Sitsen: Passive sitting posture sensing based on wireless devices

Li, Miaoyu; Jiang, Zhuohan; Liu, Yutong; Chen, Shu‐Heng; Woźniak, Marcin; Scherer, Rafał; Damaševičius, Robertas; Wei, Wei; Li, Ziyi; Li, Zuxin

doi:10.1177/15501477211024846

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…However, vision-based methods for posture recognition are limited by field of view constraints, interference and occlusion, sensitivity to lighting conditions, and motion artifacts, in addition to many issues relating to privacy invasion and user trust, which hinder widespread deployment. Wireless methods such as radio frequency identification (RFID) have also been used to detect passive sitting postures, but remain as proof-of-concept solutions prone to inaccuracies in real-world scenarios, in addition to raising important privacy challenges [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

Bourahmoune

Ishac

Amagasa

2022

Sensors

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We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system’s performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method’s performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.

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Section: Related Workmentioning

confidence: 99%

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

Bourahmoune

Ishac

Amagasa

2022

Sensors

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“…These techniques are used to process signals collected from ambient and wearable sensors (motion, proximity, microphone, video sensors, accelerometers, Body Sensor Networks, gyroscopes). The technique has been proposed to detect human activities [45], detect falls [46], recognize person's gait [47], predict body pose [48,49], classify gestures [50] and extract information on movement during interactive games and exercises [38]. Such systems may find applications in intelligent home healthcare systems and assisted living environments [51] and can monitor patients by diagnosing their health and controlling their drug intake.…”

Section: Research Literaturementioning

confidence: 99%

An Automated Recognition of Work Activity in Industrial Manufacturing Using Convolutional Neural Networks

2021

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The automated assessment and analysis of employee activity in a manufacturing enterprise, operating in accordance with the concept of Industry 4.0, is essential for a quick and precise diagnosis of work quality, especially in the process of training a new employee. In the case of industrial solutions, many approaches involving the recognition and detection of work activity are based on Convolutional Neural Networks (CNNs). Despite the wide use of CNNs, it is difficult to find solutions supporting the automated checking of work activities performed by trained employees. We propose a novel framework for the automatic generation of workplace instructions and real-time recognition of worker activities. The proposed method integrates CNN, CNN Support Vector Machine (SVM), CNN Region-Based CNN (Yolov3 Tiny) for recognizing and checking the completed work tasks. First, video recordings of the work process are analyzed and reference video frames corresponding to work activity stages are determined. Next, work-related features and objects are determined using CNN with SVM (achieving 94% accuracy) and Yolov3 Tiny network based on the characteristics of the reference frames. Additionally, matching matrix between the reference frames and the test frames using mean absolute error (MAE) as a measure of errors between paired observations was built. Finally, the practical usefulness of the proposed approach by applying the method for supporting the automatic training of new employees and checking the correctness of their work done on solid fuel boiler equipment in a manufacturing company was demonstrated. The developed information system can be integrated with other Industry 4.0 technologies introduced within an enterprise.

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“…Recognition and detection of human poses are very widely used in neural networks, as they have excellent accuracy and effectiveness on larger datasets [34,35]. However, there is a limitation in the DNN model since the minute intersections or joints of a canine feature detection are very confused to detect the pose.…”

Section: Feature Extractionmentioning

confidence: 99%

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

2021

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The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.

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Sitsen: Passive sitting posture sensing based on wireless devices

Cited by 16 publications

References 25 publications

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

An Automated Recognition of Work Activity in Industrial Manufacturing Using Convolutional Neural Networks

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

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