A Smart Chair Sitting Posture Recognition System Using Flex Sensors and FPGA Implemented Artificial Neural Network

Hu, Qisong; Tang, Xiaochen; Tang, Wei

doi:10.1109/jsen.2020.2980207

Cited by 54 publications

(33 citation statements)

References 33 publications

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“…Moreover, custom HW implementations are tailored for a specific application or for a specific support, obviously, and require less physical resources than are proposed. This happens, for example, with the sitting posture recognition in [27], specifically designed to use six flexible sensors applied at the armrests, backrest and seat of a chair, in conjunction with a very compact two-layer Artificial NN (ANN) to classify seven sitting positions, representing the state-of-the-art solution for this specific problem. However, although the Artificial NN in [27] requires less physical resources than the FCN, (755 slice reg., 1822 FFs and 649 LUTs), it consumes more power (7.33 mW) with a much higher processing time of 267.5 µs to classify one posture less than the proposed one with an average accuracy of 97.43%.…”

Section: Comparison With the Literaturementioning

confidence: 99%

“…The same approach [25] can be used in the case of the sitting posture since contact pressure is the only way to support body weight and influence posture and comfort [26]. However, recent works have demonstrated that the accuracy of such HPR systems strongly depends on the careful distribution of the sensors in specific key-points of the chair, bed or any other kind of support equipped with the system, in order to acquire data from as many body parts as possible and avoid an excessive number of sensors [27]. Therefore, the reliability of pressure-based HPR systems appears much too dependent on the shapes of the specific supports.…”

Section: Introductionmentioning

confidence: 99%

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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: Comparison With the Literaturementioning

confidence: 99%

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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“…19 Hu et al established an ANN network-based posture classification model by placing six flexible sensors under an office chair, which was implemented on a Spartan6 programmable gate array, and experiments showed that the model floating-point evaluation accuracy is 97.78% and the maximum propagation delay is 8.714 ns, which is highly applicable. 20 Sazonov's team used wearable sensors to build an SVM-based framework for SVM and polynomial logic recognition, and used the labeled data for PAC/EE algorithm training, using fast artificial neural networks (FANNs) to train the MLDs model, the model has been experimentally demonstrated to be effective in reducing execution time for real-time biofeedback systems. 21 In 2016, Xu et al identified sleep posture through pressure-sensitive sensor sheets, using the moving distance to use the pressure-sensitive image as a weighted 2D image, combined with earth mover's distance and Euclidean metric for similarity measurement, and experimentally demonstrated that the model improved accuracy by 8.01% compared with traditional sleep posture recognition methods.…”

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

“…Due to the low‐power, low‐cost, small size, and high‐performance characteristics of sensors, the field of sensor‐based recognition is more integrated with the application field 19 . Hu et al established an ANN network‐based posture classification model by placing six flexible sensors under an office chair, which was implemented on a Spartan6 programmable gate array, and experiments showed that the model floating‐point evaluation accuracy is 97.78% and the maximum propagation delay is 8.714 ns, which is highly applicable 20 . Sazonov's team used wearable sensors to build an SVM‐based framework for SVM and polynomial logic recognition, and used the labeled data for PAC/EE algorithm training, using fast artificial neural networks (FANNs) to train the MLDs model, the model has been experimentally demonstrated to be effective in reducing execution time for real‐time biofeedback systems 21 .…”

Section: Related Workmentioning

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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“…At present, there are three main ways of sitting posture recognition, which are based on machine vision [ 2 , 3 , 4 , 5 ], wearable motion sensors [ 6 , 7 , 8 , 9 ] and external pressure sensors [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Although machine vision technology has achieved great success in the field of posture recognition [ 26 ], it is difficult to work normally in situations with many obstacles.…”

Section: Introductionmentioning

confidence: 99%

Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image

Wan

Zhao

et al. 2021

Sensors

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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. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°–15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°–30°) or a large rotation angle (30°–45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

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A Smart Chair Sitting Posture Recognition System Using Flex Sensors and FPGA Implemented Artificial Neural Network

Cited by 54 publications

References 33 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

Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image

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