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Artículos de revistas sobre el tema "Classification des postures"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 5 de febrero de 2022

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1

Kim, Yong, Youngdoo Son, Wonjoon Kim, Byungki Jin, and Myung Yun. "Classification of Children’s Sitting Postures Using Machine Learning Algorithms." Applied Sciences 8, no. 8 (2018): 1280. http://dx.doi.org/10.3390/app8081280.

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural netw

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2

Kim, Wonjoon, Byungki Jin, Sanghyun Choo, Chang S. Nam, and Myung Hwan Yun. "Designing of smart chair for monitoring of sitting posture using convolutional neural networks." Data Technologies and Applications 53, no. 2 (2019): 142–55. http://dx.doi.org/10.1108/dta-03-2018-0021.

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Purpose Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits. Design/methodology/approach For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children pa

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3

Hwang, Jaejin, and Kyung-Sun Lee. "Classification of Whole-Body Postural Discomfort Using Cluster Analysis." International Journal of Environmental Research and Public Health 17, no. 22 (2020): 8314. http://dx.doi.org/10.3390/ijerph17228314.

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The objectives of this study were to evaluate the effect of gender and postures of the neck, trunk, and knee on overall postural discomfort, and to classify combined postures into different postural discomfort groups. A total of 95 participants (42 males and 53 females) performed 45 different static postures, which were a combination of 3 neck angles, 5 trunk angles, and 3 knee angles, and rated the perceived postural discomfort. Non-hierarchical K-means cluster analysis was employed to classify the 45 different combined postures into several postural discomfort groups. Postural discomfort was

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4

Martins, Leonardo, Rui Lucena, Rui Almeida, et al. "Intelligent Chair Sensor." International Journal of System Dynamics Applications 3, no. 2 (2014): 65–80. http://dx.doi.org/10.4018/ijsda.2014040105.

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In order to develop an intelligent system capable of posture classification and correction the authors developed a chair prototype equipped with air bladders in the chair's seat pad and backrest, which can in turn detect the user posture based on the pressure inside said bladders and change their conformation by inflation or deflation. Pressure maps for eleven standardized postures were gathered in order to automatically detect the user's posture, with resource to neural networks classifiers. First the authors tried to find the best parameters for the neural network classification of our data,

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5

Klishkovskaia, Tatiana, Andrey Aksenov, Aleksandr Sinitca, Anna Zamansky, Oleg A. Markelov, and Dmitry Kaplun. "Development of Classification Algorithms for the Detection of Postures Using Non-Marker-Based Motion Capture Systems." Applied Sciences 10, no. 11 (2020): 4028. http://dx.doi.org/10.3390/app10114028.

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The rapid development of algorithms for skeletal postural detection with relatively inexpensive contactless systems and cameras opens up the possibility of monitoring and assessing the health and wellbeing of humans. However, the evaluation and confirmation of posture classifications are still needed. The purpose of this study was therefore to develop a simple algorithm for the automatic classification of human posture detection. The most affordable solution for this project was through using a Kinect V2, enabling the identification of 25 joints, so as to record movements and postures for data

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6

KWON, YURI, JI-WON KIM, JAE-HOON HEO, HYEONG-MIN JEON, EUI-BUM CHOI, and GWANG-MOON EOM. "CLASSIFICATION OF SPINAL POSTURES DURING CROSS-LEGGED SITTING ON THE FLOOR." Journal of Mechanics in Medicine and Biology 19, no. 08 (2019): 1940056. http://dx.doi.org/10.1142/s0219519419400566.

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One of the most frequent sitting styles of Asians in everyday life is a cross-legged sitting. The cross-legged sitting results in higher compression load in spine than sitting on a chair, so a proper sitting posture is more needed. The purpose of this study was to classify the spinal posture during cross-legged sitting from the seat pressure pattern for future usage in the posture monitoring system. Twenty young men participated in this study. The seat pressure was measured for three spinal postures of flat, slump, and lordosis when subjects were instructed to pose a certain posture while seat

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7

Alinia, Parastoo, Ali Samadani, Mladen Milosevic, Hassan Ghasemzadeh, and Saman Parvaneh. "Pervasive Lying Posture Tracking." Sensors 20, no. 20 (2020): 5953. http://dx.doi.org/10.3390/s20205953.

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Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Seco

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8

Laudanski, Annemarie F., and Stacey M. Acker. "Classification of high knee flexion postures using EMG signals." Work 68, no. 3 (2021): 701–9. http://dx.doi.org/10.3233/wor-203404.

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BACKGROUND: High knee flexion postures are often adopted in occupational settings and may lead to increased risk of knee osteoarthritis. Pattern recognition algorithms using wireless electromyographic (EMG) signals may be capable of detecting and quantifying occupational exposures throughout a working day. OBJECTIVE: To develop a k-Nearest Neighbor (kNN) algorithm for the classification of eight high knee flexion activities frequently observed in childcare. METHODS: EMG signals from eight lower limb muscles were recorded for 30 participants, signals were decomposed into time- and frequency-dom

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9

Zhao, Mingming, Georges Beurier, Hongyan Wang, and Xuguang Wang. "Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution." Sensors 21, no. 10 (2021): 3346. http://dx.doi.org/10.3390/s21103346.

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Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures

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10

Tam, Andy Yiu-Chau, Bryan Pak-Hei So, Tim Tin-Chun Chan, Alyssa Ka-Yan Cheung, Duo Wai-Chi Wong, and James Chung-Wai Cheung. "A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions." Sensors 21, no. 16 (2021): 5553. http://dx.doi.org/10.3390/s21165553.

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Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 w

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11

Park, So-Hyun, and Young-Ho Park. "Audio-Visual Tensor Fusion Network for Piano Player Posture Classification." Applied Sciences 10, no. 19 (2020): 6857. http://dx.doi.org/10.3390/app10196857.

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Playing the piano in the correct position is important because the correct position helps to produce good sound and prevents injuries. Many studies have been conducted in the field of piano playing posture recognition that combines various techniques. Most of these techniques are based on analyzing visual information. However, in the piano education field, it is essential to utilize audio information in addition to visual information due to the deep relationship between posture and sound. In this paper, we propose an audio-visual tensor fusion network (simply, AV-TFN) for piano performance pos

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12

Zhang, Qingyun, Jin Tao, Qinglin Sun, Xianyi Zeng, Matthias Dehmer, and Quan Zhou. "A Fall Posture Classification and Recognition Method Based on Wavelet Packet Transform and Support Vector Machine." Applied Sciences 11, no. 11 (2021): 5030. http://dx.doi.org/10.3390/app11115030.

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An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is essential for the rescue and care of the elderly. In this paper, a novel method was proposed to improve the classification and recognition accuracy of fall postures. Firstly, the wavelet packet transform was used to extract multiple features from sample data. Secondly, random forest was used to evaluate the importance of the extracted features and obtain effective features throug

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13

Li, Guan, Zhifeng Liu, Ligang Cai, and Jun Yan. "Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory." Sensors 20, no. 4 (2020): 1158. http://dx.doi.org/10.3390/s20041158.

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During human–robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predict the intended actions of human workers in an HRC environment. To this end, it explores deep learning based on standing-posture recognition and a multi-recognition algorithm fusion method for HRC. To acquire the pressure-distribution data, ten experimental participants stood on a pressure-sensing floor embedded with thin-film pressure sensors. The

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14

Kappattanavar, Arpita Mallikarjuna, Nico Steckhan, Jan Philipp Sachs, Harry Freitas da Cruz, Erwin Böttinger, and Bert Arnrich. "Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review." JMIR Biomedical Engineering 6, no. 1 (2021): e21105. http://dx.doi.org/10.2196/21105.

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Background A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements. Methods A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases

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15

Zerrouki, N., and A. Houacine. "Automatic Classification of Human Body Postures Based on the Truncated SVD." Journal of Advances in Computer Networks 2, no. 1 (2014): 58–62. http://dx.doi.org/10.7763/jacn.2014.v2.82.

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16

Liu, Yuanguo, and Ying Wu. "A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm." Traitement du Signal 38, no. 3 (2021): 599–605. http://dx.doi.org/10.18280/ts.380307.

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The effect of motion posture recognition hinges on the accurate description of motion postures with effective feature information. This study introduces Wronskian function to improve the denoising ability of visual background extractor (ViBe) algorithm, and thus acquires relatively clear motion targets. Then, a multi-feature fusion motion posture feature model was developed based on genetic algorithm (GA). Specifically, GA was called to optimize and fuse the extracted feature information, while a fitness function was constructed based on the mean variance ratio, and used to select the feature

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17

Luna-Perejón, Francisco, Juan Manuel Montes-Sánchez, Lourdes Durán-López, Alberto Vazquez-Baeza, Isabel Beasley-Bohórquez, and José L. Sevillano-Ramos. "IoT Device for Sitting Posture Classification Using Artificial Neural Networks." Electronics 10, no. 15 (2021): 1825. http://dx.doi.org/10.3390/electronics10151825.

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Nowadays, the percentage of time that the population spends sitting has increased substantially due to the use of computers as the main tool for work or leisure and the increase in jobs with a high office workload. As a consequence, it is common to suffer musculoskeletal pain, mainly in the back, which can lead to both temporary and chronic damage. This pain is related to holding a posture during a prolonged period of sitting, usually in front of a computer. This work presents a IoT posture monitoring system while sitting. The system consists of a device equipped with Force Sensitive Resistors

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18

Tang, Keison, Arjun Kumar, Muhammad Nadeem, and Issam Maaz. "CNN-Based Smart Sleep Posture Recognition System." IoT 2, no. 1 (2021): 119–39. http://dx.doi.org/10.3390/iot2010007.

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Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care faci

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19

Silanon, Kittasil. "Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9026375.

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Hand posture recognition is an essential module in applications such as human-computer interaction (HCI), games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG) feature (which is applied with more focus on the information within certain region of the image rather than each single pixel) and Adaptive Boost (i.e., AdaBoost) learning technique to select the best weak classifier

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20

Lee, Seulah, Yuna Choi, Minchang Sung, Jihyun Bae, and Youngjin Choi. "A Knitted Sensing Glove for Human Hand Postures Pattern Recognition." Sensors 21, no. 4 (2021): 1364. http://dx.doi.org/10.3390/s21041364.

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In recent years, flexible sensors for data gloves have been developed that aim to achieve excellent wearability, but they are associated with difficulties due to the complicated manufacturing and embedding into the glove. This study proposes a knitted glove integrated with strain sensors for pattern recognition of hand postures. The proposed sensing glove is fabricated at all once by a knitting technique without sewing and bonding, which is composed of strain sensors knitted with conductive yarn and a glove body with non-conductive yarn. To verify the performance of the developed glove, electr

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21

Li, Xin, Anzi Ding, Shaojie Mei, Wenjin Wu, and Wenguang Hou. "Convolutional Neural Network-Based Fish Posture Classification." Complexity 2021 (June 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/9939688.

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Fish killing machines can effectively relieve the workers from the backbreaking labour. Generally, it is necessary to ensure the fish to be in unified posture before being input into the automatic fish killing machine. As such, how to detect the actual posture of fish in real time is a new and meaningful issue. Considering that in the actual situation, we only need to determine the four postures which are related to the head, tail, back, and belly of the fish, and we transfer this task into a four-kind classification problem. As such, the convolutional neural network (CNN) is introduced here t

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Gou, Huan, Tengda Shi, Lei Yan, and Jiang Xiao. "Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data." Journal of Robotics and Mechatronics 28, no. 3 (2016): 418–24. http://dx.doi.org/10.20965/jrm.2016.p0418.

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[abstFig src='/00280003/18.jpg' width=""300"" text='The result of parameters optimization by GA' ] The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data proces

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Skach, Stewart, and Healey. "Smarty Pants: Exploring Textile Pressure Sensors in Trousers for Posture and Behaviour Classification." Proceedings 32, no. 1 (2019): 19. http://dx.doi.org/10.3390/proceedings2019032019.

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In this paper, we introduce a new modality for capturing body postures and social behaviour. Vice versa, we propose a new application area for on-body textile sensors. We have developed “smart trousers” with embedded textile pressure sensors that allow for classification of a large variety of postural movements as well as interactional states. Random Forest models are used to investigate those. Here, we give an overview of the research conducted and discuss potential use cases of the presented design.

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Speck, Giselle Mari, Cristhiane Guertler, Walter Quadros Seiffert, Lizandra Garcia Lupi Vergara, and Eugenio Andrés Díaz Merino. "Work Ergonomic Analysis: Application of a Postural Study on the Oysters Cultivation." Journal of Health Sciences 21, no. 1 (2019): 15. http://dx.doi.org/10.17921/2447-8938.2019v21n1p15-20.

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O cultivo de moluscos marinhos é uma atividade de grande importância no Brasil proporcionando a geração de emprego e renda para pescadores artesanais e comunidades pesqueiras, contribuindo para o desenvolvimento local. Entretanto, este tipo de cultivo ainda é realizado de forma bastante artesanal com intensa utilização de mão de obra. Diante disso, este estudo teve por objetivo realizar uma descrição das posturas e movimentos de maricultores durante a realização da atividade de classificação de ostras. Participaram voluntariamente dez funcionários de uma fazenda marinha no município de Florian

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Iazzi, Abderrazak, Mohammed Rziza, and Rachid Oulad Haj Thami. "Fall Detection System-Based Posture-Recognition for Indoor Environments." Journal of Imaging 7, no. 3 (2021): 42. http://dx.doi.org/10.3390/jimaging7030042.

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The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets fo

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Ding, Weili, Bo Hu, Han Liu, Xinming Wang, and Xiangsheng Huang. "Human posture recognition based on multiple features and rule learning." International Journal of Machine Learning and Cybernetics 11, no. 11 (2020): 2529–40. http://dx.doi.org/10.1007/s13042-020-01138-y.

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Abstract The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used toget

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Chezhiyan, Ponmozhi, and Deepalakshmi P. "Joint-angle-based yoga posture recognition for prevention of falls among older people." Data Technologies and Applications 53, no. 4 (2019): 528–45. http://dx.doi.org/10.1108/dta-03-2019-0041.

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Purpose United Nations’ World Population Ageing Report states that falls are one of the most common problems in the elderly around the world. Falls are a leading cause of morbidity and mortality among mature adults, and the second leading cause of accidental or unintentional injury/death after road traffic injuries. The rates are higher in hospitalized patients and nursing home residents. Major contributing reasons for falling are loss of footing or traction, balance problem in carpets and rugs, reduced muscle strength, poor vision, mobility/gait, cognitive impairment: in other words lack of b

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Mohd Said, Aisyah, Haidzir Manaf, Saiful Adli Bukry, and Maria Justine. "Mobility and Balance and Their Correlation with Physiological Factors in Elderly with Different Foot Postures." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/385269.

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This study determines (1) the correlation between mobility and balance performances with physiological factors and (2) the relationship between foot postures with anthropometric characteristics and lower limb characteristics among elderly with neutral, pronated, and supinated foot. A cross-sectional observational study was conducted in community-dwelling elderly (age: 69.86 ± 5.62 years). Participants were grouped into neutral (n=16), pronated (n=14), and supinated (n=14) foot based on the foot posture index classification. Anthropometric data (height, weight, and BMI), lower limb strength (5-

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Straczkiewicz, Marcin, Nancy W. Glynn, Vadim Zipunnikov, and Jaroslaw Harezlak. "Fast and Robust Algorithm for Detecting Body Posture Using Wrist-Worn Accelerometers." Journal for the Measurement of Physical Behaviour 3, no. 4 (2020): 285–93. http://dx.doi.org/10.1123/jmpb.2019-0067.

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Background: The increasing popularity of wrist-worn accelerometers introduces novel challenges to the research on physical activity and sedentary behavior. Estimation of body posture is one such challenge. Methods: The authors proposed an approach called SedUp to differentiate between sedentary (sitting/lying) and standing postures. SedUp is based on the logistic regression classifier, using the wrist elevation and the motion variability extracted from raw accelerometry data collected on the axis parallel to the forearm. The authors developed and tested our method on data from N = 45 community

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Kılıç, Alper, İsmail Babaoğlu, Ahmet Babalık, and Ahmet Arslan. "Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks." International Journal of Antennas and Propagation 2019 (March 31, 2019): 1–10. http://dx.doi.org/10.1155/2019/7541814.

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Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred

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Licciardo, Gian Domenico, Alessandro Russo, Alessandro Naddeo, et al. "A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems." Applied Sciences 11, no. 11 (2021): 4752. http://dx.doi.org/10.3390/app11114752.

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A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the

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Triesch, Jochen, and Christoph von der Malsburg. "Classification of hand postures against complex backgrounds using elastic graph matching." Image and Vision Computing 20, no. 13-14 (2002): 937–43. http://dx.doi.org/10.1016/s0262-8856(02)00100-2.

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Panakarn, Piyanan, Suphakant Phimoltares, and Chidchanok Lursinsap. "Identifying Sport Types and Postures with Complex Background by Fusion of Local Descriptors." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (2015): 1550008. http://dx.doi.org/10.1142/s0218001415500081.

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Sport type classification and posture identification based on visual meaning of posture semantic in still images are challenging tasks. The difficulty of these tasks comes from the complex image content consisting of a player's posture, the color and texture of a player's clothes as well as complexity of the background. Player detection is one of the most important tasks in posture identification. For sport type classification without object segmentation, the new set of features, based on 64-bins color histogram, DCT coefficients, and Cb and Cr components, is introduced. To achieve high accura

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Domínguez-Morales, Manuel J., Francisco Luna-Perejón, Lourdes Miró-Amarante, Mariló Hernández-Velázquez, and José L. Sevillano-Ramos. "Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks." Applied Sciences 9, no. 19 (2019): 3970. http://dx.doi.org/10.3390/app9193970.

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Abnormal foot postures during gait are common sources of pain and pathologies of the lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure measurement system is developed to identify areas with higher or lower pressure load. This system is composed of an embedded system placed in the insole and a user application. The instrumented insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth module. The user applicati

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Zhu, Yuhong, Jingchao Yu, Fengye Hu, Zhijun Li, and Zhuang Ling. "Human activity recognition via smart-belt in wireless body area networks." International Journal of Distributed Sensor Networks 15, no. 5 (2019): 155014771984935. http://dx.doi.org/10.1177/1550147719849357.

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Human activity recognition based on wireless body area networks plays an essential role in various applications such as health monitoring, rehabilitation, and physical training. Currently, most of the human activity recognition is based on smartphone, and it provides more possibilities for this task with the rapid proliferation of wearable devices. To obtain satisfactory accuracy and adapt to various scenarios, we built a smart-belt which embedded the VG350 as posture data collector. This article proposes a hierarchical activity recognition structure, which divides the recognition process into

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KUMAR, P. PRAMOD, PRAHLAD VADAKKEPAT, and AI POH LOH. "HAND POSTURE AND FACE RECOGNITION USING A FUZZY-ROUGH APPROACH." International Journal of Humanoid Robotics 07, no. 03 (2010): 331–56. http://dx.doi.org/10.1142/s0219843610002180.

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A novel algorithm based on fuzzy-rough sets is proposed for the recognition of hand postures and face. Features of the image are extracted using the computational model of the ventral stream of visual cortex. The recognition algorithm translates each quantitative value of the feature into fuzzy sets of linguistic terms using membership functions. The membership functions are formed by the fuzzy partitioning of the feature space into fuzzy equivalence classes, using the feature cluster centers generated by the subtractive clustering technique. A rule base generated from the lower and upper appr

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Wan, Qilong, Haiming Zhao, Jie Li, and Peng Xu. "Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image." Sensors 21, no. 2 (2021): 426. http://dx.doi.org/10.3390/s21020426.

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Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects.

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Wan, Qilong, Haiming Zhao, Jie Li, and Peng Xu. "Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image." Sensors 21, no. 2 (2021): 426. http://dx.doi.org/10.3390/s21020426.

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Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects.

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39

Greene, Runyu L., Yu Hen Hu, Nicholas Difranco, et al. "Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions." Human Factors: The Journal of the Human Factors and Ergonomics Society 61, no. 1 (2018): 64–77. http://dx.doi.org/10.1177/0018720818791367.

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Objective: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an “elastic” rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. Background: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were pre

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40

Sawaryn, Ben, Michel Klaassen, Bert-Jan van Beijnum, Hans Zwart, and Peter H. Veltink. "Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System." Sensors 21, no. 17 (2021): 5954. http://dx.doi.org/10.3390/s21175954.

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Background: Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to estimate metabolic energy consumption to correct hormone administration levels, considerable improvements to the system can be made. Therefore, this research aimed to investigate the possibility to use the current system to identify several postures and movements. Methods: seven healthy participants to

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Cai, Wenyu, Dongyang Zhao, Meiyan Zhang, Yinan Xu, and Zhu Li. "Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System." Sensors 21, no. 18 (2021): 6246. http://dx.doi.org/10.3390/s21186246.

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As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved

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42

Tang, Wenlong, and Edward S. Sazonov. "Highly Accurate Recognition of Human Postures and Activities Through Classification With Rejection." IEEE Journal of Biomedical and Health Informatics 18, no. 1 (2014): 309–15. http://dx.doi.org/10.1109/jbhi.2013.2287400.

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TAKANO, Wataru, and Haeyeon LEE. "Behavior Classification and Description Generation on 2D Human Postures in Care Facilities." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 1P1—L09. http://dx.doi.org/10.1299/jsmermd.2019.1p1-l09.

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Jitaree, Sirinapa, and Pornchai Phukpattaranont. "Force classification using surface electromyography from various object lengths and wrist postures." Signal, Image and Video Processing 13, no. 6 (2019): 1183–90. http://dx.doi.org/10.1007/s11760-019-01462-z.

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Zhao, Chihang, Bailing Zhang, and Jie He. "Vision-based Classification of Driving Postures by Efficient Feature Extraction and Bayesian Approach." Journal of Intelligent & Robotic Systems 72, no. 3-4 (2012): 483–95. http://dx.doi.org/10.1007/s10846-012-9797-z.

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Mannini, A., and A. M. Sabatini. "Computational methods for the automatic classification of postures and movements from acceleration data." Gait & Posture 30 (October 2009): S68—S69. http://dx.doi.org/10.1016/j.gaitpost.2009.07.068.

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47

Lee, Seulah, Minchang Sung, and Youngjin Choi. "Wearable fabric sensor for controlling myoelectric hand prosthesis via classification of foot postures." Smart Materials and Structures 29, no. 3 (2020): 035004. http://dx.doi.org/10.1088/1361-665x/ab6690.

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Goñi, A., A. Illarramendi, and D. Antón. "Exercise Recognition for Kinect-based Telerehabilitation." Methods of Information in Medicine 54, no. 02 (2015): 145–55. http://dx.doi.org/10.3414/me13-01-0109.

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SummaryBackground: An aging population and people’s higher survival to diseases and traumas that leave physical consequences are challenging aspects in the context of an efficient health management. This is why telerehabilitation systems are being developed, to allow monitoring and support of physiotherapy sessions at home, which could reduce healthcare costs while also improving the quality of life of the users.Objectives: Our goal is the development of a Kinect-based algorithm that provides a very accurate real-time monitoring of physical rehabilitation exercises and that also provides a fri

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49

Dhawal, Raj Singh, and Liang Chen. "A Copula Based Method for the Classification of Fish Species." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 1 (2017): 29–45. http://dx.doi.org/10.4018/ijcini.2017010103.

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The proposed work develops a method for classification of the species of a fish given in an image, which is a sub-ordinate level classification problem. Fish image categorization is unique and challenging as the images of same fish species can show significant differences in the fish's attributes when taken in different conditions. The authors' approach analyses the local patches of images, cropped based on specific body parts, and hence keep comparison more specific to grab more finer details rather than comparing global postures. The authors have used Histogram of Oriented Gradients and colo

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Shao, Yu, Xinyue Wang, Wenjie Song, Sobia Ilyas, Haibo Guo, and Wen-Shao Chang. "Feasibility of Using Floor Vibration to Detect Human Falls." International Journal of Environmental Research and Public Health 18, no. 1 (2020): 200. http://dx.doi.org/10.3390/ijerph18010200.

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With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the v

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