Human Posture Recognition Using a Hybrid of Fuzzy Logic and Machine Learning Approaches

Ren, Weiyan; Ma, Ou; Ji, Hongxin; Liu, Xinyuan

doi:10.1109/access.2020.3011697

Cited by 52 publications

(26 citation statements)

References 40 publications

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“…Human Posture Recognition (HPR) has been recently implemented by using Machine Learning (ML) approaches [14,15] in conjunction with mechanical or image sensors [16][17][18]. In particular, recent papers deal with use of machine learning and AI for posture tracking and recognition in order to improve car drivers' safety and car-occupants' comfort.…”

Section: Introductionmentioning

confidence: 99%

A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

et al. 2021

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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 computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 mW and 7 mW for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 MHz and 26.6 MHz, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 kHz and 9.13 kHz, respectively. Furthermore, synthesis results with a CMOS 130 nm technology have been reported, to give an estimation about the possibility of an in-sensor circuital implementation.

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Section: Introductionmentioning

confidence: 99%

A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

et al. 2021

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“…15 In 2020, Ren et al's team improved the posture recognition algorithm of autonomous assistive robots for patients requiring care by combining fuzzy logic and SVM algorithms via Kinect, and experimental results showed a 97.1% accuracy in recognizing full-body lying posture data for 32 test subjects. 16 Takano and Haeyeon used the same 2D data for posture recognition methods to improve motion. Identifying the HMM model and generating textual descriptive information from image categorized observations, establishing a probabilistic framework using words linked to motor primitives to enable accurate grammatical alignment, have been shown to be effective in improving the effectiveness of taking action in geriatric care.…”

Section: Image-based Gesture Recognitionmentioning

confidence: 99%

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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“…According to the Oxford Dictionary, the posture of the human body is a special posture of the body and the way a person maintains his physical state [24]. Human posture recognition is the extraction, classification, and identification of human posture features and natural language description.…”

Section: Human Motion Gesture Recognitionmentioning

confidence: 99%

Human Motion Gesture Recognition Based on Computer Vision

Zhang

Chen³

2021

Complexity

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Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods.

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Human Posture Recognition Using a Hybrid of Fuzzy Logic and Machine Learning Approaches

Cited by 52 publications

References 40 publications

A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

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

Human Motion Gesture Recognition Based on Computer Vision

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