A Classifier of Shoulder Movements for a Wearable EMG-Based Device

2017 ◽  
Vol 02 (02) ◽  
pp. 1740003
Author(s):  
Giuseppina Gini ◽  
Lisa Mazzon ◽  
Simone Pontiggia ◽  
Paolo Belluco
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Body Part ◽  
Time Constraints ◽  
Pattern Recognition Method ◽  
Noninvasive Method ◽  
Recognition Method ◽  
Two Phase ◽  
The Real ◽  
User Intentions

Prostheses and exoskeletons need a control system able to rapidly understand user intentions; a noninvasive method is to deploy a myoelectric system, and a pattern recognition method to classify the intended movement to input to the controller. Here we focus on the classification phase. Our first aim is to recognize nine movements of the shoulder, a body part seldom considered in the literature and difficult to treat since the muscles involved are deep. We show that our novel sEMG two-phase classifier, working on a signal window of 500[Formula: see text]ms with 62[Formula: see text]ms increment, has a 97.7% accuracy for nine movements and about 100% accuracy on five movements. After developing the classifier using professionally collected sEMG data from eight channels, our second aim is to implement the classifier on a wearable device, composed by the Intel Edison board and a three-channel experimental portable acquisition board. Our final aim is to develop a complete classifier for dynamic situations, considering the transitions between movements and the real-time constraints. The performance of the classifier, using three channels, is about 96.9%, the classification frequency is 62[Formula: see text]Hz, and the computation time is 16[Formula: see text]ms, far less than the real-time constraint of 300[Formula: see text]ms.

scholarly journals A real-time walking pattern recognition method for soft knee power assist wear

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Wenkang Wang ◽  
Liancun Zhang ◽  
Juan Liu ◽  
Bainan Zhang ◽  
Qiang Huang
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Recognition Performance ◽  
Pattern Recognition Method ◽  
Joint Angles ◽  
Recognition Method ◽  
Walking Pattern ◽  
Input Signals ◽  
One Step ◽  
Measurement Units

Real-time recognition of walking-related activities is an important function that lower extremity assistive devices should possess. This article presents a real-time walking pattern recognition method for soft knee power assist wear. The recognition method employs the rotation angles of thighs and shanks as well as the knee joint angles collected by the inertial measurement units as input signals and adopts the rule-based classification algorithm to achieve the real-time recognition of three most common walking patterns, that is, level-ground walking, stair ascent, and stair descent. To evaluate the recognition performance, 18 subjects are recruited in the experiments. During the experiments, subjects wear the knee power assist wear and carry out a series of walking activities in an out-of-lab scenario. The results show that the average recognition accuracy of three walking patterns reaches 98.2%, and the average recognition delay of all transitions is slightly less than one step.

scholarly journals A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093313 ◽  
Author(s):  
Tangsen Huang ◽  
Xiangdong Yin ◽  
Qingjiao Cao
Keyword(s):  
Real Time ◽  
Spectrum Sensing ◽  
Computation Time ◽  
Time Spectrum ◽  
Fusion Center ◽  
Local Data ◽  
Green Communication ◽  
Processing Efficiency ◽  
The Real ◽  
Sensing Performance

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.

Real-Time Assembly Recognition Based on the Similarity Between Spatial Increment and Part Model

2019 ◽  
Author(s):  
Jiazhen Pang ◽  
Yuan Li ◽  
Jie Zhang ◽  
Jianfeng Yu
Keyword(s):  
Real Time ◽  
Weak Link ◽  
Intelligent Manufacturing ◽  
Digital Model ◽  
Recognition Method ◽  
Camera System ◽  
The Real ◽  
Depth Images ◽  
Engineering Significance ◽  
Part Model

Abstract Manual work is a weak link within the intelligent manufacturing, however, it plays an important role in the highly customized and multi-variety assembling. Assisted by intelligent assembling technology such as augmented reality, a manual worker can integrate into the cyber-physics system to improve efficiency and reduce errors, which is of great engineering significance in the assembling field of industry 4.0. Assembly recognition is the initial part of progress analysis and it has predictable changing progress stages which can be matched with the digital model for recognition constraints. Therefore, based on the similarity between spatial increment information and part model, a real-time assembly recognition method is proposed in this paper. Firstly, the depth images from the multi-camera system were used to capture the assembling scene. Then, compared with the previous assembling scene, the spatial incremental information was used to quantitatively represent the assembled part. The spatial increment information and digital model are described with distance distribution. Finally, based on Earth mover’s distance algorithm, the matching between the spatial increment information and the part model indicates the part which had been assembled to realize the real-time assembly recognition. In the case study, an assembling process for 3D printing assembly which corresponded with the digital model was used to approve the feasibility of the real-time assembly recognition method.

Handling Overruns and Underruns of Real-Time Processes With Precedence and Exclusion Relations Using a Pre-Run-Time Schedule

2013 ◽  
Author(s):  
Jia Xu
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Total System ◽  
Time Process ◽  
Time Schedule ◽  
Worst Case ◽  
Processor Time ◽  
The Real ◽  
Actual Computation ◽  
Run Time

Many embedded systems applications have hard timing requirements where real-time processes with precedence and exclusion relations must be completed before specified deadlines. This requires that the worst-case computation times of the real-time processes be estimated with sufficient precision during system design, which sometimes can be difficult in practice. If the actual computation time of a real-time process during run-time exceeds the estimated worst-case computation time, an overrun will occur, which may cause the real-time process to not only miss its own deadline, but also cause a cascade of other real-time processes to also miss their deadline, possibly resulting in total system failure. However, if the actual computation time of a real-time process during run-time is less than the estimated worst-case computation time, an underrun will occur, which may result in under-utilization of system resources. This paper describes a method for handling underruns and overruns when scheduling a set of real-time processes with precedence and exclusion relations using a pre-run-time schedule. The technique effectively tracks and utilizes unused processor time resources to reduce the chances of missing real-time process deadlines, thereby providing the capability to significantly increase both system utilization and system robustness in the presence of inaccurate estimates of the worst-case computation times of real-time processes.

Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

2020 ◽  
pp. 095440702094839
Author(s):  
Kai Zhang ◽  
Yi Yang ◽  
Mengyin Fu ◽  
Meiling Wang
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Method ◽  
Global Motion ◽  
Two Phase ◽  
Holonomic Constraints ◽  
Planning Cycle ◽  
Indoor And Outdoor Environments

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.

Research on Network Partitioning Based on Expanded Grey Cluster Theory

2013 ◽  
Vol 791-793 ◽  
pp. 1690-1694
Author(s):  
Ya Ning Shao ◽  
Bo Wang ◽  
Di Chen Liu
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Voltage Stability ◽  
Short Circuit ◽  
Computation Time ◽  
Cluster Method ◽  
Network Partitioning ◽  
Cluster Theory ◽  
The Real ◽  
Identification Criterion

the voltage stability discriminant method of short circuit capacity indicators based on measured trajectory needs a great deal of computation time while dynamic Thevenin equivalent need to be done to all the nodes, it is unable to meet the requirements of rapid real-time sentenced to stability. On this foundation, this paper proposes a quick network partitioning method. Firstly, start the real-time disturbance identification criterion and pick up the bus nods triggered by the disturbance. Then split the bus nods into k subsets using Expanded Grey Cluster method. Finally evaluate stability by parallel computing. The method is proved simple, efficient and practical through simulation.

scholarly journals Real-Time Recognition Method for 0.8 cm Darning Needles and KR22 Bearings Based on Convolution Neural Networks and Data Increase

2018 ◽  
Vol 8 (10) ◽  
pp. 1857 ◽  
Author(s):  
Jing Yang ◽  
Shaobo Li ◽  
Zong Gao ◽  
Zheng Wang ◽  
Wei Liu
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
High Speed ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Computation Time ◽  
Real Data ◽  
Accurate Method ◽  
Recognition Method ◽  
Industrial Object

The complexity of the background and the similarities between different types of precision parts, especially in the high-speed movement of conveyor belts in complex industrial scenes, pose immense challenges to the object recognition of precision parts due to diversity in illumination. This study presents a real-time object recognition method for 0.8 cm darning needles and KR22 bearing machine parts under a complex industrial background. First, we propose an image data increase algorithm based on directional flip, and we establish two types of dataset, namely, real data and increased data. We focus on increasing recognition accuracy and reducing computation time, and we design a multilayer feature fusion network to obtain feature information. Subsequently, we propose an accurate method for classifying precision parts on the basis of non-maximal suppression, and then form an improved You Only Look Once (YOLO) V3 network. We implement this method and compare it with models in our real-time industrial object detection experimental platform. Finally, experiments on real and increased datasets show that the proposed method outperforms the YOLO V3 algorithm in terms of recognition accuracy and robustness.

Sign in / Sign up

Export Citation Format

Share Document