A portable sitting posture monitoring system based on a pressure sensor array and machine learning

Xu, Ran; Wang, Cong; Xiao, Yao; Gao, Xuliang; Zhu, Zhiyuan; Chen, Bin

doi:10.1016/j.sna.2021.112900

Cited by 42 publications

(22 citation statements)

References 31 publications

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“…Consequently, to improve recognition accuracy and minimize external interference, this study adopted a simulated scenario of an individual seated in a chair by fixing the artificial spine bottom on the table. [ 62 ] The dynamic tests involved bending and twisting of the spine at various degrees were conducted to distinguish between the CM at the vertebral plane and IS in the vertical plane.…”

Section: Resultsmentioning

confidence: 99%

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe₃O₄ particles

Li,

Chen,

et al. 2024

Interdisciplinary Materials

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Monitoring the position of orthopedic implants in vivo is paramount for enhancing postoperative rehabilitation. Traditional radiographic methods, although effective, pose inconveniences to patients in terms of specialized equipment requirements and delays in rehabilitation adjustment. Here, a nonradiographic design concept for real‐time and precisely monitoring the position of in vivo orthopedic implants is presented. The monitoring system encompasses an external magnetic field, a three‐dimensional (3D)‐printed superparamagnetic intervertebral body fusion cage (SIBFC), and a magnetometer. The SIBFC with a polyetheretherketone framework and a superparamagnetic Fe3O4 component was integrally fabricated by the high‐temperature selective laser sintering technology. Owing to the superparamagnetic component, the minor migration of SIBFC within the spine would cause the distribution change of the magnetic induction intensities, which can be monitored in real‐time by the magnetometer no matter in the static states or dynamic bending motions. Besides horizontal migration, occurrences of intervertebral subsidence in the vertical plane of the vertebrae can also be effectively distinguished based on the obtained characteristic variations of magnetic induction intensities. This strategy exemplifies the potential of superparamagnetic Fe3O4 particles in equipping 3D‐printed orthopedic implants with wireless monitoring capabilities, holding promise for aiding patients' rehabilitation.

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Section: Resultsmentioning

confidence: 99%

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe₃O₄ particles

Li,

Chen,

et al. 2024

Interdisciplinary Materials

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“…Sensor-based recognition methods are less costly and simple to operate but are limited to devices and require the real-time wearing of sensors [ 17 , 145 ].…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…To help understand more clearly, we created a table of abbreviations and corresponding full names for posture recognition terms as follows (Table 3): Sensor-based recognition methods are less costly and simple to operate but are limited to devices and require the real-time wearing of sensors [17,145].…”

Section: Referencesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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“…In this work two wearable devices positioned on the chest and on the right thigh were used. In [ 22 ] seven types of sitting postures with a pressure sensor array are processed on a Raspberry Pi using seven ML algorithms for comparation, showing that a five-layer Artificial Neural Network achieves the highest accuracy of about 97%.…”

Section: Introductionmentioning

confidence: 99%

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

Leone

Rescio

Caroppo

et al. 2023

Sensors

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Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.

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A portable sitting posture monitoring system based on a pressure sensor array and machine learning

Cited by 42 publications

References 31 publications

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe₃O₄ particles

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe₃O₄ particles

A Survey on Artificial Intelligence in Posture Recognition

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

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A portable sitting posture monitoring system based on a pressure sensor array and machine learning

Cited by 42 publications

References 31 publications

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe3O4 particles

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe3O4 particles

A Survey on Artificial Intelligence in Posture Recognition

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

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Product

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A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe₃O₄ particles

A nonradiographic strategy to real‐time monitor the position of three‐dimensional‐printed medical orthopedic implants by embedding superparamagnetic Fe₃O₄ particles