Human activity recognition in pervasive health-care: Supporting efficient remote collaboration

Osmani, Venet; Balasubramaniam, Sasitharan; Botvich, Dmitri

doi:10.1016/j.jnca.2007.11.002

Cited by 119 publications

(44 citation statements)

References 28 publications

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“…ICT for the elderly has traditionally focused on healthcare aspects, and made extensive use of the rich sensor technology that allows monitoring and engaging subjects in smart environment scenarios [2,26,41]. High-impact applications in these scenarios include fall detection systems [7,25], tools for combatting cognitive decline [3,17,28], and serious games for exercising, rehabilitation and for socializing [6,11,12,21,30,31,37].…”

Section: Discussionmentioning

confidence: 99%

Behavior Analysis for Elderly

Salah

Kröse

Cook

2015

Human Behavior Understanding

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Abstract. Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU'15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.

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Section: Discussionmentioning

confidence: 99%

Behavior Analysis for Elderly

Salah

Kröse

Cook

2015

Human Behavior Understanding

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“…where C (1) and C (2) are the pair-coordinates of all three respective axis. Both equations are examined for angular and sinusoidal features characteristics.…”

Section: ) Spatiotemporal Joints Angular Featuresmentioning

confidence: 99%

“…[25] 68.0 Graph based genetic programming [37] 72.1 Moving Pose [38] 73.8 Integrating Joints features [39] 76.0 Motion features [40] 79.1 Actionlet ensemble [25] 85.7 Super normal vector [41] 86.2 Depth Cuboid Similarity features [42] 88.2 Multi-Features method 92. 2 Also, we compare the recognition performance using MSRDailyActivity3D dataset where the proposed method achieved a superior mean recognition rate of 92.2% over the state of the methods [24], [25], [37]- [42] as shown in Table VII. …”

Section: ) Msrdailyactivity3d Datasetmentioning

confidence: 99%

“…IntroductIon M onItorInG human activities of daily living is an essential way of describing the functional and health status of a human [1]. Therefore, human activity recognition (HAR) is one of genuine components in personalized life-care and healthcare systems, especially for the elderly and disabled [2]. To monitor daily activities of the elderly people, video-cameras can be deployed in smart environments, such as smart homes or smart hospitals to acquire time-series activity video clips.…”

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confidence: 99%

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

Jalal¹,

Kamal²,

Kim³

2017

IJIMAI

101

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-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.

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“…Human activity recognition (HAR) receives intensive attentions in recent years, due to many practical applications, such as video surveillance [14] [15], health care [16] [17] and context-aware computing [18] [19]. In general, pattern recognition schemes can directly handle the samples which are represented in a vector space.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Triaxial Accelerometer-Based Energy Efficient Human Activity Recognition Using Smartphone

2017

IJAERD

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Human activity recognition in pervasive health-care: Supporting efficient remote collaboration

Cited by 119 publications

References 28 publications

Behavior Analysis for Elderly

Behavior Analysis for Elderly

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

Real-Time Triaxial Accelerometer-Based Energy Efficient Human Activity Recognition Using Smartphone

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