An Auto-Calibration Approach to Robust and Secure Usage of Accelerometers for Human Motion Analysis in FES Therapies

Sun, Mingxu; Jiang, Yinghang; Liu, Qi; Liu, Xiaodong

doi:10.32604/cmc.2019.06079

Cited by 7 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article will use the support vector machine model (SVM) as the classification algorithm of the action recognition method. Support vector machine is a new type of machine learning method based on statistical learning theory and structural risk minimization criteria, and it is also a major achievement of machine learning research in recent years [4]. It finds the global optimal solution from the limited sample information.…”

Section: Methods Of Sports Dance Movementmentioning

confidence: 99%

Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

Yao

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Because of its high research value, action recognition has become a very popular research direction in recent years. However, the research on the combination of motion recognition technology and dance movements is still in its infancy. At the same time, due to the high complexity of dance movements and the problems of human body self-occlusion when performing dances, research on dance video action recognition has been caused. Progress is relatively slow. This article mainly introduces the research of sports dance action recognition system oriented to human motion monitoring and sensing, fully considers the abovementioned problems, and makes in-depth research and analysis on the current excellent action recognition research content in this field. This paper proposes a research method of sports dance movement recognition for human movement monitoring and sensing, including sports dance movement classification algorithm and sports dance movement preprocessing algorithm, which is used to conduct research experiments on sports dance movement recognition for human movement monitoring and sensing. The experimental results of this article show that the average recognition accuracy of the sports dance action recognition system for human motion monitoring and sensing is 92%, which can be used in daily sports dance training and competition.

show abstract

Section: Methods Of Sports Dance Movementmentioning

confidence: 99%

Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

Yao

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Sun et al presented a method for treating functional electrical stimulation (FES) by using a finite state machine (FSM) which has been shown to be effective using additive data by accelerometer according to the calculated gain. The results proved that the calibration gain is close to 1 and the error rate is smaller compared to the error before calibration [16]. Venugopal et al introduced a unique method for intracerebral haemorrhage (ICH) detection by extracting unique features called fusionbased feature extraction (FFE) with deep learning (DL) networks, this method is called FFEDL-ICH.…”

Section: Introductionmentioning

confidence: 97%

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Al-Mekhlafi¹,

Senan²,

Rassem³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.

show abstract

“…Subsequently, the article elaborated on the concept of sports health evaluation and the research content of the sports health evaluation model. In order to enable residents in the study area to reach the threshold of exercise time, the system provides a personalized exercise guide for each exercise behavior of the residents, and conducts the process of energy consumption [ 7 ]. Improve, put forward a health evaluation model of athletes, thereby improving the exercise effect of athletes, eliminating the adverse effects of individual physical differences in the group of athletes, thus realizing a fair group health evaluation system [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Design of a Resident Physical Fitness Data Monitoring System Based on the Sensor and Fuzzy Algorithm

Jun

Mingxiao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This article first introduces the working principle and research progress of motion sensors and analyzes the advantages and disadvantages of motion tracking sensors to provide a reference for in-depth research on motion sensing technology. By combining the fuzzy judgment method to identify the kinematic parameters, this paper establishes the statistical identification of features to predict the kinematic parameters and uses the fuzzy identification method to model the human behavioral characteristics. At the same time, the article designs a set of real-time data monitoring system for athletes’ real-time monitoring behavior under actual sports conditions, which is used to obtain residents’ sports data. Then, based on such requirements, the overall system architecture was designed to integrate the data acquisition module, data storage and processing module, and real-time data presentation module. The motion monitor can initiate a real-time monitoring request for a specific system group, the server can monitor individual groups to add real-time individual motion status, and the system will issue an alert if the system monitors a potentially dangerous situation.

show abstract

An Auto-Calibration Approach to Robust and Secure Usage of Accelerometers for Human Motion Analysis in FES Therapies

Cited by 7 publications

References 10 publications

Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Design of a Resident Physical Fitness Data Monitoring System Based on the Sensor and Fuzzy Algorithm

Contact Info

Product

Resources

About