|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:
not share this data with anyone else
not attempt to identify any of the individuals
- Choose which datasets you are going to need in your research.
- Prepare a title and a short description (abstract) of your project.
- Send us an email at firstname.lastname@example.org (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:
- Be as specific as possible about why you want each data table.
- 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.
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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).
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(LIST THE TABLES YOU INTEND TO USE FROM THE DOWNLOAD SECTION).
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(LIST THE VARIABLES YOU INTEND TO USE AND TELL US HOW DO YOU PLAN DO ANALYSE THEM).
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Your Name / Position / Institution
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.
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.
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
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 . 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
|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
|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
|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.
|12||Institut Supérieur d’Informatique et Multimédia, (www.isimsf.rnu.tn)
Université de Sfax
|Ing. Yassine Ben Ayed
|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
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. 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
|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
|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|
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|