scholarly journals Paddle Stroke Analysis for Kayakers Using Wearable Technologies

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 914 ◽  
Author(s):  
Long Liu ◽  
Hui-Hui Wang ◽  
Sen Qiu ◽  
Yun-Cui Zhang ◽  
Zheng-Dong Hao
Keyword(s):  
Inertial Sensor ◽  
Quantitative Information ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Sensor Calibration ◽  
Phase Identification ◽  
Extend Kalman Filter ◽  
Multiple Sensors ◽  
Rigid Body Model

Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers’ motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers’ actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, including entry, pull, exit and recovery. The experiment shows that our method can provide comprehensive motion evaluation information under real on-water scenario, and the phase identification of kayaker’s motions is up to 98% validated by videography method. The proposed approach can provide quantitative information for coaches and athletes, which can be used to improve the training effects.

scholarly journals Using Inertial Sensors to Determine Head Motion—A Review

2021 ◽  
Vol 7 (12) ◽  
pp. 265
Author(s):  
Severin Ionut-Cristian ◽  
Dobrea Dan-Marius
Keyword(s):  
Human Activity ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Devices ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Head Motion ◽  
Technical Literature ◽  
Advantages And Disadvantages

Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

scholarly journals Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2110 ◽  
Author(s):  
Long Liu ◽  
Sen Qiu ◽  
ZheLong Wang ◽  
Jie Li ◽  
JiaXin Wang
Keyword(s):  
Motion Tracking ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Recovery Phase ◽  
Descent Method ◽  
Sensor Nodes ◽  
Sensor Calibration ◽  
Gradient Descent Method ◽  
Whole Process ◽  
Training Methodologies

Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist’s motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist’s attitude information after sensor calibration, and then the motions of canoeist’s actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learning algorithm was used as the classification method to divide the stroke cycle into different phases, including propulsion-phase and recovery-phase, a quantitative kinematic analysis was carried out. Experiments conducted in this paper demonstrated that our method possesses the capacity to reveal the similarities and differences between novice and coach, the whole process of canoeist’s motions can be analyzed with satisfactory accuracy validated by videography method. It can provide quantitative data for coaches or athletes, which can be used to improve the skills of rowers.

scholarly journals Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2068
Author(s):  
Armands Ancans ◽  
Modris Greitans ◽  
Ricards Cacurs ◽  
Beate Banga ◽  
Artis Rozentals
Keyword(s):  
Inertial Sensor ◽  
Body Movement ◽  
The Body ◽  
Sensor Nodes ◽  
Digital Cameras ◽  
Wearable Sensor ◽  
Wireless System ◽  
Acquisition Device ◽  
Set Up ◽  
Application Specific

This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

scholarly journals Developing a Powerful and Resilient Smart Body Sensor Network through Hypercube Interconnection

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ahmad S. Almogren
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Intelligent Systems ◽  
Intelligent Design ◽  
State Of The Art ◽  
The Body ◽  
Sensor Nodes ◽  
Body Sensor Networks ◽  
Embedded Computing ◽  
Body Sensor Network

With recent advances in wireless sensor networks and embedded computing technologies, body sensor networks (BSNs) have become practically feasible. BSNs consist of a number of sensor nodes located and deployed over the human body. These sensors continuously gather vital sign data of the body area to be used in various intelligent systems in smart environments. This paper presents an intelligent design of the body sensor network based on virtual hypercube structure backbone termed as Smart BodyNet. The main purpose of the Smart BodyNet is to provide resilience for the BSN operation and reduce power consumption. Various experiments were carried out to show the performance of the Smart BodyNet design as compared to the state-of-the-art approaches.

Investigating the Measurement of Physical Activity and Associated Factors in Youth and Adults with Cystic Fibrosis

2021 ◽  
Author(s):  
◽  
Mayara S. Bianchim
Keyword(s):  
Physical Activity ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Vigorous Physical Activity ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Cut Points ◽  
Measurement And Analysis ◽  
The Relationship ◽  
Crucial Part

Cystic Fibrosis (CF) is a multisystemic condition that affects almost every organ in the body, but especially the lungs. Regular physical activity (PA) can significantly slow disease progression and has become a crucial part of CF care. Previous research evaluating PA in CF has been hindered by the use of cut-points developed for healthy populations and the investigation of collinear movement behaviours as independent entities, both of which are likely to have confounded their findings and any subsequent inferences regarding associated health outcomes. Therefore, the overall aim of this thesis was to investigate the measurement and analysis of PA in those with CF. An initial systematic review provided recommendations for research calibrating accelerometry in paediatric clinical populations, highlighting that the pathophysiology of the condition must be accounted for and that the protocol should include a broad range of activities varying in intensity (Chapter 4). Subsequently, Chapter 5 developed and cross-validated raw acceleration CF-specific cut-points in youth which were then further assessed in Chapter 6, demonstrating that the CF-specific thresholds were associated with higher levels of moderate-to-vigorous physical activity (MVPA) and sedentary time (SED) and lower levels of light PA compared to generic cut-points. Furthermore, lung function was associated with light PA when using condition-specific thresholds. Further investigation of the relationship between PA and health in Chapter 7 found that reallocating time from sedentary to any other behaviour was beneficial for lung function, with the greatest improvements observed when SED was reallocated to sleep or MVPA. Finally, Chapter 8 developed and validated machine learning algorithms that achieved excellent accuracy to classify PA types and intensities in youth with CF. In conclusion, these findings significantly advance the assessment of PA, enhancing our understanding of the relationship between PA and health in CF and informing future condition-specific PA guidelines, care strategies and interventions.

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

scholarly journals Radiomics: from qualitative to quantitative imaging

2020 ◽  
Vol 93 (1108) ◽  
pp. 20190948 ◽  
Author(s):  
William Rogers ◽  
Sithin Thulasi Seetha ◽  
Turkey A. G. Refaee ◽  
Relinde I. Y. Lieverse ◽  
Renée W. Y. Granzier ◽  
...  
Keyword(s):  
Quantitative Imaging ◽  
Quantitative Information ◽  
Clinical Applications ◽  
Machine Learning Algorithms ◽  
Biological Information ◽  
Imaging Data ◽  
Multistage Process ◽  
Advantages And Disadvantages ◽  
Future Direction ◽  
Biological Outcomes

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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