An Intelligent Shoe-Integrated System for Plantar Pressure Measurement

Chen, Meng; Huang, Bufu; Lee, Ka Keung; Xu, Yangsheng

doi:10.1109/robio.2006.340213

Cited by 31 publications

(10 citation statements)

References 9 publications

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“…where the term T ω ω is called the regularization term and 1 N i i C ξ = ∑ is empirical tolerance [34].…”

Section: Gait-classification Modelmentioning

confidence: 99%

Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

Chen

Wang

et al. 2021

Preprint

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Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

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“…where the term T ω ω is called the regularization term and 1 N i i C ξ = ∑ is empirical tolerance [34].…”

Section: Gait-classification Modelmentioning

confidence: 99%

Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

Chen

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Fig.4 pressure on the sensors [20] As shown in Fig.4, , and separately represents the distance of each sensor from the center of platform, ij F (i=1, 2, 3, 4,j=x, y, z) is component of GRF in the axis j measured by the sensor i [9], so we can write:…”

Section: Fig 2 Cartesian Systemmentioning

confidence: 99%

“…There are two kinds of gait analysis system, one is stereophotogrammetric system, it observes the gesture of walking, such as [1]- [4], and the other is plantar pressure system, it measures plantar pressure, such as [5]- [9]. This paper will focus on the latter one.…”

Section: Introductionmentioning

confidence: 99%

A novel platform system for gait analysis

Zhou

Sun

2008

2008 Conference on Human System Interactions

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A novel platform system for gait analysis was developed, with its two main parts: force platform and pressure platform, it can offer complete components of pressures in walking, including vertical pressure, shear forces, pressure distribution and COP (centre of pressure) with reliable result. System configuration was introduced as an important part, and gait model was established. Some examples of application were also reported.

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“…The various factors of human physiology, diseases, etc. will affect the person's gait, it studies the body's normal plantar pressure when walking, that have important reference value for researching gait analysis, medical rehabilitation, clinical and smart shoes, and so on [1] .…”

Section: Introductionmentioning

confidence: 99%

Research on Normal Human Plantar Pressure Test

Liu

Gao

Feng

et al. 2016

MATEC Web of Conferences

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FSR400 pressure sensor, nRF905 wireless transceiver and MSP40 SCM are used to design the insole pressure collection system, LabVIEW is used to make HMI of data acquisition, collecting a certain amount of normal human foot pressure data, statistical analysis of pressure distribution relations about five stages of swing phase during walking, using the grid closeness degree to identify plantar pressure distribution pattern recognition, and the algorithm simulation, experimental results demonstrated this method feasible.

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An Intelligent Shoe-Integrated System for Plantar Pressure Measurement

Cited by 31 publications

References 9 publications

Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

A novel platform system for gait analysis

Research on Normal Human Plantar Pressure Test

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