Lower-Limb Rehabilitation at Home

2020 ◽  
Vol 12 (1) ◽  
pp. 15-27
Author(s):  
Seanglidet Yean ◽  
Bu Sung Lee ◽  
Chai Kiat Yeo
Keyword(s):  
Wearable Sensors ◽  
Initial Study ◽  
Lower Limbs ◽  
Support Vector ◽  
Machine Method ◽  
Average Accuracy ◽  
Sensor Signals ◽  
And Performance ◽  
The Impact ◽  
Rehabilitation Exercise

Aging causes loss of muscle strength, especially on the lower limbs, resulting in a higher risk of injuries during functional activities. To regain mobility and strength from injuries, physiotherapy prescribes rehabilitation exercise to assist the patients' recovery. In this article, the authors survey the existing work in exercise assessment and state identification which contributes to innovating the biofeedback for patient home guidance. The initial study on a machine-learning-based model is proposed to identify the 4-state motion of rehabilitation exercise using wearable sensors on the lower limbs. The study analyses the impact of the feature extracted from the sensor signals while classifying using the linear kernel of the support vector machine method. The evaluation results show that the method has an average accuracy of 95.83% using the raw sensor signal, which has more impact than the sensor fused Euler and joint angles in the state prediction model. This study will both enable real-time biofeedback and provide complementary support to clinical assessment and performance tracking.

scholarly journals An approach for the impact feature extraction method based on improved modal decomposition and singular value analysis

2018 ◽  
Vol 25 (5) ◽  
pp. 1096-1108 ◽  
Author(s):  
Fengfeng Bie ◽  
Kirill V. Horoshenkov ◽  
Jin Qian ◽  
Junfeng Pei
Keyword(s):  
Feature Extraction ◽  
Singular Value ◽  
Support Vector ◽  
Driving Mechanism ◽  
Machine Method ◽  
Value Analysis ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Reciprocating Pump ◽  
The Impact

For non-stationary vibration useful information of the impact feature tends to be overwhelmed with strong routine components, which make it difficult to implement pattern recognition. This paper proposes improved signal processing methods of variational mode decomposition (VMD) and singular value decomposition (SVD) for non-stationary impact feature extraction in application to condition monitoring of reciprocating machinery. The impact feature is firstly simulated with the dynamics' analysis of the driving mechanism of a reciprocating pump. Through comparison the merit of the improved VMD method is demonstrated. The singular value of the decomposed modes is extracted with the SVD method. The support vector machine method is used as the classifier for the extracted set of features. The performance of the proposed VMD-based method is validated practically through a set of measured data from the reciprocating pump setup.

Traffic Sign Recognition Based on Vision Bionics

2012 ◽  
Vol 190-191 ◽  
pp. 746-751
Author(s):  
Zhuo Zhang ◽  
Xin Nan Fan ◽  
Xue Wu Zhang ◽  
Rui Yu Liang ◽  
Shan Ming Lin
Keyword(s):  
Road Traffic ◽  
Positional Distribution ◽  
Saliency Map ◽  
Support Vector ◽  
Traffic Sign ◽  
Machine Method ◽  
Attention Model ◽  
Sign Recognition ◽  
Average Accuracy ◽  
Visual Attention Mechanism

Inspired by the research of human visual system in neuroanatomy and psychology, the paper proposes an road traffic sign identification model based on vision bionics.The model combines data-driven and task-driven visual attention mechanism to focus on traffic sign target rapidly and accuractly.Firstly,It simulates the Itti attention model to obtain “what” information and uses the priori knowledge of positional distribution of traffic sign as “where” information.Then,it adjusts saliency map according to “what” and “where” stream so as to select traffic sign focus preferentially.Secondly,it measures the similarity of shape and color features between traffic sign and attention region to get interested region.Finally, it segments traffic sign based on color characteristics and classify shape of traffic sign based on the Support Vector Machine method. The experimental results demonstrate that the feasibility and effectiveness of the proposed model; Furthermore, the average accuracy rate of shape classification on DtBs matrix reaches 98%.

scholarly journals Golf swing classification with multiple deep convolutional neural networks

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880218 ◽  
Author(s):  
Libin Jiao ◽  
Rongfang Bie ◽  
Hao Wu ◽  
Yu Wei ◽  
Jixin Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Golf Swing ◽  
Support Vector ◽  
Classification Methods ◽  
Deep Convolutional Neural Networks ◽  
Machine Method ◽  
And Performance

The use of smart sports equipment and body sensory systems supervising daily sports training is gradually emerging in professional and amateur sports; however, the problem of processing large amounts of data from sensors used in sport and discovering constructive knowledge is a novel topic and the focus of our research. In this article, we investigate golf swing data classification methods based on varieties of representative convolutional neural networks (deep convolutional neural networks) which are fed with swing data from embedded multi-sensors, to group the multi-channel golf swing data labeled by hybrid categories from different golf players and swing shapes. In particular, four convolutional neural classifiers are customized: “GolfVanillaCNN” with the convolutional layers, “GolfVGG” with the stacked convolutional layers, “GolfInception” with the multi-scale convolutional layers, and “GolfResNet” with the residual learning. Testing on the real-world swing dataset sampled from the system integrating two strain gage sensors, three-axis accelerometer, and three-axis gyroscope, we explore the accuracy and performance of our convolutional neural network–based classifiers from two perspectives: classification implementations and sensor combinations. Besides, we further evaluate the performance of these four classifiers in terms of classification accuracy, precision–recall curves, and F1 scores. These common classification indicators illustrate that our convolutional neural network–based classifiers can basically group the golf swing predefined by the combination of shapes and golf players correctly and outperform support vector machine method representing traditional classification methods.

Passive Sensing of Sensorized Composite Panels: Support Vector Machine

2016 ◽  
Vol 713 ◽  
pp. 199-202
Author(s):  
Nan Yue ◽  
Zahra Sharif Khodaei ◽  
M.H. Ferri Aliabadi
Keyword(s):  
Least Square ◽  
Training Data ◽  
Composite Panel ◽  
Support Vector ◽  
Stiffened Panels ◽  
Passive Sensing ◽  
Vector Machines ◽  
Meta Modeling ◽  
Sensor Signals ◽  
The Impact

Strain readings recorded by surface mounted piezoelectric sensors due to impact events on composite panel are used to detect and characterize the impact. Sensor signals on a composite stiffened panels have been simulated by a valid numerical model. Applicability of least square support vector machines (LSSVM) on creating a meta-model to detect and characterize impact event has been investigated. In particular, the main advantage of LSSVM over other meta-modeling technique was found to be the smaller number of training data that is required. Experimental results on a composite panel has been used to validate the findings.

Method for Predicting Mobile Service Evolution from User Reviews and Update Logs

2020 ◽  
Vol 30 (10) ◽  
pp. 1551-1586
Author(s):  
Jiafei Song ◽  
Zhongjie Wang ◽  
Zhiying Tu ◽  
Xiaofei Xu
Keyword(s):  
User Satisfaction ◽  
Real Data ◽  
Machine Learning Algorithms ◽  
Release Time ◽  
Support Vector ◽  
Mobile Service ◽  
User Reviews ◽  
Average Accuracy ◽  
And Performance ◽  
Application Developers

Because of rapid growth in mobile application markets, competition between companies that provide similar applications has become fierce. To improve user satisfaction for keeping existing users and attracting new users, application developers need to quickly respond to customer feedback regarding functionality and performance defects. In software engineering, specifying an accurate evolution plan according to user feedback is useful but quite difficult. Hence, we propose an approach for predicting and recommending evolution plans to application developers that includes: (1) when a new version of an App should be released; (2) which features should be updated in the next version and (3) if a new version is released, to what degree users would like or dislike it. This approach is based on an elaborate text analysis of massive numbers of user reviews and App update histories. A collocation-based mRAKE method is presented to extract requested and updated features from user reviews and update logs, and the intensity and sentiment scores of each feature are calculated to quantitatively represent time-series histories of App updates and user requests. Machine learning algorithms including linear support vector, Gaussian naïve Bayes and logistic regression are employed to discover the underlying correlation between user opinions embedded in their reviews and the App update behaviors of developers, and rich experiments were conducted on real data to validate the effectiveness of the proposed approach. Overall, our approach can achieve an average accuracy of 72.8% and 93.7% in release time recommendation and content updates of successive versions, respectively, and it can predict user reactions to a planned version with an average accuracy of above 89.0%.

scholarly journals Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

2021 ◽  
Vol 4 (1) ◽  
pp. 22-27
Author(s):  
Saikin Saikin ◽  
◽  
Sofiansyah Fadli ◽  
Maulana Ashari ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Training Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Validation Data ◽  
Machine Method ◽  
Testing Data ◽  
The Impact

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.

scholarly journals Dancing With Parkinson's Disease: The SI-ROBOTICS Study Protocol

2021 ◽  
Vol 9 ◽  
Author(s):  
Roberta Bevilacqua ◽  
Marco Benadduci ◽  
Anna Rita Bonfigli ◽  
Giovanni Renato Riccardi ◽  
Giovanni Melone ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fear Of Falling ◽  
Wearable Sensors ◽  
Modern Dance ◽  
Rehabilitation Program ◽  
Scientific Meetings ◽  
And Performance ◽  
The Impact ◽  
Balance Problems

Introduction: Parkinson's disease (PD) is one of the most frequent causes of disability among older people, characterized by motor disorders, rigidity, and balance problems. Recently, dance has started to be considered an effective exercise for people with PD. In particular, Irish dancing, along with tango and different forms of modern dance, may be a valid strategy to motivate people with PD to perform physical activity. The present protocol aims to implement and evaluate a rehabilitation program based on a new system called “SI-ROBOTICS,” composed of multiple technological components, such as a social robotic platform embedded with an artificial vision setting, a dance-based game, environmental and wearable sensors, and an advanced AI reasoner module.Methods and Analysis: For this study, 20 patients with PD will be recruited. Sixteen therapy sessions of 50 min will be conducted (two training sessions per week, for 8 weeks), involving two patients at a time. Evaluation will be primarily focused on the acceptability of the SI-ROBOTICS system. Moreover, the analysis of the impact on the patients' functional status, gait, balance, fear of falling, cardio-respiratory performance, motor symptoms related to PD, and quality of life, will be considered as secondary outcomes. The trial will start in November 2021 and is expected to end by April 2022.Discussions: The study aims to propose and evaluate a new approach in PD rehabilitation, focused on the use of Irish dancing, together with a new technological system focused on helping the patient perform the dance steps and on collecting kinematic and performance parameters used both by the physiotherapist (for the evaluation and planning of the subsequent sessions) and by the system (to outline the levels of difficulty of the exercise).Ethics and Dissemination: The study was approved by the Ethics Committee of the IRCCS INRCA. It was recorded in ClinicalTrials.gov on the number NCT05005208. The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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