motion capture

Human arm motion data including forearm, upper-arm, and scapula link IMU modules beside SLAM reference position measurements compared to VICON as ground trouth.


We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c


This dataset features cooking activities with recipes and gestures labeled. The data has been collected using two smartphones (right arm and left hip), two smartwatches (both wrists) and one motion capture system with 29 markers. There were 4 subjects who prepared 3 recipes (sandwich, fruit salad, cereal) 5 times each. The subjects followed a script for each recipe but acted as naturally as possible


In an aging population, the demand for nurse workers increases to care for elders. Helping nurse workers make their work more efficient, will help increase elders quality of life, as the nurses can focus their efforts on care activities instead of other activities such as documentation.
Activity Recognition can be used for this goal. If we can recognize what activity a nurse is engaged in, we can partially automate documentation process to reduce time spent on this task, monitor care plan compliance to assure that all care activities have been done for each elder, among others.

Last Updated On: 
Fri, 12/06/2019 - 03:40