group.

I also agree that Dem@Care can include my name, my position, my institution, and the title of my research topic on its public Web page and other documents. If I cease work on Dem@Care data, then this collaboration will be listed with an ending date. 

I agree that there will be a reference to: Karakostas A., Briassouli A., Avgerinakis K., Kompatsiaris I., Tsolaki M. "The Dem@Care Experiments and Datasets: a Technical Report”, arXiv:1701.01142 [cs.CV], https://arxiv.org/abs/1701.01142

Your Name / Position / Institution 



Datasets Terms of Use

Confidentiality

You cannot send the datasets to any other party (even if they have access to it themselves), nor disclose to anyone else the information contained within it as well as its structure. It is allowed to share data with students in your research group who you agree to supervise and take full responsibility that their use of the data also meets these Terms of Use.


Anonymisation

You should not link individual data (records, image or video files etc) with any other information about an individual that you may have. This implies that you cannot attempt to contact any individuals either.

Moreover, if you are going to use images or video captures from Dem@Care datasets in any of your papers or presentations, in any case you should not show patients’ faces.  


Non-Commerical License

We grant you a non-commercial license to use the data. You can only use it for academic research that does not earn revenue, and your research also cannot be in collaboration with any commercial entities.


Scope of Research

If the scope of your research changes, then you should contact us again for us to agree the change. If you stop researching using Dem@Care data you should make us aware of this fact.


Publication Reference

On any publications that arise from Dem@Care data you should reference to: Karakostas A., Briassouli A., Avgerinakis K., Kompatsiaris I., Tsolaki M. "The Dem@Care Experiments and Datasets: a Technical Report”, arXiv:1701.01142 [cs.CV], https://arxiv.org/abs/1701.01142

Ethical Clearance

Although, all of Dem@Care tests require that users read and agree to information regarding using their data for research, making the data available to other researchers, and their ability to withdraw from the research at any time, it is your responsibility to ascertain whether it is ethical to use the data that Dem@Care has collected for academic research.


Acknowledgement of Dem@Care

All uses of Dem@Care data, including but not limited to research papers, at conferences, on websites, and in press releases, should include prominent acknowledgement that Dem@Care is the data source. However, it should not imply that Dem@Care endorses the research. It should be clear that Dem@Care is an external data supplier.


Access to data

Dem@Care cannot guarantee continued access to the data, nor that our service will not be interrupted from time to time. The license to use and store our data is recoverable, which means that Dem@Care may ask you to cease use of it and to delete it from any storage you have at our sole discretion.


Warranties

Dem@Care disclaims any warranties, for example but not limited to, the data's suitability for research or publication


Downloads


#

Institution

Contact

Research objectives

Datasets downloaded

1

Kingston University

Prof. Vasileios Argyriou

This project is focused on designing and implementing mechanisms to monitor the patients’ behaviour in a non-intrusive way during their everyday life considering all the related ethical issues will be introduced.

The depth information and the position of the joints will be used. Features are going to be defined and this information will be used to train a machine learning algorithm that will detect different human behaviours. 

DS1

2

University of Malaya



Prof Loo Chu Kiong

Our project focuses on evaluating such features to assess its performance in ADL recognition for both first and third person perspective. Another novel approach is to study the fusion of both egocentric (first person) and third person viewpoint for ADL recognition. The Dem@Care dataset will be used to evaluate the performance of the proposed method. The dataset also allow us to investigate algorithms that discriminate between activity performed by a healthy person and person with mild dementia.

DS1, DS2

3

MIT, Lincoln Laboratory



Bea Yu

Associate Technical Staff

We have recently used speech and video features to automatically predict depression [1,2], winning both AVEC 2013 and 2014 Depression Sub-Challenges.  Given the potential health care implications of rising numbers of people suffering from dementia due to a large aging population in the US and around the world, we have expanded our focus to automated dementia prediction and monitoring using speech and features from other modalities [3].  The Dem@Care Datasets appear to be a very useful resource for this purpose and we were very excited to see that they are available to academic researchers.

DS3

4

Department of Computer Science

FAST National University, Karachi

Furqan M Khan

Assistant Professor

Supervised recognition of daily living activities



DS1

5

Università degli Studi di Milano

Prof. Claudio Bettini

A non-intrusive sensor-based infrastructure acquires low-level data about the interaction of the individual with the home environment including objects, appliances and furniture. Our goal is to detect abnormal behaviors at a fine-grained level, thus providing an important tool to support the medical diagnosis.

DS9, DS10, DS11, DS12

6

Faculty of Electronics, Telecommunications and Information Technology University Politehnica of Bucharest

Assoc. Prof. Dr. Eng. Bogdan IONESCU

The research proposes to implement a solution in order to monitor and track down the needs of old and sick people, based on advanced video processing. 

The project will test different scenarios of and using skin detection and other techniques will try to find the best method in distinguish a healthy person from a sick one . All these methods and techniques will be implemented in MatLab environment.

DS1, DS3, DS6

7

Biomedical group at Mondragon University (Spain)

Dr. Asier Aztiria

The general objective is to develop a system that learns how patients’ behaviours and biomedical signals are affected when a patient suffers a specific disorder. Such knowledge will be used to detect symptoms in a transparent way and to help in the early diagnosis of such disorders.

DS1, DS3, DS4, DS9,
DS10, DS11, DS12


8

Faculty of Engineering, Computing and Science

Swinburne University of Technology Sarawak Campus



Dr. Lau Bee Theng

Associate Professor

The aim of the project is to develop an affordable robot for children, the elderly and disabled patients that helps to autonomously monitor for possible injuries while providing an avatar for Telepresence by the carer, in addition to being a highly extensible assistive robot development platform for every home

DS1, DS2, DS6, DS8

9

University of Bristol



Yangdi Xu



A person’s routine incorporates the frequent and regular behaviour patterns over a time scale, e.g. daily routine. In this work we present a method for unsupervised discovery of a single person’s daily routine within an indoor environment using a static depth sensor. Routine is modelled using top down and bottom up hierarchies, formed from location and silhouette spatio-temporal information. We employ and evaluate stay point estimation and time envelopes for better routine modelling. The method is tested for three individuals modelling their natural activity in an office kitchen. Results demonstrate the ability to automatically discover unlabelled routine patterns related to daily activities as well as discard infrequent events.

DS8

10

Department of Computer Science & Engineering,
SriVenkateswara College of Engineering Sriperumbudur


K.S.Gayathri 
Associate Professor



The project focuses on development of machine learning based algorithms for the automated recognition of daily activities of older people. The datasets could help substantially with the development and evaluation of algorithms for understanding the behavior of the people with and without dementia.

DS1, DS2, DS6, DS8

11 German Research Center for Artificial Intelligence Nicklas Linz Project: ELEMENT - Early Detection of Cognitive Disorders such as Dementia on the Basis of Speech Analysis
Abstract: The project is focused on developing a light screening application to access a persons cognitive health through speech analysis.
The data form Dem@Care will be used to verify the accuracy of machine learning models trained on other resources.
DS5, DS7
12 Institut Supérieur d'Informatique et Multimédia, (www.isimsf.rnu.tn)
Université de Sfax
Ing. Yassine Ben Ayed
Associate Professo
Acoustic variables from the audio files,and linguistic variables from the associated transcripts, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. DS5, DS7
13 Monta Vista High School in Cupertino, California, USA Renee Fallon The Voice of Alzheimer’s: Wearable Technology Coupled with Machine Learning to Track Alzheimer’s Disease Progression

Alzheimer’s disease is the most prevalent form of dementia throughout the world, and despite significant advancements in treatment, tracking disease progression remains a challenge. To address this, his project seeks to use wearable technology in conjunction with machine learning, voice recognition/detection, and signal processing algorithms to create a comprehensive, quantitative method of analyzing the unique vocal aspects that vary from patient to patient in order to effectively track AD progression.
DS3, DS5, DS7
14 University Utara Malaysia Prof Abdull Sukor Shaari This project focuses on investigating elderly people's behaviour based on their daily routine and identifying an abnormal situation. The aim is to develop an efficient reasoning system that can monitor elderly individual's daily lives and detect abnormalities as well as identify the most probable reason and solution. The system is supported by a semantic knowledge base that can represent knowledge of the world in order to support the reasoning system. DS4, DS5, DS10, DS11, DS12
15 De Montfort University Ismini Psychoula The project focuses on designing and developing mechanisms that help residents of ambient assisted living environments to maintain their privacy and security. The datasets will be used to evaluate the performance of algorithms that can learn to automatically remove any private information that the residents don't want other people to have, especially on video data. DS1, DS2, DS6, DS8
16 Neural Information Processing Institute, University of Ulm, Germany Yan Zhang It is important to propose an automatic and unobstructive method to recognize abnormal behaviors of elderly people. In our work, we proposed a novel unsupervised online learning algorithm to group the body skeletons overtime and learn the long-term dynamics gradually, so that static abnormal behaviors and abnormal movements can be detected. This algorithm first determine the number of clusters using an novel graph spectrum algorithm; then the codebook will update in an online manner as perceiving data. In addition, the transition probability between keywords in codebook is updated as well. The skeleton extraction and analysis algorithm are implemented in GPU. As tested on other dataset, it can run in real-time.  DS1, DS2, DS4, DS6, DS8
17 Dept of Computer Science and Engg.
Indian Institute of Technology Patna
Jimson Mathew
Associate Professor and Head
Our project aim at robust activity recognition using data from multiple sensors. We aim to study the effect of fusing multi-sensor data in accuracy of activity recognition system. RGB-D data will help our model tobe keen attention on the person in sight from the background and extractmore details than RGB data. Motion, physiological and plug sensor data
will be fused with RGB-D data for improving recognizing the activity.
Features extracted from this dataset will be used to trained our machinelearning model. The model will be able to predict abnormal activities and alert as necessary.
DS1,DS4, DS6, DS8, DS9,
DS10, DS11, DS12
18 Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan Dr. Muhammad Awais Azam / Assistant Professor This research work aims to provide an assistant and independent living for human beings based on human behavior modeling and activity interpretation in their living and working environments. The motivation is to formulate a universal framework for continuous monitoring of human behavior, which is based on recognition of activities of daily living, detection of changes in those activities and ultimately the detection of abnormal and/or unforeseen activities. This dataset will be used to detect the activities by monitoring physical, physiological, visual and contextual parameters related to human beings using multiple sensor modalities. Features extracted from this dataset will be used to train different machine learning classifiers, which will be able to detect, recognize and predict unusual behavior of a person. Detection of unforeseen and abnormal human behavior is useful for the safety and healthcare of human beings and necessary aid and/or guidance can be provided to the persons when needed. DS1,DS2,DS4,DS6,DS8,DS9,
DS10,DS11,DS12
19 Semnan university, Iran Dr. Hadi Soltanizadeh /Assistant Professor Early detection of Alzheimer disease in old people using behavioral features such as speech, gait and
daily activity. We are working on new ICT technology to recognize Alzheimer in old people in the early
stage of disease. We have been working on speech, gait and daily activity to detect Alzheimer in old
people and also to classify old people to different groups such as normal, MCI and AD.
DS1, DS3, DS5, DS6, DS7, DS8
20 Department of Computer Engineering, Sejong University Dongkyoo Shin, Ph.D. Intelligent dementia care support system for home care support

It is a daily life detection system for reducing the burden of care person for dementia patients. It is based on the life log data of patients with dementia and detects every life of dementia patients and connects them to caregivers or care hospitals.
DS1, DS4, DS6, DS8, DS9, DS10, DS11, DS12
21 University Paris Est Créteil - UPEC Dr. Ing. Abdelghani CHIBANI We aim to implement multimodal deep learning models to track and monitor elderly people daily activities to prevent the daily risks,. DS2, DS4, DS9, DS10, DS11, DS12
22 National Institute of Technology Raipur Dr. Govind P. Gupta We aim to study the effect of fusing multi-sensor data with video-sensor data to enhance the accuracy of the activity recognition system. Video-Sensor data will help our model to keep keen attention on the personal insight from the background and extract more details than motion-sensor data. Motion, physiological and plug sensor data will be fused with video-sensor data for improving recognizing the activity. DS1, DS2, DS4, DS6
23 Saifer. www.saifer.ai/en/ Dr. Dániel Törtei One of main challenges in ambient assisted living among elderly is a efficient, non-intrusive and stigma-
free remote monitoring. SOS buttons, pendants, bracelets and similar wearable solutions are seen as
instrusive. Our embedded fall detector, which is an embedded camera with a small fire-alarm size
integrated circuit, uses machine learning algorithms to analyze videos and is a completely discreet and
non-intrusive solution.
DS1, DS6, DS8
24 University of sfax Dr. Wael Ouarda This project is focused on designing and implementing mechanisms to monitor the patients’ behaviour in a non-intrusive way during their everyday life considering all the related ethical issues will be introduced. DS1, DS6, DS8
25 Thi Hoang Ngan Le Carnegie Mellon University Predicting the action of a person before it is actually executed has a wide range of applications in numerous reseach areas such as autonomous robots, surveillance and health care. In this reserach we focus on predicting risky activity that may happens to dementia disorders. DS1, DS2, DS6, DS8
26 Guoliang Fan Oklahoma State University, USA CATcare: Cognitive Assistive Technology for Dementia Homecare DS1, DS2, DS8
27 Xingjian WANG School of Automation Science and Electrical Engineering
Beihang University
The Design of Video-based Elderly Abnormal Behavior Detection and Inference System
The objective of our research is to develop an intelligent health care system, which is used to detect and infer abnormal health behavior of solitary seniors, including sudden injury like falling and gradual decline of cognition. We adopt indoor surveillance camera as sensor to detect daily activities of elderly people since visual signal can depict rich contextual information. Deep learning method is used to extract the spatial and temporal feature of continuous video sequence and recognize care recipients' behavior. The inference system detects abnormal pattern based on contextual information and the daily routine of care recipient.
DS1, DS2, DS6, DS8
28 Sorin MOGA IMT Atlantique Sleep stages classification using machine learning algoritms. DS11, DS12
29 Bhuvaneshwari Bhaskaran University of Memphis In this project, we will analyze the behavior of dementia patients using multimodal data –visual (DS1, DS2, DS4, DS6, DS8), audio (DS3, DS5, DS7), and physiological (DS9, DS10, DS11, DS12). This analysis will lead to short-term prediction of abnormal behaviors and long-term prediction of the progression of the disease. DS1, DS2, DS4, DS6, DS8, DS3, DS5, DS7, DS9, DS10, DS11, DS12
30 Azadeh Mansouri Kharazmi University We intended to use these datasets for action recognition purpose in the compressed domain. Actually, the main goal of the project is related to the improvement of the action recognition speed for  Activities of Daily Living using available compressed domain components. DS1, DS2
31 Dr. Erfu Yang Department of Design, Manufacture and Engineering Management
University of Strathclyde
The title of our project is 'Investigation of a Smart and Low-Cost Autonomous System for Early Detection and Monitoring of Mild Cognitive Impairment in the Elderly'. The objectives is for early detecting the MCI symptoms in the elderly, monitoring the progress of the disease and providing a collective care of the MCI patients. MCI may be the early stage of dementia. In the project, we plan to detect the mild cognitive impairment by using the mobile phone to monitor the abnormal facial expression and body movement of the elderly people. DS1, DS6, DS8
32 Prof. Yuefeng Li School of Electrical Engineering and Computer Science Dementia is one of the leading causes of death in Australia and all over the world. The risk of getting dementia increases with age. Accordingly and as the Australian's population ages, the incidence of dementia has increased and, without medical breakthrough, is expected to duplicate in the coming decade. Unfortunately, there is no cure for dementia. However, early detection helps in mitigating side effects of its symptoms and allow for planning for the future. Although research into early detection of has increased in the last decade, there is a lack of low-cost and non-invasive longitudinal diagnosis instruments. Therefore, our research project is focusing on building an intelligent system for early detection of dementia through conversation analysis. DS3, DS5, DS7
33 Seyed Shahrestani School of Computing, Engineering and Mathematics
Western Sydney University
Title: Improving self-dependent Living of Older Adults with the Internet of Things
Our works center around the realization that early detection of the onset of dementia is highly relevant to aging well and improving the quality of life for older adults. IoT based activity monitoring systems can assist with this detection. Smart environments can also significantly improve independence and the overall quality of life of people living with dementia while reducing the cost and burden of care on the society and caregivers.
DS1, DS3, DS4, DS7, DS8, DS9, DS11
34 Durga Sivan Department of CSE,
Karunya Institute of Technology and Sciences,

Karunya Nagar, Coimbatore, India.
This project is focused on designing and implementing mechanisms to monitor elderly people behaviour in a non-intrusive way during their everyday life. The monitored data are used to make intelligent decisions for further recommendations. This information will be used to train a machine-learning algorithm that will detect abnormal health behaviours. DS1, DS3, DS4, DS9, DS10, DS11, DS12
35 Rinkle Rani Computer Sc. & Engg. Department

Thapar Institute of Engineering & Technology,

Patiala (INDIA)
The aim of our research is to perform the fusion of multimodal sensor data to give an integrated view in order to deal with the issues of interoperability and exchange through Semantic Technology. The behavior of dementia patients is to be analyzed in depth to achieve effective activity recognition. The knowledge base and inference system is to be designed based on Semantic Web technology and supervised/unsupervised learning to detect critical and abnormal situations for elderly monitoring and care. We have recently worked in this domain to accommodate the issue of heterogeneity in the inference mechanisms using the Extrasensory dataset.


DS1, DS4, DS5, DS6, DS8, DS9, DS10, DS11, DS12





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