Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

2021 ◽  
pp. 1-21
Author(s):  
Borja Bordel ◽  
Ramón Alcarria ◽  
Tomás Robles
Keyword(s):  
Activity Recognition ◽  
Industry 4.0 ◽  
Markov Models ◽  
Recognition Rate ◽  
Industrial Applications ◽  
Recognition Algorithm ◽  
Second Phase ◽  
K Nearest Neighbors ◽  
Dynamic Time ◽  
Processing Techniques

Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.

scholarly journals FPGA-BASED IMPLEMENTATION OF LITHUANIAN ISOLATED WORD RECOGNITION ALGORITHM / LIETUVIŲ KALBOS PAVIENIŲ ŽODŽIŲ ATPAŽINIMO ALGORITMO ĮGYVENDINIMAS LAUKU PROGRAMUOJAMA LOGINE MATRICA

2013 ◽  
Vol 5 (2) ◽  
pp. 101-104
Author(s):  
Tomyslav Sledevič ◽  
Liudas Stašionis
Keyword(s):  
Word Recognition ◽  
Experimental Testing ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Clock Signal ◽  
Time Warping ◽  
Process Implementation ◽  
Isolated Word ◽  
Dynamic Time ◽  
Isolated Word Recognition

The paper describes the FPGA-based implementation of Lithuanian isolated word recognition algorithm. FPGA is selected for parallel process implementation using VHDL to ensure fast signal processing at low rate clock signal. Cepstrum analysis was applied to features extraction in voice. The dynamic time warping algorithm was used to compare the vectors of cepstrum coefficients. A library of 100 words features was created and stored in the internal FPGA BRAM memory. Experimental testing with speaker dependent records demonstrated the recognition rate of 94%. The recognition rate of 58% was achieved for speaker-independent records. Calculation of cepstrum coefficients lasted for 8.52 ms at 50 MHz clock, while 100 DTWs took 66.56 ms at 25 MHz clock. Article in Lithuanian. Santrauka Pateikiamas lietuvių kalbos pavienių žodžių atpažinimo algoritmo įgyvendinimas lauku programuojama logine matrica (LPLM). LPLM įrenginys pasirinktas dėl lygiagrečiai veikiančių procesų įgyvendinimo galimybės taikant VHDL kalbą. Tai užtikrina spartų signalų apdorojimą esant taktiniam dažniui iki 50 MHz. Kalbos požymiams išskirti taikoma kepstrinė šnekos analizė. Požymiams palyginti taikomas dinaminis laiko skalės kraipymo (DSLK) metodas. Sudaryta 100 žodžių požymių biblioteka, kuri saugoma vidinėje LPLM BRAM atmintyje. Pasiektas 94 % atpažinimo tikslumas priklausomai nuo kalbėtojo ir 58 % – nepriklausomai nuo kalbėtojo. Kepstro koeficientų skaičiavimas vienam žodžiui trunka 8,52 ms, esant 50 MHz taktiniam dažniui, ir šimtui DLSK – 66,56 ms, esant 25 MHz taktiniam dažniui.

scholarly journals Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2190
Author(s):  
Tomasz Kapuscinski ◽  
Marian Wysocki
Keyword(s):  
Sign Language ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Markov Models ◽  
Experimental System ◽  
Deaf People ◽  
Time Warping ◽  
K Nearest Neighbors ◽  
Continuous Stream ◽  
Dynamic Time

The paper addresses the recognition of dynamic Polish Sign Language expressions in an experimental system supporting deaf people in an office when applying for an ID card. A method of processing a continuous stream of RGB-D data and a feature vector are proposed. The classification is carried out using the k-nearest neighbors algorithm with dynamic time warping, hidden Markov models, and bidirectional long short-term memory. The leave-one-subject-out protocol is used for the dataset containing 121 Polish Sign Language sentences performed five times by four deaf people. A data augmentation method is also proposed and tested. Preliminary observations and conclusions from the use of the system in a laboratory, as well as in real conditions with an experimental installation in the Office of Civil Affairs are given.

scholarly journals Designing and Developing a Smart Yogurt Filling Machine in the Industry 4.0 Era

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 300
Author(s):  
Bashir Salah ◽  
Ali M. Alsamhan ◽  
Sajjad Khan ◽  
Mohammed Ruzayqat
Keyword(s):  
Industry 4.0 ◽  
Industrial Applications ◽  
Design Stage ◽  
Control Architecture ◽  
Second Phase ◽  
Level Control ◽  
Input Devices ◽  
Beverage Industry ◽  
Production Techniques ◽  
High Level

Industry 4.0 allows for greater flexibility in production processes so that products can be customized (i.e., mass customization). Innovative production techniques in an industrial liquid/yogurt filling machine (YFM) improved efficiency in the beverage industry. In this study, we have introduced the second phase designed control architecture of our YFM based on the concepts of industry 4.0 incorporating an NFC platform for improving customer satisfaction. Especially during this pandemic period, wireless technologies have been ubiquitous and pervasive for customized products. The basic components of the YFM have been described. High-level control architecture programmed fully automated filling operations, and the design stage of the development of a PFC-based controller for the YFM is elaborated. For the evaluation of the proposed control system, the operations of the electric/pneumatic input devices and actuators were simulated on FluidSIM-MecLab. The results of the simulation verify the design logic of the PFC-based controller. Comparisons were made between different production types using the developing YFM. A complex learning environment replicating a real production system to understand, learn, and apply modern manufacturing approaches has been developed. Through the creation of this YFM, the academic environment and industrial applications are combined. Consequently, the problem verification is becoming more realistic and more efficient than online (trial and error) automation programming.

Segment Matching Gesture Recognition Algorithm and its Application in Smartphone

2014 ◽  
Vol 511-512 ◽  
pp. 936-940
Author(s):  
Yi Zhang ◽  
Sheng Hui Li ◽  
Yuan Luo
Keyword(s):  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Recognition Algorithm ◽  
The Novel ◽  
Time Warping ◽  
Signal Features ◽  
High Power Consumption ◽  
Segment Matching ◽  
Dynamic Time

Aim to the traditional acceleration gesture recognition system on PC platform had the problem of high power consumption, hard to carry and low recognition rate, the paper proposes a novel gesture recognition algorithm. The algorithm first sampled the gestures signal acceleration by acceleration sensor, and then segmented and smoothing filtered the collected original signal. After preprocessing, extracted the feature value and segmented the feature value according to segments signal energy. Finally for all the segments used the improved DTW(Dynamic Time Warping) algorithm[1] to match the extracted signal features with the predefined template feature respectively and integrated the matching results of them, then concluded the final recognition results. We apply the proposed algorithm to the smartphone and test the system. Testing result shows that: The novel algorithm can improve the recognition rate and enable the system to real-time and accuracy recognizes gestures.

scholarly journals Faster R-CNN Algorithm Based Relay Protection Platen State Identification Method

2022 ◽  
Vol 2160 (1) ◽  
pp. 012078
Author(s):  
Xinhai Li ◽  
Haixin Luo ◽  
Lingcheng Zeng ◽  
Chenxu Meng ◽  
Yanhe Yin
Keyword(s):  
Recognition Rate ◽  
Relay Protection ◽  
Recognition Algorithm ◽  
Pressure Plate ◽  
Image Processing Techniques ◽  
State Identification ◽  
Manual Methods ◽  
Processing Techniques ◽  
Speed And Accuracy ◽  
Manual Verification

Abstract Currently, the check of the relay protection pressure plate’s throw-out status is mainly carried out manually, due to the extremely large number of decompression plates, manual methods can cause detection errors due to fatigue. This paper proposes the processing of relay protection pressure plate photographs by using image processing techniques, the Faster R-CNN image recognition algorithm uses the feature of generating detection frames directly using RPN to identify the platen throwback status of the processed platen images, greatly improving the speed and accuracy of the detection frame generation. The experimental results show that, the method proposed in this paper effectively solves the problem of errors arising from manual verification checks of platen throwbacks, reduced workload for substation staff, the platen recognition rate can be over 98% correct.

scholarly journals A Motion-based User Interface for the Control of Virtual Humans Performing Sports

2011 ◽  
Vol 10 (3) ◽  
pp. 1-8
Author(s):  
Xiubo Liang ◽  
Zhen Wang ◽  
Weidong Geng ◽  
Franck Multon
Keyword(s):  
User Interface ◽  
Markov Models ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Interactive Simulation ◽  
Virtual Humans ◽  
Novel Approach ◽  
Human Computer Interfaces ◽  
Average Recognition Rate ◽  
Novice Users

Traditional human computer interfaces are not intuitive and natural for the choreography of human motions in the field of VR and video games. In this paper we present a novel approach to control virtual humans performing sports with a motion-based user interface. The process begins by asking the user to draw some gestures in the air with a Wii Remote. The system then recognizes the gestures with pre-trained hidden Markov models. Finally, the recognized gestures are employed to choreograph the simulated sport motions of a virtual human. The average recognition rate of the recognition algorithm is more than 90% on our test set of 20 gestures. Results on the interactive simulation of several kinds of sport motions are given to show the efficiency and interestingness of our system, which is easy-to-use especially for novice users

A HYBRID TWO-PHASE ALGORITHM FOR FACE RECOGNITION

2004 ◽  
Vol 18 (08) ◽  
pp. 1423-1435 ◽  
Author(s):  
FRANK Y. SHIH ◽  
KAI ZHANG ◽  
YAN-YU FU
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Second Phase ◽  
Two Phase ◽  
Data Set ◽  
Linear Discriminant ◽  
Database Size

Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

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