scholarly journals Digital Twin Coaching for Physical Activities: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5936
Author(s):  
Rogelio Gámez Díaz ◽  
Qingtian Yu ◽  
Yezhe Ding ◽  
Fedwa Laamarti ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Multimodal Interaction ◽  
Team Sports ◽  
Physical Activities ◽  
Future Perspectives ◽  
Privacy And Security ◽  
Digital Twin ◽  
The World ◽  
Work Done

Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

scholarly journals Relation Between Physical Activities and Built Environment: A Literature Review

2014 ◽  
Vol 7 ◽  
pp. 91-98
Author(s):  
Md Masud Parves Rana ◽  
Xiaolu Zhou
Keyword(s):  
Physical Activity ◽  
Literature Review ◽  
Built Environment ◽  
Physical Inactivity ◽  
Physical Activities ◽  
Neighborhood Environment ◽  
High Rate ◽  
Observational Measures ◽  
Perceived Environment ◽  
The World

Physical inactivity has been a leading factor of chronic diseases and high rate of mortality in the world. Despite the fact, only a small portion of people are able to meet up the recommended physical activities. However, there are handful studies suggest that built environment may provide stimulus or barriers to people’s participation in physical activities. Drawing upon this context, this paper aims to review articles regarding ‘built environment and physical activity’ focusing on characteristics of built environment which are particularly helpful in improving neighborhood environment, and to catch the attention to physical activities, such as walking, and cycling. It also critically reviews the measures of built environment, and finds three measures viz. (i) perceived environment measures, (ii) observational measures, and (iii) GIS-based measures. The article argues that integrated measures of built environment might be helpful to reduce limitations of individual measures and to understand the reasons of less participation in physical activities. It also suggests some practical interventions for improvement of built environment which is essentially inevitable to persuade physical activities. DOI: http://dx.doi.org/10.3329/jles.v7i0.20127 J. Life Earth Sci., Vol. 7: 91-98, 2012

scholarly journals ROLE OF PHYSICAL EDUCATION IN THE ALL ROUND DEVELOPMENT OF THE CHILD - A STUDY

2020 ◽  
pp. 1-3
Author(s):  
P.V. Sandhya Latha
Keyword(s):  
Physical Activity ◽  
Physical Education ◽  
Physical Fitness ◽  
Team Sports ◽  
Physical Activities ◽  
Body Of Knowledge ◽  
Wide Range ◽  
History Of ◽  
Physical Skills

Physical education is a course taught in school that focuses on developing physical fitness and the ability to perform and enjoy day to day physical activities with ease. Kids also develop skills necessary to participate in a wide range of activities such as cricket,basketball or swimming.Regular physical education classes prepare kids to be physically and mentally active, fit and healthy. Physical education helps students develop physical skills and confidence. They would be expected to journal about how they feel during the process and reflect on how these changes affect performance and mood.Physical education also helps students develop social skills.For example,team sports help them learn to respect others, contribute to a team goal, and socialize as a productive member of a team.This Study is to prove that there is a direct correlation between physical activity and the overall development of the child. It is to prove that there is a systematic, scientific improvement in the cognitive, emotional, social skills and also improvement in Health when physical education is implemented in the Childs day to day programme.The curriculum of physical education possesses a body of knowledge which is basic to health and fitness that leads to a fine living. It has a core of activity skill and technique in its content.We are living in a world layered in technology and convince.Physical Education is so important for our future because it is one of the best natural and pure means we have to promote and foster play and purpose for our children. Children need it more than we know and technology is slowly eating away at something we might never get back. Physical Education's purpose is to preserve the foundational history of health, fitness, and to allow our youth to develop into people with strong intrapersonal skills,core values,and respect and understanding of a healthy mind/body connection.With physical education being a crucial need especially for children, it should be implemented in all the educational organization.To make sure that it is implemented,it has to be a part of the curriculum.Certain norms have to be implemented to make sure PE is a part of the academic curriculum.Regular assessments will be helpful to work on the improvement of the Childs physical as well as overall development

scholarly journals Employing Machine Learning to Estimate Hallmark Measures of Physical Activities from Wrist-worn Devices Across Age Groups

2021 ◽  
Author(s):  
Mamoun T. Mardini ◽  
Chen Bai ◽  
Amal A. Wanigatunga ◽  
Santiago Saldana ◽  
Ramon Casanova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Energy Expenditure ◽  
Age Groups ◽  
Physical Activities ◽  
Accelerometer Data ◽  
Activity Type ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Score Range

Wrist-worn fitness trackers and smartwatches are proliferating with an incessant attention towards health tracking. Given the growing popularity of wrist-worn devices across all age groups, a rigorous evaluation for recognizing hallmark measures of physical activities and estimating energy expenditure is needed to compare their accuracy across the lifespan. The goal of the study was to build machine learning models to recognize physical activity type (sedentary, locomotion, and lifestyle) and intensity (low, light, and moderate), identify individual physical activities, and estimate energy expenditure. The primary aim of this study was to build and compare models for different age groups: young [20-50 years], middle (50-70 years], and old (70-89 years]. Participants (n = 253, 62% women, aged 20-89 years old) performed a battery of 33 daily activities in a standardized laboratory setting while wearing a portable metabolic unit to measure energy expenditure that was used to gauge metabolic intensity. Tri-axial accelerometer collected data at 80-100 Hz from the right wrist that was processed for 49 features. Results from random forests algorithm were quite accurate in recognizing physical activity type, the F1-Score range across age groups was: sedentary [0.955 – 0.973], locomotion [0.942 – 0.964], and lifestyle [0.913 – 0.949]. Recognizing physical activity intensity resulted in lower performance, the F1-Score range across age groups was: sedentary [0.919 – 0.947], light [0.813 – 0.828], and moderate [0.846 – 0.875]. The root mean square error range was [0.835 – 1.009] for the estimation of energy expenditure. The F1-Score range for recognizing individual physical activities was [0.263 – 0.784]. Performances were relatively similar and the accelerometer data features were ranked similarly between age groups. In conclusion, data features derived from wrist worn accelerometers lead to high-moderate accuracy estimating physical activity type, intensity and energy expenditure and are robust to potential age-differences.

scholarly journals IMPACT OF COVID 19: A QUALITATIVE STUDY GOLFING ACTIVITIES FOR SPORTS TOURISM MANAGEMENT IN TERENGGANU

2021 ◽  
Author(s):  
Jassmin Johari ◽  
Keyword(s):  
Physical Activity ◽  
Physical Activities ◽  
Sporting Events ◽  
Physical Behavior ◽  
Tourism Management ◽  
The World ◽  
Huge Impact ◽  
Sports Activities ◽  
Improve Health ◽  
Life Phenomenon

Sports globally are also affected and impacted because of COVID-19. Time sports although often associated with healthy physical activity and its ability to improve the ability to improve health, these sports activities are seen as more dangerous if continued. Global concerns trigger a disturbing world life phenomenon in all aspects of life especially to the countries hit by this epidemic. This limitless and borderless COVID-19 pandemic forced various major world sporting events to be postponed. Sports are all physical activities performed to express feelings and convey value through the physical behavior as well as to test the skills of an individual with other individuals who venture into the same field of sports through competition based on the rules that have been set. As a clearer understanding, a pandemic occurs when many individuals can be infected with a disease easily and the disease can spread from one individual to another continuously without hindrance around the world and have a huge impact on sporting events.

scholarly journals Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3352
Author(s):  
Mamoun T. Mardini ◽  
Chen Bai ◽  
Amal A. Wanigatunga ◽  
Santiago Saldana ◽  
Ramon Casanova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Energy Expenditure ◽  
Physical Activities ◽  
Learning Models ◽  
Middle Aged ◽  
Activity Types ◽  
Machine Learning Models

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20–50 years), middle-aged (50–70 years], and older adults (70–89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20–89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost’s models were high for sedentary (0.955–0.973), locomotion (0.942–0.964) and lifestyle (0.913–0.949) activity types with no apparent difference across age groups. Low (0.919–0.947), light (0.813–0.828) and moderate (0.846–0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835–1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

scholarly journals Physical activity prediction using fitness data: Challenges and issues

2021 ◽  
Vol 10 (1) ◽  
pp. 419-426
Author(s):  
Nur Zarna Elya Zakariya ◽  
Marshima Mohd Rosli
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Active Role ◽  
Physical Activities ◽  
Machine Learning Techniques ◽  
Behavior Model ◽  
Learning Techniques ◽  
Healthcare Technologies ◽  
Personal Context ◽  
Behavior Change Technique

In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in Behavior Change Technique (BCT) and we selected two suitable models which are Fogg Behavior Model (FBM) and Trans-theoretical Behavior Model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health.

Artificial cognitive systems: the next generation of the digital twin. An opinion.

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 3
Author(s):  
David Jones
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industry 4.0 ◽  
Cognitive Systems ◽  
Digital Twin ◽  
Machine Learning Methods ◽  
Strong Artificial Intelligence ◽  
The World ◽  
High Level ◽  
Computational Systems

The digital twin is often presented as the solution to Industry 4.0 and, while there are many areas where this may be the case, there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability, cause and effect, and planning that Industry 4.0 requires. As the limitations of machine learning are beginning to be understood, the paradigm of strong artificial intelligence is emerging. The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world. This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly. This argument is based on the inherent similarities between the digital twin and artificial cognitive systems, and the insights that can already be seen in aligning the two approaches.

Artificial cognitive systems: the next generation of the digital twin. An opinion.

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 3
Author(s):  
David Jones
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industry 4.0 ◽  
Cognitive Systems ◽  
Digital Twin ◽  
Machine Learning Methods ◽  
Strong Artificial Intelligence ◽  
The World ◽  
High Level ◽  
Computational Systems

The digital twin is often presented as the solution to Industry 4.0 and, while there are many areas where this may be the case, there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability, cause and effect, and planning that Industry 4.0 requires. As the limitations of machine learning are beginning to be understood, the paradigm of strong artificial intelligence is emerging. The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world. This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly. This argument is based on the inherent similarities between the digital twin and artificial cognitive systems, and the insights that can already be seen in aligning the two approaches.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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