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

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.

Recognition of Gait Patterns Using Support Vector Machines

2011 ◽  
pp. 243-262 ◽  
Author(s):  
Rezaul Begg ◽  
Marimuthu Palaniswami
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Gait Pattern ◽  
Tool Support ◽  
Support Vector ◽  
Gait Patterns ◽  
Gait Characteristics ◽  
Vector Machines ◽  
Recognition Technique ◽  
Recognition Capability

Automated gait pattern recognition capability has many advantages. For example, it can be used for the detection of at-risk or faulty gait, or for monitoring the progress of treatment effects. In this chapter, we first provide an overview of the major automated techniques for detecting gait patterns. This is followed by a description of a gait pattern recognition technique based on a relatively new machine-learning tool, support vector machines (SVM). Finally, we show how SVM technique can be applied to detect changes in the gait characteristics as a result of the ageing process and discuss their suitability as an automated gait classifier.

scholarly journals Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002683 ◽  
Author(s):  
John R. Zech ◽  
Marcus A. Badgeley ◽  
Manway Liu ◽  
Anthony B. Costa ◽  
Joseph J. Titano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Cross Sectional Study ◽  
Chest Radiographs ◽  
Sectional Study ◽  
Cross Sectional ◽  
Generalization Performance ◽  
Deep Learning Model

scholarly journals Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

2021 ◽  
Vol 2 ◽  
Author(s):  
Anderson Antonio Carvalho Alves ◽  
Lucas Tassoni Andrietta ◽  
Rafael Zinni Lopes ◽  
Fernando Oliveira Bussiman ◽  
Fabyano Fonseca e Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Deep Learning ◽  
Audio Signal ◽  
Gait Pattern ◽  
Classification Performance ◽  
Audio Signal Processing ◽  
Gait Patterns ◽  
Audio Features ◽  
Gaited Horses

This study focused on assessing the usefulness of using audio signal processing in the gaited horse industry. A total of 196 short-time audio files (4 s) were collected from video recordings of Brazilian gaited horses. These files were converted into waveform signals (196 samples by 80,000 columns) and divided into training (N = 164) and validation (N = 32) datasets. Twelve single-valued audio features were initially extracted to summarize the training data according to the gait patterns (Marcha Batida—MB and Marcha Picada—MP). After preliminary analyses, high-dimensional arrays of the Mel Frequency Cepstral Coefficients (MFCC), Onset Strength (OS), and Tempogram (TEMP) were extracted and used as input information in the classification algorithms. A principal component analysis (PCA) was performed using the 12 single-valued features set and each audio-feature dataset—AFD (MFCC, OS, and TEMP) for prior data visualization. Machine learning (random forest, RF; support vector machine, SVM) and deep learning (multilayer perceptron neural networks, MLP; convolution neural networks, CNN) algorithms were used to classify the gait types. A five-fold cross-validation scheme with 10 repetitions was employed for assessing the models' predictive performance. The classification performance across models and AFD was also validated with independent observations. The models and AFD were compared based on the classification accuracy (ACC), specificity (SPEC), sensitivity (SEN), and area under the curve (AUC). In the logistic regression analysis, five out of the 12 audio features extracted were significant (p < 0.05) between the gait types. ACC averages ranged from 0.806 to 0.932 for MFCC, from 0.758 to 0.948 for OS and, from 0.936 to 0.968 for TEMP. Overall, the TEMP dataset provided the best classification accuracies for all models. The most suitable method for audio-based horse gait pattern classification was CNN. Both cross and independent validation schemes confirmed that high values of ACC, SPEC, SEN, and AUC are expected for yet-to-be-observed labels, except for MFCC-based models, in which clear overfitting was observed. Using audio-generated data for describing gait phenotypes in Brazilian horses is a promising approach, as the two gait patterns were correctly distinguished. The highest classification performance was achieved by combining CNN and the rhythmic-descriptive AFD.

The Differences in Gait Pattern Between Dancers and Non-Dancers

2008 ◽  
Vol 24 (4) ◽  
pp. 451-457 ◽  
Author(s):  
C. -W. Lung ◽  
J. -S. Chern ◽  
L. -F. Hsieh ◽  
S. -W. Yang
Keyword(s):  
Ankle Sprain ◽  
Healthy Subjects ◽  
Center Of Pressure ◽  
Reaction Force ◽  
High Stress ◽  
Gait Pattern ◽  
Swing Phase ◽  
Gait Patterns ◽  
Gait Characteristics ◽  
Walking Pattern

AbstractStudents in dancing department routinely perform hours of dancing every day. Extreme ankle posture can subject the ankle of the dancers to high stress and can significantly increase the mobility of the ankle. This causes ankle sprain which occurs frequently during daily walking. Measurements of the ground reaction force (GRF) and the center of pressure (CoP) provide useful variables to analyze the walking patterns of dancers, which might help understand the causes of ankle sprain. The aims of this work were (1) to investigate the differences in gait patterns between dancers and non-dancers and (2) to explore the gait characteristics in dancers. Thirteen students in dancing department and twenty age-matched normal healthy subjects were recruited. All subjects were requested to walk along a 10-meter walkway. Results showed that the dancers have greater medial shear force of the GRF, and decreased the CoP velocity during the pre-swing phase, delayed peak-CoP velocity occurrence during the mid-stance, and straighter CoP trajectory through the forefoot at push off. The intense and demanding dancing activities change the walking pattern of dancers, which may lead to higher chance of getting ankle sprain.

Muscle Contracture Emulating System for Studying Artificially Induced Pathological Gait in Intact Individuals

2005 ◽  
Vol 21 (4) ◽  
pp. 348-358 ◽  
Author(s):  
Andrej Olenšek ◽  
Zlatko Matjačić ◽  
Tadej Bajd
Keyword(s):  
Mechanical System ◽  
Gait Pattern ◽  
Gait Variability ◽  
Spastic Diplegia ◽  
Gait Patterns ◽  
Gait Kinematics ◽  
Novel Method ◽  
Pathological Gait ◽  
Analysis System ◽  
Kinematics And Kinetics

When studying pathological gait it is important to correctly identify primary gait anomalies originating from damage to the central nervous and musculoskeletal system and separate them from compensatory changes of gait pattern, which is often challenging due to the lack of knowledge related to biomechanics of pathological gait. A mechanical system consisting of specially designed trousers, special shoe arrangement, and elastic ropes attached to selected locations on the trousers and shoes is proposed to allow emulation of muscle contractures of soleus (SOL) and gastrocnemius (GAS) muscles and both SOL-GAS. The main objective of this study was to evaluate and compare gait variability as recorded in normal gait and when being constrained with the proposed system. Six neurologically and orthopedically intact volunteers walked along a 7-m walkway while gait kinematics and kinetics were recorded using VICON motion analysis system and two AMTI forceplates. Statistical analysis of coefficient of variation of kinematics and kinetics as recorded in normal walking and during the most constrained SOL-GAS condition showed comparable gait variability. Inspection of resulting group averaged gait patterns revealed considerable resemblance to a selected clinical example of spastic diplegia, indicating that the proposed mechanical system potentially represents a novel method for studying emulated pathological gait arising from artificially induced muscle contractures in neurologically intact individuals.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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