Real-time human motion analysis by image skeletonization

Fujiyoshi, Hironobu; Lipton, A.J.

doi:10.1109/acv.1998.732852

Cited by 253 publications

(203 citation statements)

References 6 publications

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“…Such methods vary from correlation [12] and silhouette matching [32] to HMMs [17] and neural networks [54]. The objective is to recognize actions such as walking [123], carrying objects [51], removing and placing objects [96], pointing and waving [32], gestures for control [4], standing vs walking [53], walking vs jogging [115], walking vs running [43], and classifying various aerobic exercises [122] or ballet dance steps [21].…”

Section: Dynamic Recognitionmentioning

confidence: 99%

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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Section: Dynamic Recognitionmentioning

confidence: 99%

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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“…To accomplish this, we developed a vision module that recognizes the user's arm gestures/poses in real time, Several different approaches have been developed to accomplish tracking of human motion, both in 2-D and 3-D, including skeletonization methods [44], [45], gesture recognition using probabilistic methods [46], and colorbased tracking [47], among others. We opted to create an arm pose recognition system that takes advantage of our simplified exercise setup in order to achieve real-time results without imposing any markers on the user.…”

Section: Vision Modulementioning

confidence: 99%

Using Socially Assistive Human–Robot Interaction to Motivate Physical Exercise for Older Adults1

Fasola¹,

Matarić²

2015

Household Service Robotics

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A robot designed to engage elderly users in physical exercise is described in this paper; a user study indicates a strong user preference for a relational robot.By Juan Fasola and Maja J Matarić, Fellow IEEE ABSTRACT | In this paper, we present the design, implementation, and user study evaluation of a socially assistive robot (SAR) system designed to engage elderly users in physical exercise aimed at achieving health benefits and improving quality of life. We discuss our design methodology, which incorporates insights from psychology research in the area of intrinsic motivation, and focuses on maintaining engagement through personalized social interaction. We describe two user studies conducted to test the motivation theory in practice with our system. The first study investigated the role of praise and relational discourse in the exercise system by comparing a relational robot coach to a nonrelational robot coach. The second study evaluated participant preferences regarding user choice in the task scenario. Both studies served to evaluate the feasibility and overall effectiveness of the robot exercise system. The results of both studies are presented; they show a strong user preference for the relational over the nonrelational robot in terms of enjoyableness, companionship, and as an exercise coach, varying user preferences regarding choice, and high user ratings of the system across multiple metrics. The outcomes of the presented user studies, brought together, support the motivational capabilities of the robot, and demonstrate the viability and usefulness of the system in motivating exercise in elderly users.

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“…Cutler and Davis [33] explored the nature of an object's self-similarity in periodic motion and applied Time-Frequency analysis to detect and characterise the periodicity in videos. Fujiyoshi and Lipton [29] generate a "star" skeleton from the object boundary. They apply Fourier analysis to its skeleton for detecting periodic motion.…”

Section: Human Periodic Motion Recognitionmentioning

confidence: 99%

Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach

Meng

Holstein

2006

Image and Vision Computing

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Recognition of Human Periodic Movements From UnstructuredInformation Using A Motion-based Frequency Domain Approach Feature-based motion cues play important role in biological visual perception. We present a motion-based frequency-domain scheme for human periodic motion recognition. As a baseline study of feature-based recognition we use unstructured feature-point kinematic data obtained directly from a marker-based optical motion capture (MoCap) system, rather than accommodate bootstrapping from the low-level image processing of feature detection. Motion power spectral analysis is applied to a set of unidentified trajectories of feature points representing whole body kinematics. Feature power vectors are extracted from motion power spectra and mapped to a low dimensionality of feature space as motion templates that offer frequency domain signatures to characterise different periodic motions. Recognition of a new instance of periodic motion against pre-stored motion templates is carried out by seeking best motion power spectral similarity. We test this method through nine examples of human periodic motion using MoCap data. The recognition results demonstrate that feature-based spectral analysis allows classification of periodic motions from low-level, un-structured interpretation without recovering underlying kinematics. Contrasting with common structure-based spatio-temporal approaches, this motion-based frequency-domain method avoids a time-consuming recovery of underlying kinematic structures in visual analysis and largely reduces the parameter domain in the presence of human motion irregularities.

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Real-time human motion analysis by image skeletonization

Cited by 253 publications

References 6 publications

A Survey of Computer Vision-Based Human Motion Capture

A Survey of Computer Vision-Based Human Motion Capture

Using Socially Assistive Human–Robot Interaction to Motivate Physical Exercise for Older Adults1

Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach

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