Biomedical and Health Sciences
Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living).
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A database of lips traces
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.
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Electronic Health Records and clinical longitudinal data have been visualized in a wide range of applications to assist the understanding of the status and evolution of patients. Few studies have objectively assessed these applications. We utilized the insights-based method to objectively assess the effectiveness of an application that visualizes longitudinal data from the Australian national electronic health record. Five professional psychiatrists took part in the assessment study.
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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
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2420 Views
The dataset contains depth frames collected using Microsoft Kinect v1 during the execution of food and drink intake movements.
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411 Views
The dataset contains depth frames collected using Microsoft Kinect v1 during the execution of food and drink intake movements.
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326 Views
The dataset contains depth frames and skeleton joints collected using Microsoft Kinect v2 and acceleration samples provided by an IMU during the execution of the timed up and go test.
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1831 Views
The dataset contains depth frames and skeleton joints collected using Microsoft Kinect v2 and acceleration samples provided by an IMU during the simulation of ADLs and falls.
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16117 Views
The dataset contains depth frames collected using Microsoft Kinect v1 in top-view configuration and can be used for fall detection.
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3011 Views