Category:
Other
Net4Age Ontology Terms:
aging
, computer
, design
, device
, disease
, healthcare
, medical science
, patient
, people
, technology
, usability
, wellbeing
Description:
Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem.
Overview:
Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision.
Objectives:
To propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem.
Initiatives:
Innovative approach, well explained, progressive
Shortcomings:
Description is limited to academy, because it is explained in research paper
Relevance:
4
Relevance Description:
Useful paper and useful technology
Quality:
4
Opinion:
Practical and well based approach and technology
Recommended:
Yes
Overlap:
Yes
Overlap Detail:
Microsoft Kinect (Zhang, 2012) and Intel Realsense (Keselman et al., 2017)
Sources:
https://peerj.com/articles/cs-442/
Keywords:
Posture detection, person measurements in space, human body, device, application, technology
Submitted By:
Rytis Maskeliunas
Email:
milica.solarevic@gmail.com