Optical motion capture system with pan-tilt camera tracking and real time data processing

Kurihara, Keisuke; Hoshino, Shotaro; Yamane, Katsu; Nakamura, Yūichi

doi:10.1109/robot.2002.1014713

Cited by 63 publications

(46 citation statements)

References 7 publications

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“…Table I summarizes the motions performed in terms of the extracted segments. The motion capture system [17] captures the Cartesian position of markers located on the body (for example, shoulder, elbow, wrist, hip, knee, etc.) with a sampling rate of 10ms, and performs inverse kinematics computations to convert the data to joint angle positions in real time.…”

Section: Methodsmentioning

confidence: 99%

Scaffolding on-line segmentation of full body human motion patterns

Kulić

Nakamura

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper develops an approach for on-line segmentation of whole body human motion patterns during human motion observation and learning. A Hidden Markov Model is used to represent the incoming data sequence, where each model state represents the probability density estimate over a window of the data. Based on the assumption that data belonging to the same motion primitive will have the same underlying distribution, the segmentation is implemented by finding the optimum state sequence over the developed model. The basic algorithm is modified to add the capability for modifying the model based on known motion primitives. The inclusion of such scaffolding motion primitives can improve the performance of the basic segmentation algorithm. The modified algorithm is tested on a corpus of continuous human motion data to show the efficacy of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

Scaffolding on-line segmentation of full body human motion patterns

Kulić

Nakamura

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

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“…The motion capture system [24] captures the Cartesian position of markers located on the body (for example, shoulder, elbow, wrist, hip, knee, etc.) with a sampling rate of 10ms.…”

Section: Methodsmentioning

confidence: 99%

Incremental learning of full body motion primitives and their sequencing through human motion observation

Kulić

Ott

Lee

et al. 2011

The International Journal of Robotics Research

188

113

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Abstract-This paper describes an approach for on-line, incremental learning of full body motion primitives from observation of human motion. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Motion segments are next incrementally clustered and organized into a hierarchical tree structure representing the known motion primitives. Motion primitives are encoded using hidden Markov models, so that the same model can be used for both motion recognition and motion generation. At the same time, the relationship between motion primitives is learned via the construction of a motion primitive graph. The motion primitive graph can then be used to construct motions, consisting of sequences of motion primitives. The approach is implemented and tested on the IRT humanoid robot.

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“…Alexander Hornung discusses a method that automatically estimates all parameters on the fly [6]. Kazuutaka Kurihaha proposed an optical motion capture system with pan-tilt camera tracking, which expanded capturing range [9]. Victor B. Zordan used a physical model to map optical motion capture data to corresponding skeletal motion [15].…”

Section: Introductionmentioning

confidence: 99%

Efficient Marker Matching Using Pair-Wise Constraints in Physical Therapy

Johnson

Xie

Slaboda

et al. 2010

Advances in Visual Computing

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Abstract. In this paper, we report a robust, efficient, and automatic method for matching infrared tracked markers for human motion analysis in computer-aided physical therapy applications. The challenges of this task stem from non-rigid marker motion, occlusion, and timing requirements. To overcome these difficulties, we use pair-wise distance constraints for marker identification. To meet the timing requirements, we first reduce the candidate marker labels by proximity constraints before enforcing the pair-wise constraints. Experiments with 38 real motion sequences, our method has shown superior accuracy and significant speedup over a semi-automatic proprietary method and the Iterative Closest Point (ICP) approach.

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Optical motion capture system with pan-tilt camera tracking and real time data processing

Cited by 63 publications

References 7 publications

Scaffolding on-line segmentation of full body human motion patterns

Scaffolding on-line segmentation of full body human motion patterns

Incremental learning of full body motion primitives and their sequencing through human motion observation

Efficient Marker Matching Using Pair-Wise Constraints in Physical Therapy

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