Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology.The potential lies in improving anomaly detection while reducing manual labour. However, medical data is often sparse andunavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. Inthis respect, we presentKvasir-Capsule, a large VCE dataset collected from examinations at Bærum Hospital in Norway.Kvasir-Capsuleconsists of118videos from which we can generate2,830,089image frames. We have labelled and medicallyverified44,260frames with a bounding box around detected anomalies from 13 different classes of findings. In addition to theselabelled images, there are2,785,829unlabelled frames included in the dataset. Initial experiments demonstrate the potentialbenefits of AI-based computer-assisted diagnosis systems for VCE. However, they also show that there is great potentialfor improvements, and theKvasir-Capsuledataset can play a valuable role in developing better algorithms in order for VCEtechnology to reach its true potential

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We report work in progress from interdisciplinary research on Assisted Living Technology in smart homes for older adults with mild cognitive impairments or dementia. We present our field trial, the set-up for collecting and storing data from real homes, and preliminary results on action recognition using low resolution depth video cameras. The data have been collected from seven apartments with one resident each over a period of two weeks. We propose a pre-processing of the depth videos by applying an Infinite Response Filter (IIR) for extracting the movements in the frames prior to classification. In this work we classify four actions: TV interaction (turn it on/ off and switch over), standing up, sitting down, and no movement. Our first results indicate that using the IIR filter for movement information extraction improves accuracy and can be an efficient method for recognizing actions. Our current implementation uses a convolutional long short-term memory (ConvLSTM) neural network, and achieved an average peak accuracy of 86%.