Towards accelerometry based static posture identification

Xu, Min; Goldfain, Albert; Chowdhury, Atanu Roy; DelloStritto, Jim

doi:10.1109/ccnc.2011.5766477

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Xu et. al proposed a scheme in which body postures could be modeled based on the Euler angles of the torso, arms and legs [20]. The algorithm proposed here was validated by the usage of a classification engine in Matlab.…”

Section: Fig 1 Kinect Sensormentioning

confidence: 99%

A study on leg posture recognition from Indian classical dance using Kinect sensor

Saha

Ghosh

Konar

et al. 2013

2013 International Conference on Human Computer Interactions (ICHCI)

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This paper proposes a simple yet a novel technique to recognize leg postures in Indian classical dance by making use of a Kinect sensor. The sensor device has the ability to track the skeleton of the subject with the help of a visible camera and an IR camera coupled to an IR laser and diffraction grating. Twenty five leg postures from 'Odissi', an Indian Classical dance have been used for the evaluation our proposed algorithm. This methodology extracts eight features, which in turn can be categorized under three levels of symmetry viz. the vertical symmetry, the horizontal symmetry and the angular symmetry. Finally a similarity function is devised which is the basis of the leg posture recognition technique. This method provides better human computer interaction and also aims at spreading the dance form for e-learning purpose. The proposed algorithm can be applied for any dance form for leg posture recognition purposes. It gives 86.75% accuracy with five subjects.Index Terms-angular symmetry, human computer interaction, horizontal symmetry, leg posture, similarity function, vertical symmetry.

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Section: Fig 1 Kinect Sensormentioning

confidence: 99%

A study on leg posture recognition from Indian classical dance using Kinect sensor

Saha

Ghosh

Konar

et al. 2013

2013 International Conference on Human Computer Interactions (ICHCI)

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“…Previous studies have addressed worker positioning around the CMM rather than posture [9,10] . Some investigations unrelated to mining have focused on wireless and embedded sensor technology to determine human posture [11–14] . However, these studies were ultimately concerned with human position in specific postures.…”

Section: Introductionmentioning

confidence: 99%

Determining Underground Mining Work Postures Using Motion Capture and Digital Human Modeling

Lutz

DuCarme

Smith

et al. 2016

JEHS

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According to Mine Safety and Health Administration (MSHA) data, during 2008–2012 in the U.S., there were, on average, 65 lost-time accidents per year during routine mining and maintenance activities involving remote-controlled continuous mining machines (CMMs). To address this problem, the National Institute for Occupational Safety and Health (NIOSH) is currently investigating the implementation and integration of existing and emerging technologies in underground mines to provide automated, intelligent proximity detection (iPD) devices on CMMs. One research goal of NIOSH is to enhance the proximity detection system by improving its capability to track and determine identity, position, and posture of multiple workers, and to selectively disable machine functions to keep workers and machine operators safe. Posture of the miner can determine the safe working distance from a CMM by way of the variation in the proximity detection magnetic field. NIOSH collected and analyzed motion capture data and calculated joint angles of the back, hips, and knees from various postures on 12 human subjects. The results of the analysis suggests that lower body postures can be identified by observing the changes in joint angles of the right hip, left hip, right knee, and left knee.

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“…Some studies over-instrumented test subjects with far too many sensors [129], which might be acceptable in short-term lab experiments but not in free-living environments. Research tools/sensors are useful to prove a given concept but to be applicable (and ubiquitous) to people's lives they need to take di↵erent shape, more compelling and comfortable to the user.…”

Section: Suitability Of Sensing Technologies For Home Armentioning

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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Towards accelerometry based static posture identification

Cited by 7 publications

References 14 publications

A study on leg posture recognition from Indian classical dance using Kinect sensor

A study on leg posture recognition from Indian classical dance using Kinect sensor

Determining Underground Mining Work Postures Using Motion Capture and Digital Human Modeling

Classification and suitability of sensing technologies for activity recognition

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