In Situ Motion Capture of Speed Skating: Escaping the Treadmill

Boyd, Jeffrey E.; Godbout, Andrew; Thornton, Chris

doi:10.1109/crv.2012.68

Cited by 12 publications

(10 citation statements)

References 11 publications

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“…15 The system can follow a speed skater on the ice to capture the full-body motion. The moving robotic platform addresses the limited viewing area of Kinect and provides a visual guidance for athletes.…”

Section: Remote Controlmentioning

confidence: 99%

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Lun

Zhao

2015

Int. J. Patt. Recogn. Artif. Intell.

281

109

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Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classi¯cation of motion recognition techniques to highlight the di®erent approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lowerlevel computer vision tasks such as segmentation, object detection and human pose estimation.

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Section: Remote Controlmentioning

confidence: 99%

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Lun

Zhao

2015

Int. J. Patt. Recogn. Artif. Intell.

281

109

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“…While traditional systems confine biomechanical analysis to the laboratory, developments in markerless optical systems and inertial motion capture have enabled movement to be studied across a more diverse range of environments, such as on the rowing lake [1], tennis court [2], and ice rink [3].…”

Section: Introductionmentioning

confidence: 99%

Markerless motion capture using appearance and inertial data

Wong

Zhang

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Current monitoring techniques for biomechanical analysis typically capture a snapshot of the state of the subject due to challenges associated with long-term monitoring. Continuous long-term capture of biomechanics can be used to assess performance in the workplace and rehabilitation at home. Noninvasive motion capture using small low-power wearable sensors and camera systems have been explored, however, drift and occlusions have limited their ability to reliably capture motion over long durations. In this paper, we propose to combine 3D pose estimation from inertial motion capture with 2D pose estimation from vision to obtain more robust posture tracking. To handle the changing appearance of the human body due to pose variations and illumination changes, our implementation is based upon Least Soft-Threshold Squares Tracking. Constraints on the variation of the appearance model and estimated pose from an inertial motion capture system are used to correct 2D and 3D estimates simultaneously. We evaluate the performance of our method with three state-of-the-art trackers, Incremental Visual Tracking, Multiple Instance Learning, and Least Soft-Threshold Squares Tracking. In our experiments, we track the movement of the upper limbs. While the results indicate an improvement in tracking accuracy at some joint locations, they also show that the result can be further improved. Conclusions and further work required to improve our results are discussed.

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“…Many research groups all over the world started to deal with research in this multidisciplinary area where knowledge of information engineering and biomedicine are necessary. For all we name only:  neural networks in the brain cancer diagnosis [1],  advanced statistical analysis in the detection of nasopharyngeal cancer [2],  computational intelligence in a diagnosis of neck and head cancer [3],  analysis and diagnosis of Parkinson's disease [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Software Tool For Voice Disorder Diagnostics

Jičínský¹,

Mareš²

2019

TransVSBms

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Causes of voice disorders may vary the same way as treatment techniques. Surgical intervention is the most used treatment method. As for not so serious cases, vocal exercises can be efficiently used instead. Still, there are only few methods to find right diagnosis or classify the scale of impact on patient's voice. Nowadays, it is possible to extract key features (fundamental frequency, sound pressure level and less common such as cepstral coefficients, zero crossing rate or spectral energy) from patient's speech using state-of-the-art voice processing methods. So, the software called Voice disorder diagnostician was designed. The tool that can store patient's data and immediately provide the results of analysis. Abstrakt Příčiny vzniku poruch hlasu mohou být různé stejně tak jako jejich léčba. Nejčastěji se poruchy hlasu léčí chirurgicky tj. operací nebo v méně závažnějších případech hlasovými cvičeními. Přesto však v současné době existuje jen velmi málo metod, které mohou spolehlivě určit diagnózu pacienta a míru poškození hlasu. S využitím aktuálních metod pro zpracování řeči je možné určit klíčové parametry jako je základní tón hlasu, hladina akustického tlaku, ale i méně běžné jako například kepstrální koeficienty, počet průchodů nulou, či spektrální energie. Pro tyto účely byla vytvořena aplikace s názvem Voice disorder diagnostician. Nástroj, který umožňuje ukládat data od pacientů a provádět jejich okamžitou analýzu.

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In Situ Motion Capture of Speed Skating: Escaping the Treadmill

Cited by 12 publications

References 11 publications

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Markerless motion capture using appearance and inertial data

Software Tool For Voice Disorder Diagnostics

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