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Relevant bibliographies by topics / Automatic Motion Detection and Analysis

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Journal articles

Academic literature on the topic 'Automatic Motion Detection and Analysis'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "Automatic Motion Detection and Analysis"

1

Li, Zhe, Aya Kanazuka, Atsushi Hojo, et al. "Automatic Puncture Timing Detection for Multi-Camera Injection Motion Analysis." Applied Sciences 13, no. 12 (2023): 7120. http://dx.doi.org/10.3390/app13127120.

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Precisely detecting puncture times has long posed a challenge in medical education. This challenge is attributable not only to the subjective nature of human evaluation but also to the insufficiency of effective detection techniques, resulting in many medical students lacking full proficiency in injection skills upon entering clinical practice. To address this issue, we propose a novel detection method that enables automatic detection of puncture times during injection without needing wearable devices. In this study, we utilized a hardware system and the YOLOv7 algorithm to detect critical fea

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Fu, Eugene Yujun, Hong Va Leong, Grace Ngai, and Stephen C. F. Chan. "Automatic fight detection in surveillance videos." International Journal of Pervasive Computing and Communications 13, no. 2 (2017): 130–56. http://dx.doi.org/10.1108/ijpcc-02-2017-0018.

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Purpose Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner. Design/methodology/approach Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithm

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DAIMON, Tatsuru, Kazuhide MOTEGI, and Hironao KAWASHIMA. "Automatic detection of driver's eye motion using video image sequence analysis." Japanese journal of ergonomics 31, no. 1 (1995): 39–50. http://dx.doi.org/10.5100/jje.31.39.

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Kotoku, Jun’ichi, Shinobu Kumagai, Ryouhei Uemura, Susumu Nakabayashi, and Takenori Kobayashi. "Automatic Anomaly Detection of Respiratory Motion Based on Singular Spectrum Analysis." International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 05, no. 01 (2016): 88–95. http://dx.doi.org/10.4236/ijmpcero.2016.51009.

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Zhang, Peng Jun, Yu Cheng Bo, Hui Yuan Wang, and Qiang Li. "Fault Detection of Artillery Automatic Loading System Based on PCA." Advanced Materials Research 590 (November 2012): 459–64. http://dx.doi.org/10.4028/www.scientific.net/amr.590.459.

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The motion process of the automatic loading system is a high overloading and intermittent motion environment will bring about motor windings loosening, transmission system wear and tear, fracture, sensor failure and other security risks or system failures. In the paper no-stationary signal analysis by wavelet transform through wavelet decomposition and non-linear threshold de-noising. And use PCA established system model for on-line monitor. By calculate and analysis four kind of result to find fault source. Finally through the experimental prove the reliability of the method.

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D’Aloia, Matteo, Annalisa Longo, and Maria Rizzi. "Noisy ECG Signal Analysis for Automatic Peak Detection." Information 10, no. 2 (2019): 35. http://dx.doi.org/10.3390/info10020035.

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Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal proc

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Schütz, Anne K., Verena Schöler , E. Tobias Krause , et al. "Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes." Animals 11, no. 6 (2021): 1723. http://dx.doi.org/10.3390/ani11061723.

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Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of

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Hsu, Yu-Cheng, Hailiang Wang, Yang Zhao, Frank Chen, and Kwok-Leung Tsui. "Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation." Journal of Medical Internet Research 23, no. 12 (2021): e30135. http://dx.doi.org/10.2196/30135.

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Background Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective The objective of this study was to develop an automatic motion data analytics framework using signal data collected from a

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Marc, O., and N. Hovius. "Amalgamation in landslide maps: effects and automatic detection." Natural Hazards and Earth System Sciences 15, no. 4 (2015): 723–33. http://dx.doi.org/10.5194/nhess-15-723-2015.

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Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We

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Marc, O., and N. Hovius. "Amalgamation in landslide maps: effects and automatic detection." Natural Hazards and Earth System Sciences Discussions 2, no. 12 (2014): 7651–78. http://dx.doi.org/10.5194/nhessd-2-7651-2014.

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Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area-frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in datasets produced by automated mapping. We p

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