Intelligent System for Soccer Gait Pattern Recognition

It is difficult to combine human sensory cognition with quality detection to form a pattern recognition system based on human perception. In the future, miniature stepper motor modules will be widely used in advanced intelligent equipment. However, the reducer module based on powder metallurgy parts and the stepper motor may have various defects during operation, with varying definitions of those that affect the user comfort. It is tremendously important to develop an intelligent system to effectively simulate human senses. In this work, an elaborated personification of the perceptual system is proposed to simulate the ventral and flow of the human perception system: two branch systems consisting of a spatiotemporal convolutional neural network (S-CNN) and a concatenated HoppingNet temporal convolutional neural network (T-CNN). To ensure high robustness of the system, we combined principal component analysis (PCA) with the opinions of an experienced quality control (QC) team members to screen the data, and used a bionic ear to simulate human perception characteristics. After repeated comparisons of the tester, the results show that our anthropoid pattern sensing system has high accuracy and robustness for a stepper motor module.

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A close to Real-Time Intelligent System for Enumerating Total Viable Bacteria Based on Pattern Recognition Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.741.344 ◽

2015 ◽

Vol 741 ◽

pp. 344-349

Author(s):

Yun Ding ◽

Yong Guang Yin

Keyword(s):

Pattern Recognition ◽

Drinking Water ◽

Real Time ◽

Intelligent System ◽

Plate Count ◽

Accurate Information ◽

Water Safety ◽

Drinking Water Safety ◽

Viable Bacteria ◽

Aerobic Plate Count

In this paper, a novel close to real-time artificial intelligent system for enumerating Total Viable Bacteria (TVB) in drinking water was developed by using pattern recognition and machine vision technology. In order to identify the viable bacteria accurately, four shape features including circularity ratio, eccentricity, rectangularity, and compact degree, and four color features (GRsd, BRsd, HRsd, SRsd) of the stained viable bacteria image were extracted. An optimal artificial neural network was used as the bacterial recognition classifier, whose inputs were the extracted feature parameters and output was bacteria signal or non-bacteria signal. By using this intelligent system, TVB counts in each sample can be enumerated within 1 h, but the traditional Aerobic Plate Count (APC) method will take us 48 h. The comparative test also indicated that the counting results by two methods are closely correlated (R2=0.9942). This close to real-time accurate information may contribute to melioration and instauration of drinking water safety systems and risk management for TVB.

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An Advanced Attention-Enhanced Hybrid Deep Learning Model for WBSNs’ Gait Pattern Recognition

10.21203/rs.3.rs-1251973/v1 ◽

2022 ◽

Author(s):

Jianning Wu ◽

Qiaoling Tan ◽

Xiaoyan Wu

Keyword(s):

Pattern Recognition ◽

Deep Learning ◽

Nonlinear Dynamic ◽

Gait Pattern ◽

Gait Variability ◽

Gait Patterns ◽

Generalization Performance ◽

Dynamic Correlation ◽

Gait Characteristics ◽

Deep Learning Model

Abstract Background: The deep learning techniques have been attracted increasing attention on wireless body sensor networks (WBSNs) gait pattern recognition that has a great contribution to monitoring gait change in clinical application. However, in existing studies, there are some challenging issues such as low generalization performance and no potential interpretation for gait variability. It is necessary to search for the advanced deep learning models to resolve these issues. Method: A public WARD database including acceleration and gyroscope data acquired from each subject wearing five sensors was selected, and the gait with different combination of on-body multi-sensors is considered as a WBSNs’ gait pattern. An advanced attention-enhanced hybrid deep learning model of DCNN and LSTM for WBSNs’ gait pattern recognition was proposed. In our proposed technique, the combination model of DCNN with LSTM is firstly to discover the spatial-temporary gait correlation features. And then the attention mechanism is introduced to exploit the more valuable intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability hidden in spatial-temporary gait space obtained. This significantly contributes to enhancing the generalization performance and taking insight on gait variability in a certain anatomical region. Results: The ten gait patterns are randomly selected from WARD database to evaluate the feasibility of our proposed method. Our experiments demonstrated the superior generalization ability of our method to some models such as CNN-LSTM, DCNN-LSTM. Our proposed model could classify ten gait patterns with the highest accuracy and F1-score of 91.48% and 91.46%, respectively. Moreover, we also found that the classification performance of a certain gait pattern was almost same best when the combinations of three or five on-body sensors were employed respectively, suggesting that our method possibly take insight on gait variability in a certain anatomical region. Conclusion: Our proposed technique could feasibly discover the more intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability from on-body multi-sensors gait data, which greatly contributed to best generalization performance and potential clinical interpretation. Our proposed technique would hopefully become a powerful tool of monitoring gait change in clinical application.

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