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Academic literature on the topic 'Automatic Motion Detection and Analysis'
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Journal articles on the topic "Automatic Motion Detection and Analysis"
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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 features of injection motion, including puncture time and injection depth parameters. We constructed a sample of 126 medical injection training videos of medical students, and skilled observers were employed to determine accurate puncture times. Our experimental results demonstrated that the mean puncture time of medical students was 2.264 s and the mean identification error was 0.330 s. Moreover, we confirmed that there was no significant difference (p = 0.25 with a significance level of α = 0.05) between the predicted value of the system and the ground truth, which provides a basis for the validity and reliability of the system. These results show our system’s ability to automatically detect puncture times and provide a novel approach for training healthcare professionals. At the same time, it provides a key technology for the future development of injection skill assessment systems.
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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 algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words. Findings The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach. Originality/value By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.
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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 processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.
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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 the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.
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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 an inertial sensor for balance activity analysis in community-dwelling older adults. Methods In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.
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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 present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
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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 present 5 inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area-frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and DEM analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% wrongly amalgamated landslides missed and 3.9–4.8% correct polygons wrongly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.