A Study on Natural Interaction for Human Body Motion Using Depth Image Data

Sidik, Mohd Kufaisal bin Mohd; Sunar, Mohd Shahrizal; Ismail, Ismahafezi; Mokhtar, Mahani; Jusoh, Norela

doi:10.1109/dmdcm.2011.26

Cited by 15 publications

(2 citation statements)

References 14 publications

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“…Camera-based systems capture video of individuals for subsequent movement analysis (e.g., [ 23 – 25 ]). Recently, the Microsoft Kinect [ 26 ] has been used for near real-time full-body motion capture and gesture recognition (e.g., [ 27 – 29 ]). The Kinect provides sensor data from a color camera, an infrared projector and camera that uses a structured light approach to calculate depth, and a microphone array.…”

Section: Introductionmentioning

confidence: 99%

Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes

2015

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Researchers in the cognitive and affective sciences investigate how thoughts and feelings are reflected in the bodily response systems including peripheral physiology, facial features, and body movements. One specific question along this line of research is how cognition and affect are manifested in the dynamics of general body movements. Progress in this area can be accelerated by inexpensive, non-intrusive, portable, scalable, and easy to calibrate movement tracking systems. Towards this end, this paper presents and validates Motion Tracker, a simple yet effective software program that uses established computer vision techniques to estimate the amount a person moves from a video of the person engaged in a task (available for download from http://jakory.com/motion-tracker/). The system works with any commercially available camera and with existing videos, thereby affording inexpensive, non-intrusive, and potentially portable and scalable estimation of body movement. Strong between-subject correlations were obtained between Motion Tracker’s estimates of movement and body movements recorded from the seat (r =.720) and back (r = .695 for participants with higher back movement) of a chair affixed with pressure-sensors while completing a 32-minute computerized task (Study 1). Within-subject cross-correlations were also strong for both the seat (r =.606) and back (r = .507). In Study 2, between-subject correlations between Motion Tracker’s movement estimates and movements recorded from an accelerometer worn on the wrist were also strong (rs = .801, .679, and .681) while people performed three brief actions (e.g., waving). Finally, in Study 3 the within-subject cross-correlation was high (r = .855) when Motion Tracker’s estimates were correlated with the movement of a person’s head as tracked with a Kinect while the person was seated at a desk (Study 3). Best-practice recommendations, limitations, and planned extensions of the system are discussed.

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Section: Introductionmentioning

confidence: 99%

Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes

2015

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“…Computer vision approaches have been used to recognise human activities, e.g., [2][3][4][5], human body pose and motion [34], and measuring task performance, e.g., [6] and [7]. Models are either learned from image or motion-based features.…”

Section: Introductionmentioning

confidence: 99%

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

John

Hipiny

Ujir

2019

2019 International Conference on Computer and Drone Applications (IConDA)

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It is recommended for a novice to engage a trained and experienced person (i.e., a coach) before starting an unfamiliar aerobic or weight routine. The coach's task is to provide real-time feedbacks to ensure that the routine is performed in a correct manner. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method is implemented inside an Augmented Reality (AR) desktop app that employs a single RGB camera to capture still images of the subject as he or she progresses through the routine. The background-subtracted body pose image is compared against the exemplar body pose image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function is reported to have an accuracy of 93.67%.

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Two Dimensional and Gesture Based Medical Visualization Interface and Image Processing Methodologies to Aid and Diagnose of Lung Cancer

Krishnan

Nishan²,

Theerthagiri

et al. 2021

Intelligent Systems Reference Library

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A Study on Natural Interaction for Human Body Motion Using Depth Image Data

Cited by 15 publications

References 14 publications

Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes

Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

Two Dimensional and Gesture Based Medical Visualization Interface and Image Processing Methodologies to Aid and Diagnose of Lung Cancer

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