Dem@Care Project – GAADRD Dataset
Dem@Care is providing a public dataset, which is collected during lab experiments. The data collection took place in the Greek Alzheimer’s Association for Dementia and Related Disorders (GAADRD) in Thessaloniki, Greece. The datasets include video and audio recordings as well as data from physiological sensors. The dataset is available to the research community under specific terms of use.
General Info:
– The people recruited for this dataset were 25 elderly people, aged 65 and above, of both genders.
– The participant pool included people that were healthy and people suffering from conditions ranging from Mild Cognitive Impairment (MCI) to mild dementia, and in a few cases full-blown Alzheimer’s Disease (AD).
– The participants are asked to perform following activities of daily living (ADL):
Activities of Interest:
– Reading Article
– Watering Plant
– Preparing Drug Box
– Preparing Drink
– Turning On Radio
– Talking On Phone
– Balancing Account
Video Data:
– RGB and Depth images
– 640×480 resolution
– 25 videos
Ground Truth:
– Activities are manually annotated for each video.
The dataset can be downloaded by following the instruction in Dem@Care Dataset Web-Page.
Tracking Data:
– Videos have been processed for people detection and tracking using [1] and [2].
– Tracking data for 25 videos can be downloaded from here: Tracking_Data_GAADRD
– Tracking data contains following information in XML-format:
i) Timestamp
<MobFrame timeYear=“2012” timeDay=“25” timeMonth=“6” timeMs=“949” timeMin=“40” frameID=“14” timeHour=“14” timeSec=“41” nbMobiles=“1”>
ii) 2D information (please ignore speed information):
<Info2D ySpeed=“0” timeDay=“25” timeHour=“14” yCenter=“114” xSpeed=“0” timeMonth=“6” timeSec=“41” timeMin=“40” yCog=“114” timeYear=“2012” frameID=“14” width=“169” nbMovPix=“0” orientation=“0” xCog=“515” xCenter=“515” camID=“0” length=“68” timeMs=“949” />
iii) 3D information (please ignore speed information):
<Info3D w3D=“662” zSpeed=“0” x=“2028.08” ySpeed=“0” y=“-765.066” h3D=“1561.11” xSpeed=“0” z=“5499.51” l3D=“1097” orientation=“0” />
[1] Anh-Tuan Nghiem, Edouard Auvinet, and Jean Meunier. Head detection using kinect camera and its application to fall detection. In 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA, pages 164–169, 2012.
[2] Duc Phu Chau, Julien Badie, François Bremond, and Monique Thonnat. Online Tracking Parameter Adaptation based on Evaluation. In IEEE International Conference on Advanced Video and Signal-based Surveillance, Krakow, Poland, August 2013.
