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.

# 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 at (based on the template below) 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:


I require access to the following datasets:


I am planning to use the following variables:


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],

Your Name / Position / Institution

Datasets Terms of Use


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.


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],

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.


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



# 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.

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
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, (
Université de Sfax
Ing. Yassine Ben Ayed
Associate Professor
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,
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. 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 Carnegie Mellon University Thi Hoang Ngan Le 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 Oklahoma State University, USA Guoliang Fan CATcare: Cognitive Assistive Technology for Dementia Homecare DS1, DS2, DS8
27 School of Automation Science and Electrical Engineering
Beihang University
Xingjian WANG 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 IMT Atlantique Sorin MOGA Sleep stages classification using machine learning algoritms. DS11, DS12
29 University of Memphis Bhuvaneshwari Bhaskaran 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 Kharazmi University Azadeh Mansouri 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 Department of Design, Manufacture and Engineering Management
University of Strathclyde
Dr. Erfu Yang 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 School of Electrical Engineering and Computer Science Prof. Yuefeng Li 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 School of Computing, Engineering and Mathematics
Western Sydney University
Seyed Shahrestani 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 Department of CSE,
Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India.
Durga Sivan 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 Computer Sc. & Engg. Department, Thapar Institute of Engineering & Technology, Patiala (INDIA) Rinkle Rani 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
36 Computer Sc. & Engg. Department, Thapar Institute of Engineering & Technology, Patiala (INDIA) Swastik Gupta My team at Thapar Institute Of Engineering and technology are working & research on dementia patients to find solutions for it. For this, we need some existing data to apply algorithms on it to find some patterns in it. If we can get access to your collected dataset, that will help us a lot and many dementia patients out there. DS1, DS4, DS5, DS6, DS8, DS9, DS10, DS11, DS12
37 Computer Science Department, Durham University, United Kingdom Dr Hubert P.H. Shum Interpretable two-stream graph convolutional network for Parkinson’s tremor evaluation.
Our project proposes a two-stream graph convolutional network architecture with the attention mechanism, which could indicate important features and relationships in the deep learning model during Parkinson’s evaluation based on kinetics video data and motion sensor data (e.g. accelerometer sensor data, physiological sensor data).
DS1, DS2, DS4, DS6, DS8, DS9, DS10, DS11, DS12
38 Symbiosis Institute of Technology, India Sharnil Pandya Through this work we explore the usefulness of acoustic biomarkers and demonstrate a mechanism to identify dementia stages effectively. We have investigated a very time- and cost- effective approach to fully automatic dementia detection using speech. Using unsupervised speaker diarization we identified participant speech segments in a large data set. The analysis indicated that the speech rate the number and duration of silent and filled pauses, and some other derived features behave significantly differently for patients than those for control people, and hence these features can be used as acoustic biomarkers to strengthen the diagnosis. DS5, DS7
39 Neuroglee Therapeutics Pte Ltd Alger Remirata We are currently developing algorithms to detect and manage Alzheimer’s disease. We would like to ask access to the data to develop algorithms for detecting and managing Alzheimer’s and other neurological diseases. DS3, DS5, DS7
40 Higher Institute of Computer Sciences and Multimedia, University of Gabes, Tunisia Dr. Olfa Jemai The Alzheimer is a disease that essentially affects memory. It destroys the vital cells of the brain and especially those related to the different forms of memory such as the memory of the work, which explains one of the most well-known series of Alzheimer’s cognitive disorders which is the sudden difficulty to perform the activities of daily life. We aim in this project to design a cognitive orthosis in order to help people who have cognitive deficits like Alzheimer to move and to be more autonomous in their activities of daily life. DS1, DS2, DS6, DS8
41 Oak Ridge National Laboratory, United States Zach Langford, Hector Santos-Villalobos, Emma Reid Time Series Classification Using Human Activity Datasets. We are looking into open source datasets to test our current machine learning algorithm for identifying events in time series signals. We are currently using human activity datasets, such as WESAD (Wearable Stress and Affect Detection) and Human Activity Recognition Using Smartphones Data Set, but also would like to add Dem@Care datasets based on physiological sensors. DS4, DS9, DS10, DS11, DS12
42 Computational
Biomedical Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), Greece
Dr. Anastasia Pentari We are a team of FORTH-Hellas and specifically the computational biomedical laboratory (CBML). We are interested in downloading the datasets Ds3 and Ds4, as recently we started studying the changes of speech, mainly, under a variety of diseases. As a consequence, these datasets would be interesting and useful. DS3, DS4
43 University of Paris-Saclay, France Imen Trabelsi, Researcher Daily activities recognition DS4, DS9, DS10
44 Faculty of Information Technology University of Benghazi, Libya Dr. Ahmed Lawgali, Associate professor Speech Recognition for Early Detecting Alzheimer diseases by using different Machine Learning Algorithms DS3, DS5, DS7
45 University of Surrey, UK Dr. Samaneh Kouchaki, Lecturer Anomaly detection in healthcare sensor data collected via remote patient monitoring DS3, DS4, DS5, DS7, DS9, DS10, DS11, DS12
46 Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan Kun-chan Lan, Professor Our project is to develop a deep-learning based multimodal model based on multiple types of data (sensor, audio, video) in this database for the applications of dementia detection and staging DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
47 Department of Computer Science, Bowie State University Soo-Yeon Ji, Associate Professor Designing a dementia monitoring system DS3, DS4, DS5, DS7, DS9, DS10, DS11, DS12
48 Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, India Dr. V. Sowmya, Assistant Professor Neurological Disease Classification by Deep Learning Techniques DS3, DS4, DS5, DS7, DS9
49 Faculty of Engineering & Computing, De Montfort University, Dubai Dr. Farhan S. Ujager, Senior Lecturer of Cyber Security Early detection of dementia by using AI/ Machine learning-based techniques DS4, DS10, DS11, DS12
50 Dpt. Computer Technology, University of Alicante, Spain Professor Dr. Jose Garcia-Rodriguez Monitoring and Detection of human behaviors for personalized assistance and early disease detection DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
51 Department of Computing, Imperial College London Dr. Soteris Demetriou, Assistant Professor Analysis of data augmentation techniques for Dementia detection DS5, DS7
52 Department of Computing, Bournemouth University, United Kingdom Dr. Jane Henriksen-Bulmer, Senior Lecturer in Computer Science AI model for simple human actions recognition and regular activity monitoring in a smart home based setup DS1, DS2, DS4, DS6, DS8, DS9, DS10
53 Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, India Dr. Veeky Baths, Associate Professor, Cognitive Neuroscience Lab Understanding the onset of self-injurious behavior among people with Dementia and uncovering the role of depression/stress in it DS1, DS2, DS4, DS11, DS12
54 Computer Science Department, Technical University of Cluj-Napoca, Romania Dr. Cristina Bianca Pop, Senior Lecturer Detect cognitive decline symptoms from daily living activities DS9, DS10, DS11, DS12
55 School of Computing and Artificial Intelligence, Southwest Jiaotong University, China Dr. Xun Gong, Professor Train a machine learning algorithm that can identify abnormal patient behavior in an ICU sickroom DS1, DS2, DS6, DS8
56 Network Objects Control and Communication Systems Lab, University of Sousse, Tunisia Dr. Fatma Ezzahra Sayadi, HDR at NOCCS-Lab Human Activity Recognition for elderly people with dementia using Deep Learning DS1, DS2, DS6, DS8
57 Department of Occupational Therapy, University of Manitoba, Canada Dr. Amine Choukou, Associate Professor Monitoring and Recognition System for Behavior & Activities of Daily Living Among Patients with Dementia Using Smart Algorithms and Assistive Technology DS1, DS2, DS6, DS8
58 Tecnologico de Monterrey, School of Engineering and Sciences, Department of Mechatronics, Monterrey, Mexico PhD. Eng.Sc. Sergio A. Navarro Tuch, Professor/Researcher System for the assistance and monitoring of Alzheimer’s patients based on Humanitude DS3, DS5, DS7, DS10, DS11, DS12
59 Université Polytechnique Hauts-de-France, France Dorsaf Zekri, Assistant Professor in Computer Science Applying learning method to extract knowledge from elderly ADL to observe elderly’s behavior changes over time DS9, DS10, DS11, DS12
60 University of Jyväskylä, Faculty of Information Technology, Finland Tommi Kärkkäinen, Professor Distance-based methods for detecting dementia from speech DS3, DS5, DS7
61 Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, China Yang Guanci, Professor Multimodal data-driven assessment of early Alzheimer’s disease at home DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
62 College of Computer Science & Software Engineering, Sichuan University, China Tao Lin, Professor Identification of mild cognitive impairment based on multimodal data DS1, DS2, DS3, DS4, DS5, DS6, DS9, DS10, DS11, DS12
63 Computer Engineering Department, Electronics Group, Electrical Engineering Department, Yazd University, Yazd, Iran Dr. Sayed Alireza Sadrossadat, Assistant Professor Alzheimer’s disease recognition using 5 senses data DS4, DS9, DS10, DS11, DS12
64 Department of Linguistics, University of British Columbia, Canada Bryan Gick, Professor The effects of dementia on F0-related head movement DS1, DS2, DS3, DS5, DS6, DS7, DS8
65 Dalian Maritime University, China Yilin Pan, Lecturer Home-based dementia detection and disease progression tracking with multi-modal dataset DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10
66 Department of Computer Science, Lamar University, Beaumont, United States Jane Liu, Professor Early-Stage Dementia Detection using Natural Language Processing DS3, DS5, DS7
67 Computer Science & Engineering, National Institute of Technology Hamirpur, India Dr. Mohit Kumar, Assistant Professor Deep Learning-Based Multimodal Model for Personalized Assistance for Dementia Patients DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, SD11, DS12
68 Belarusian State University of Informatics and Radioelectronics Dr. Uladzimir Vishniakou, Professor Voice analysis for Alzheimer’s patients to try to determine whether any person over 60 years old has Alzheimer’s disease by voice recordings by training the model to improve its accuracy in identifying early dissimilar features in the speech of Alzheimer’s patients DS3, DS5, DS7
69 Institute of Information Management, ETH Zurich, Switzerland Dr. Filipe Barata Investigating health status changes in patients with Alzheimer’s and Dementia using voice DS3, DS5, DS7
70 Department of Electrical and Computer Engineering, University of Washington, USA Dr. Payman Arabshahi, Associate Professor Develop a machine learning based program that analyzes sleep and gait patterns, as well as data from in-home and wearable devices (e.g., heart rate, breathing rate, body temperature, and audio) over time, to generate a reading on neurodegenerative diseases, including Alzheimer’s Disease DS3, DS4, DS5, DS7, DS9, DS10, DS11, DS12
71 Computer Science Department, Technical University of Cluj-Napoca, Romania Dr. Cristina Bianca Pop, Senior Lecturer Detect cognitive decline symptoms from daily living activities DS9, DS10, DS11, DS12
72 IE University in Madrid, Spain Manoel Gadi, Professor Early Detection of Chronic Illness Using Voice Detection DS3, DS5, DS7
73 Technical University of Denmark Andrea Burattin, Associate Professor Online conformance checking to support human behavior study DS4, DS9, DS10, DS11, DS12
74 University of Sunderland, UK Dr Basel Barakat, Senior Lecturer Novel framework combining multiple models to detect Alzheimer’s Dementia using Deep Learning methodology DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
75 National Engineering School of
Sousse, Tunisia
Fatma Zahra Sayadi, Senior Lecturer Human activity recognition for Alzheimer disease prediction DS6, DS8
76 School of Informatics, The University of Edinburgh, Scotland Dr. Longfei Chen, Research Associate Monitoring Behaviour Patterns of Older Adults with Dementia Using RGB-D Camera DS1, DS2, DS4, DS6, DS8
77 Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Australia Amit Lampit, Associate Professor Music Attuned Technology – Care via eHealth (MATCH) project: a collaboration among music therapists, aged care clinicians, computer scientists and engineers at the University of Melbourne that aims to improve the lives of people living with dementia by leveraging the therapeutic properties of music DS1, DS2, DS3, DS4
78 University of Jendouba, Tunisia Anouar Ben Khalifa, Professor Early detection of Alzheimer’s disease using artificial vision DS1, DS2, DS3, DS4, DS6, DS8
79 Department of Computer Science, Faculty of Science and Technology, Middlesex University, London, United Kingdom Juan Carlos Augusto, Professor Activity Profiling and Interactive Data Visualization for Dementia Patient Monitoring and Care DS4, DS9, DS10, DS11, DS12
80 Department of Computer Science, University of Sheffield, United Kingdom Heidi Christensen, Professor An exploration of the aspects of speech and language associated with dementia for the purposes of developing understanding of the linguistic impacts of dementia informing and creating speech technology approaches to neurological condition detection DS3, DS5
81 Sichuan University, China Wenchao Du, Researcher Multilingual Alzheimer’s Dementia Recognition through Spontaneous Speech DS3, DS5, DS7
82 Budapest University of Technology and Economics, Hungary Dr. Sztahó Dávid, Research fellow Multilingual voice disorder detection using continuous speech with machine learning and deep learning algorithms DS3
83 University of Louisville, USA Dr. Faisal Aqlan, Associate Professor and Program Director Using Artificial Intelligence to Optimize Communication in Dementia Care DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
84 University of California, Irvine Dr. Adeline Nyamathi, Professor Establishing the Foundations of Emotional Intelligence of Care Companion Robots to Mitigate Agitation among High Risk Dementia Patients via Emphatic Patient-Robot Interactions DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8
85 Birla Institute of Technology and Science, Pilani, India Vikas Kumawat, Assistant Professor Visual Cues Determination for Alzheimer’s DS1, DS6, DS8
86 Beijing Jiaotong University, China Jingjing Yu, Associate Professor Development of a multimodal algorithm for the early detection of dementia and aging based on daily behavior patterns DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
87 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, India Dr. J. Nirmaladevi, Professor Smart Assistance System for Dementia People DS1, DS2, DS4, DS6, DS8, DS9, DS10, DS11, DS12
88 Department of Computer Science (PPGCC), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Brazil Dr. Dalvan Griebler, Professor Training and evaluation of classification models utilizing audio and textual features representing manifestations of Alzheimer’s Disease in several languages. DS3, DS5, DS7
89 Northeastern University, USA Dakuo Wang, Associate Professor Generative Agents for Older Adult’s Daily Routine Simulation and Multi-Modality Physical Data Generation DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
90 Hanyang University, South Korea Kijung Yoon, Assistant Professor Gait-based dementia detection DS1, DS2, DS6, DS8
91 Department of Econometrics and Data Science, Vrije Universiteit Amsterdam Noah Stegehuis, Research Associate Unveiling Alzheimer’s disease: Machine Learning meets Ordinary Least Squares for efficient diagnosis DS3, DS5, DS7
92 Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria Jesús B. Alonso Hernández, Professor, Head of Institute Development of a comprehensive analytical model that integrates audio, video, and physiological sensor data to identify early biomarkers of Alzheimer’s disease DS1, DS2, DS3, DS4, DS5, DS6, DS7, DS8, DS9, DS10, DS11, DS12
93 Departamento de Ingeniería Eléctrica y Electrónica, Universidad Católica San Pablo, Arequipa, PERÚ Jimmy Diestin Ludeña Choez, Profesor Auxiliar Tiempo Completo, Director del Centro de Investigación e Innovación en Electrónica y Telecomunicaciones (CIIET) Attention mechanisms and Curriculum learning for Alzheimer detection DS3, DS4