A Posture Recognition Model Dedicated for Differentiating between Proper and Improper Sitting Posture with Kinect Sensor

Chin, Lck.; Eu, Kok Seng; Tay, Tee Tiong; Teoh, Choe-Yung; Yap, Kian Meng

doi:10.1109/have.2019.8920964

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…Another approach focuses on wiring sensors directly to the human body to acquire data, although it limits the freedom of movement for work activities ( Arnold et al, 2020 ). Despite these achievements, it is still quite difficult to recognize posture in real-time or correctly identify transitional activities in real-world environments ( Nweke et al, 2019 ) as the recognition of fine-grained activities such as correct or incorrect cases of sitting postures is still a problem ( Chin et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

Detection of sitting posture using hierarchical image composition and deep learning

Kulikajevas

Maskeliūnas

Damaševičius

2021

PeerJ Computer Science

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Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem. The DRHN network accepts the RGB-Depth frame sequences and produces a representation of semantically related posture states. We achieved 91.47% accuracy at 10 fps rate for sitting posture recognition.

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Section: Related Workmentioning

confidence: 99%

Detection of sitting posture using hierarchical image composition and deep learning

Kulikajevas

Maskeliūnas

Damaševičius

2021

PeerJ Computer Science

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“…11 Chin et al collected data sets of office sitting postures through Kinect and designed a posture recognition model based on SVM and artificial neural network (ANN), comparing and finding that linear SVM linearly nuclei have the highest accuracy. 12 Yu et al obtained human features in image sequences by cosine discrete transform before truncating the singular value decomposition (SVD), with improved classification speed and a 30% increase in accuracy. 13 In 2019, Kamel et al designed an action-fusion-based model from depth map and pose data to recognize human movements, continuous depth maps with moving joint descriptors were used as input using three-channel Convolutional Neural Networks (CNNs), and later training with two depth motion images (DMIs) to achieve the final movement classification, which experimentally demonstrated the efficiency of this method.…”

Section: Image-based Gesture Recognitionmentioning

confidence: 99%

“…The fully connected neural network parameters are updated according to Equations ( 11) and (12). Whenever a batch of data completes forward propagation, the weights and bias parameters are updated using backward propagation, and the process is repeated until the loss value is less than the set threshold or the network update reaches the number of iterations to stop the BP update and obtain the output value, which is output to the activation function for result normalization (Figure 3).…”

Section: Forward Propagationmentioning

confidence: 99%

“…In 2018, Munoz et al designed a Kinect‐based multimodal learning analysis model as AdaBoost, for behavior recognition by detecting students' learning state through body and gesture postures 11 . Chin et al collected data sets of office sitting postures through Kinect and designed a posture recognition model based on SVM and artificial neural network (ANN), comparing and finding that linear SVM linearly nuclei have the highest accuracy 12 . Yu et al obtained human features in image sequences by cosine discrete transform before truncating the singular value decomposition (SVD), with improved classification speed and a 30% increase in accuracy 13 .…”

Section: Related Workmentioning

confidence: 99%

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A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

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“…In most existing work, user’s sitting posture is recognized using videos or wearable sensors. Video-based recognition method 12–19 uses the image recorded by the camera to detect sitting posture. Although they have higher accuracy, they have obvious drawbacks including leakage of privacy and requirement of light.…”

Section: Introductionmentioning

confidence: 99%

Sitsen: Passive sitting posture sensing based on wireless devices

Jiang

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

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A Posture Recognition Model Dedicated for Differentiating between Proper and Improper Sitting Posture with Kinect Sensor

Cited by 18 publications

References 12 publications

Detection of sitting posture using hierarchical image composition and deep learning

Detection of sitting posture using hierarchical image composition and deep learning

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Sitsen: Passive sitting posture sensing based on wireless devices

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