A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Abstract

-Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

DataCare: Big Data Analytics Solution for Intelligent Healthcare Management

“…Recently Jalal et al [8] have proposed an intelligent depth video-based human activity recognition system to track elderly patients that could be used as a part of a healthcare management and monitoring system. However, the paper does not [6] have proposed an ontology-based system for prediction patients' readmission within 30 days so that these readmissions can be prevented.…”

Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning

“…It has been used in various fields such as robotics [9], computer engineering [10,11], physical science and health industry [12], natural sciences [13], and industrial academic areas [14][15][16]. As an illustration, Sileye and Jean-Marc [17] deployed head pose detection using the Hidden Markov Model to recognize the visual focus of attention of participants in meetings.…”

3D Reconstruction Framework for Multiple Remote Robots on Cloud System

“…In the area of cloud computing, a failure rules aware node resource provision policy for heterogeneous services consolidated in cloud computing infrastructure has been proposed [10]. Various applications for human pose estimation, tracking, and recognition using red-green-blue (RGB) cameras and depth cameras have also been proposed [13][14][15][16][17][18][19][20][21][22][23][24].…”

“…Nowadays, various innovative technological systems, such as multimedia contexts [1][2][3][4][5][6], video codecs [7,8], healthcare systems [9,10], and smart indoor security systems [11,12] are being actively researched. In the area of cloud computing, a failure rules aware node resource provision policy for heterogeneous services consolidated in cloud computing infrastructure has been proposed [10].…”