A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Chaaraoui, Alexandros André; Climent-Pérez, Pau; Flórez-Revuelta, Francisco

doi:10.1016/j.eswa.2012.03.005

Cited by 178 publications

(125 citation statements)

References 122 publications

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“…Applying computer vision based monitoring techniques to help with the management of health-related conditions has gained considerable attention in the last decade [22,7]. However, studies on energy expenditure using visual sensors are still strictly limited, possibly due to the absence of labeled data, which is difficult to obtain -not least given the ethical requirements necessary to obtain it.…”

Section: Related Workmentioning

confidence: 99%

Calorific Expenditure Estimation Using Deep Convolutional Network Features

Wang

Tao

Burghardt

et al. 2018

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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Section: Related Workmentioning

confidence: 99%

Calorific Expenditure Estimation Using Deep Convolutional Network Features

Wang

Tao

Burghardt

et al. 2018

2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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“…The adopted taxonomy of Human Behaviour Analysis (HBA) tasks builds up on the work presented in [22]. It is a result of a collaborative e↵ort between researchers working on the SPHERE [126] project.…”

Section: Hierarchical Taxonomy Of Human Behaviour Analysis Tasksmentioning

confidence: 99%

“…At the next level is motion, which is the movement of the entire body or part of the body, not necessarily with intent nor a complete action. "At the action level, human motion is not only detected, but also recognised in order to establish what a person is doing or with which objects the person is interacting" [22]. A sequence of actions performed in some order can be classified as activities.…”

Section: Hierarchical Taxonomy Of Human Behaviour Analysis Tasksmentioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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“…Thus, Microsoft SDK and OpenNI library functions were tested and positions recognized by Growing Neural Gas. The optimal set of characteristics that increases the accuracy of action recognition algorithm was founded by using evolutionary learning techniques [11]. Fall detection service and other actions are supported by a simple prototype that determines the fall by calculating the hip height.…”

Section: Vision Based Sensor Subsystemmentioning

confidence: 99%

A Knowledge Based Framework to Support Active Aging at Home Based Environments

Valero

Bravo

García

et al. 2013

Ambient Assisted Living and Active Aging

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Abstract. Information and Communication Technologies can support ActiveAging strategies in a scenario like the Smart Home. This paper details a person centered distributed framework, called TALISMAN+, whose aim is to promote personal autonomy by taking advantage of knowledge based technologies, sensors networks, mobile devices and internet. The proposed solution can support an elderly person to keep living alone at his house without being obliged to move to a residential center. The framework is composed by five subsystems: a reasoning module that is able to take local decisions at home in order to support active aging, a biomedical variables telemonitorisation platform running on a mobile device, a hybrid reasoning middleware aimed to assess cardiovascular risk in a remote way, a private vision based sensor subsystem, and a secure telematics solution that guarantees confidentiality for personal information. TALISMAN+ framework deployment is being evaluated at a real environment like the Accessible Digital Home.

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A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Cited by 178 publications

References 122 publications

Calorific Expenditure Estimation Using Deep Convolutional Network Features

Calorific Expenditure Estimation Using Deep Convolutional Network Features

Classification and suitability of sensing technologies for activity recognition

A Knowledge Based Framework to Support Active Aging at Home Based Environments

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