scholarly journals Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qi Nie ◽  
Yun Li ◽  
Wen Ying Xiong ◽  
Wei Xu
Keyword(s):  
Health Status ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Learning Framework ◽  
Medical Environment ◽  
Acceleration Sensors ◽  
Physical Examinations ◽  
Output Information ◽  
Status Recognition ◽  
Gated Recurrent Unit

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

scholarly journals End-to-end recognition of streaming Japanese speech using CTC and local attention

2020 ◽  
Vol 9 ◽  
Author(s):  
Jiahao Chen ◽  
Ryota Nishimura ◽  
Norihide Kitaoka
Keyword(s):  
Speech Recognition ◽  
Recognition Performance ◽  
Time Lag ◽  
Recognition Algorithm ◽  
Streaming Data ◽  
Continuous Speech Recognition ◽  
Voice Input ◽  
Sequence Modeling ◽  
End To End ◽  
Bidirectional Networks

Many end-to-end, large vocabulary, continuous speech recognition systems are now able to achieve better speech recognition performance than conventional systems. Most of these approaches are based on bidirectional networks and sequence-to-sequence modeling however, so automatic speech recognition (ASR) systems using such techniques need to wait for an entire segment of voice input to be entered before they can begin processing the data, resulting in a lengthy time-lag, which can be a serious drawback in some applications. An obvious solution to this problem is to develop a speech recognition algorithm capable of processing streaming data. Therefore, in this paper we explore the possibility of a streaming, online, ASR system for Japanese using a model based on unidirectional LSTMs trained using connectionist temporal classification (CTC) criteria, with local attention. Such an approach has not been well investigated for use with Japanese, as most Japanese-language ASR systems employ bidirectional networks. The best result for our proposed system during experimental evaluation was a character error rate of 9.87%.

scholarly journals On the Robustness of Face Recognition Algorithms Against Attacks and Bias

2020 ◽  
Vol 34 (09) ◽  
pp. 13583-13589
Author(s):  
Richa Singh ◽  
Akshay Agarwal ◽  
Maneet Singh ◽  
Shruti Nagpal ◽  
Mayank Vatsa
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Future Research ◽  
Research Directions ◽  
Recognition Algorithms ◽  
Future Research Directions ◽  
And Gender ◽  
Gender Variations ◽  
Very High

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.

Coal Dust Recognition Based on Concave Point Extraction and Ellipse Fitting

2014 ◽  
Vol 654 ◽  
pp. 296-299 ◽  
Author(s):  
Wei Zhang ◽  
Hong Bo Yi ◽  
Xiao Wen Wang
Keyword(s):  
Recognition Performance ◽  
Coal Dust ◽  
Global Optimum ◽  
Recognition Algorithm ◽  
Least Square ◽  
Dust Particles ◽  
Ellipse Fitting ◽  
Particle Recognition ◽  
Fitting In ◽  
Bare Bones

A new coal dust particle recognition algorithm based on concave points extraction and ellipse fitting is proposed for the features of irregularities and particle overlap. The new algorithm includes contour processing and ellipse fitting in this paper. In the part of contour processing, the feature points are obtained with polygonal approximation on the edge of a binary dust particles image, and then concave points of overlapping particles are extracted by the method of angle combined with size, finally the edge is segmented by concave points. To solve the problem that direct least square ellipse fitting is easily affected by noise points, bare bones particle swarm optimization is introduced to find global optimum fitting parameters and the segmented edge is ellipse fitted. Experiment results show this proposed algorithm obtains better recognition performance.

scholarly journals A Recognition Algorithm of VGPO Jamming Based on Discrete Chirp-Fourier Transform

2020 ◽  
Author(s):  
Chuanzhang Wu ◽  
Baixiao Chen
Keyword(s):  
Fourier Transform ◽  
Recognition Performance ◽  
Tracking System ◽  
Recognition Algorithm ◽  
Chirp Rate ◽  
Echo Signal ◽  
The Mean ◽  
Analytical Result ◽  
The Relationship ◽  
Scaling Characteristic

Abstract We address the recognition problem of velocity gate pull-off (VGPO) jamming from the target echo signal for the velocity automatic tracking system. To this end, we resort to the discrete chirp-Fourier transform (DCFT) to jointly estimate the chirp rates and frequencies of the target and jamming signals. Firstly, the scaling characteristic of the DCFT algorithm is explored. Then we highlight the quantitative effect of the VGPO jamming signal by analyzing the jointly estimated result in each pulse. The effective effect indicates that the relationship between the estimated chirp rate and the pulse is similar to that between the frequency offset of VGPO jamming and the time when the estimated frequency is unchanged. Finally, by utilizing the analytical result and extracting the feature of the mean square to variance ratio (MSVR) of the normalized estimated chirp rate, the VGPO jamming can be recognized. Simulation results show that, for a time when the estimated frequency is unchanged, the MSVR of VGPO jamming decreases with the pulse numbers increases, and is always larger than that of a target which is steady. Comparing to other methods, the proposed method can correctly recognize the jamming signal with jamming-to-noise ratio (JNR) 5dB which shows better recognition performance, and is also effective within a shorter period.

Combination of ELM and BEMD for Target Recognition of SAR Images

2021 ◽  
Vol 30 (13) ◽  
Author(s):  
Zhichao Liu ◽  
Baida Qu
Keyword(s):  
Feature Extraction ◽  
Embedded System ◽  
High Efficiency ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Intrinsic Mode Functions ◽  
Sar Images ◽  
Stationary Target

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

The Design of Library Management System Based on Internet of Things and Hand Lines Recognition System

2014 ◽  
Vol 672-674 ◽  
pp. 1985-1990 ◽  
Author(s):  
Wang Fang
Keyword(s):  
Management System ◽  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Library Management ◽  
Material Resources ◽  
Library Management System ◽  
Biometric Systems

For an ordinary individual biometric systems and technology, such as fingerprint recognition, palm recognition, face recognition or iris recognition, or late detection from a single object has crippled so that they have the characteristics of unity and limitations, this paper combining fingerprint and hand palm pattern recognition technology, taking into account the complexity of the image pattern and diversity, we propose a dual recognition algorithm, which greatly makes up for lack of a single fingerprint or palm print recognition method. The technology used in library management system than traditional card-borrowed books have higher efficiency and save manpower and material resources. After the experimental statistics, and achieved the desired results, not only improve the recognition efficiency, but also to ensure the accuracy of the recognition performance.

scholarly journals Modified SIFT Descriptors for Face Recognition under Different Emotions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Nirvair Neeru ◽  
Lakhwinder Kaur
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
User Interaction ◽  
Recognition Algorithm ◽  
Scale Invariant ◽  
Distance Ratio ◽  
Japanese Female ◽  
Database Size ◽  
Fully Automatic ◽  
Scale Invariant Feature

The main goal of this work is to develop a fully automatic face recognition algorithm. Scale Invariant Feature Transform (SIFT) has sparingly been used in face recognition. In this paper, a Modified SIFT (MSIFT) approach has been proposed to enhance the recognition performance of SIFT. In this paper, the work is done in three steps. First, the smoothing of the image has been done using DWT. Second, the computational complexity of SIFT in descriptor calculation is reduced by subtracting average from each descriptor instead of normalization. Third, the algorithm is made automatic by using Coefficient of Correlation (CoC) instead of using the distance ratio (which requires user interaction). The main achievement of this method is reduced database size, as it requires only neutral images to store instead of all the expressions of the same face image. The experiments are performed on the Japanese Female Facial Expression (JAFFE) database, which indicates that the proposed approach achieves better performance than SIFT based methods. In addition, it shows robustness against various facial expressions.

RECOGNITION AND VERIFICATION OF HARDWRITTEN AND HAND-PRINTER BRITISH POSTAL ADDRESSES

1991 ◽  
Vol 05 (01n02) ◽  
pp. 265-291 ◽  
Author(s):  
A. C. DOWNTON ◽  
R. W. S. TREGIDGO ◽  
E. KABIR
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
High Performance ◽  
Recognition Performance ◽  
Contextual Information ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Contextual Knowledge ◽  
Alphanumeric Character ◽  
Simple Features

An algorithmic architecture for a high-performance optical character recognition (OCR) system for hand-printed and handwritten addresses is proposed. The architecture integrates syntactic and contextual post-processing with character recognition to optimise postcode recognition performance, and verifies the postcode against simple features extracted from the remainder of the address to ensure a low error rate. An enhanced version of the characteristic loci character recognition algorithm was chosen for the system to make it tolerant of variations in writing style. Feature selection for the classifier is performed automatically using the B/W algorithm. Syntactic and contextual information for hand-printed British postcodes have been integrated into the system by combining low-level postcode syntax information with a dictionary trie structure. A full implementation of the postcode dictionary trie is described. Features which define the town name effectively, and can easily be extracted from a handwritten or hand-printed town name are used for postcode verification. A database totalling 3473 postcode/address image has used to evaluate the performance of the complete postcode recognition process. The basic character recognition rate for the full unconstrained alphanumeric character set is 63.1%, compared with an expected maximum attainable 75–80%. The addition of the syntactic and contextual knowledge stages produces an overall postcode recognition rate which is equivalent to an alphanumeric character recognition rate of 86–90%. Separate verification experiments on a subset of 820 address images show that, with the first-order features chosen, an overall correct address feature code extraction rate of around 35% is achieved.

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