We present our preliminary data analysis towards an automated assessment system for the Activate Test for Embodied Cognition (ATEC), a test which measures cognitive skills through physical activity. More specifically, we present two core ATEC tasks designed to assess attention, working memory, response inhibition, rhythm and coordination in children: the Sailor Step and the Ball-Dropto-the-Beat task. These tasks are specifically designed to assess lower and upper body accuracy, response inhibition and rhythm. Motion data were collected through the Kinect camera. This paper presents an overview of the assessment tasks, the data collection, and annotation with a preliminary analysis towards an automated scoring system through machine learning and computer vision methods.
Accurate hand segmentation is vital in many applications where the hands play a central role. Examples include sign language recognition, action recognition, and gesture recognition. A relatively unexplored obstacle to correct hand segmentation is the case of hands overlapping with the face. Inspired by the hand over face segmentation problem, we have developed the novel Multi-level Pyramid Scene Parsing Network (MPSPNet) for semantic segmentation. We evaluate MPSPNet on two standard object segmentation datasets (NYUDv2, PASCAL VOC) and two recently published and challenging datasets focusing on scenarios in which the hands overlap the face, VLM-HandOverFace and HOF. Additionally, we compare our method against several state-of-the-art hand segmentation methods such as RefineNet and PSPNet. We empirically show that the proposed method achieves a 6% improvement in mIOU compared with RefineNet on the VLM-HandOverFace dataset and a 15% improvement in mIOU compared with PSPNet on the HOF dataset.
CCS CONCEPTS• Computing methodologies ! Computer vision; Machine learning.
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