Biomedical Engineering Applications for People with Disabilities and the Elderly in the COVID-19 Pandemic and Beyond

By Valentina Emilia Balas

Biomedical Engineering Applications for People with Disabilities and the Elderly in the COVID-19 Pandemic and Beyond presents biomedical engineering applications used to manage people’s disabilities and care for the elderly to improve their quality of life and extend life expectancy. This edited book covers all aspects of assistive technologies, including the Internet of Things (IoT), telemedicine, e-Health, m-Health, smart sensors, robotics, devices for rehabilitation, and "serious" games. This book will prove useful for bioengineers, computer science undergraduate and postgraduate students, researchers, practitioners, biomedical engineering students, healthcare workers, and medical doctors.

This volume introduces recent advances in biomaterials, sensors, cellular engineering, biomedical devices, nanotechnology, and biomechanics applied in caring for the elderly and people with disabilities. The unique focus of this book is on the needs of this user base during emergency and disaster situations. The content includes risk reduction, emergency planning, response, disaster recovery, and needs assessment. This book offers readers multiple perspectives on a wide range of topics from a variety of disciplines. This book answers two key questions: What challenges will the elderly and people with disabilities face during a pandemic? How can new (or emerging) advances in biomedical engineering help with these challenges?

• Includes coverage of smart protective care tools, disinfectants, sterilization equipment and equipment for rapid and accurate COVID-19 diagnosis
• Focuses on the limitations and challenges faced by the elderly and people with disabilities in pandemic situations, such as limitations on leaving their homes and having caregivers and family visit their homes. How can technology help?
• Discusses tools, platforms and techniques for managing patients with COVID-19

• Language: English
• Publisher: Academic Press
• Release date: Jun 18, 2022
• ISBN: 9780323851909

Book preview

Biomedical Engineering Applications for People with Disabilities and the Elderly in the COVID-19 Pandemic and Beyond

Editor

Valentina Emilia Balas

Department of Automation, Industrial Engineering, Textiles and Transport, Aurel Vlaicu University of Arad, Arad, Romania

Editor

Oana Geman

Stefan cel Mare University of Suceava, Suceava, Romania

Table of Contents

Cover image

Title page

Copyright

Contributors

Chapter 1. Physical activities for the elderly in a pandemic context during a relaxation of restrictions

1. SARS-CoV-2 and the pandemic context in Romania

2. Physical fitness

3. Energy systems required during exertion

4. Physical fitness workout principles

5. The effects of the aging process

6. Goals for a healthy lifestyle

7. Assessment of physical fitness of the elderly through tests

8. Outdoor physical exercise and monitoring the effort through electronic devices

Chapter 2. Intelligent remote system for assessing a subject's health during sleep

1. Comfort and disease prevention using smart systems

2. Methods and algorithms used in data analysis

3. Project description

4. Conclusions

Chapter 3. A study on the recent developments in voltammetric sensors for the β-blocker propranolol hydrochloride

1. Introduction

2. Electrochemical sensors for the detection of PRO

3. Conclusion and future perspective

Chapter 4. Cloud-IoMT-based wearable body sensors network for monitoring elderly patients during the COVID-19 pandemic

1. Introduction

2. The Internet of Medical Things for COVID-19

3. The wearable body sensors network for COVID-19

4. Cloud-IoMT-based WBSN framework for monitoring elderly patients during the COVID-19 pandemic

5. Practical case for CI-WBSN monitoring of elderly patients during the COVID-19 pandemic

6. Discussion and future research directions

7. Conclusion

Chapter 5. Indoor physical activities for the elderly during the SARS-CoV-2/COVID-19 pandemic

1. SARS-CoV-2/COVID-19 and the emergency state in Romania

2. Motor capacity and physical activity

3. Warm-up before physical activities

4. Physical exercises and prophylaxis

5. Diet and physical activities

6. Aging

7. Indoor physical activities and maintaining of the health status and exertion

Chapter 6. Animated line, bar, and bubble plots for better COVID case analysis: Using R programming

1. Introduction

2. Methods

3. Animated line plot

4. Animated bar plot

5. Bubble plot animation

6. Conclusion

Chapter 7. Applications of physical activity before and during the COVID-19 pandemic for the elderly

1. Biopsychomotor particulars of senior citizens

2. Applications of aquatic activity in the third age

3. Applications of physical activity—medical gymnastics

4. Evaluation methods

5. Conclusions

Chapter 8. Recovery activities for the locomotor system during the COVID-19 pandemic

1. Theoretical-methodical aspects of pathophysiology of posttraumatic sequelae

2. Development of recovery programs for musculoskeletal injuries during the COVID-19 pandemic

3. Methods of evaluation of the musculoskeletal system

4. Complementary methods and techniques in posttraumatic recovery of the musculoskeletal system

Chapter 9. The emerging association between boredom, COVID-19 anxiety, and aggressiveness in imposed prolonged social isolation

1. Introduction

2. Materials and methods

3. Results and discussion

4. Discussions and conclusion

Chapter 10. COVID-19 research: Open data resources and challenges

1. Introduction

2. COVID-19 and data-driven techniques

3. Medical image data sources

4. Useful text datasets on COVID-19

5. Mobility datasets

6. Speech dataset

7. Challenges and future directions

8. Conclusion

Chapter 11. Comprehending COVID-19 as a contact network

1. Introduction

2. Graph theory for COVID-19

3. Conclusion

Chapter 12. Coronavirus: Diagnosis, detection, and analysis

1. Introduction

2. Effects of COVID-19 on the respiratory system

3. Symptoms of COVID-19

4. Transmission of COVID-19

5. Diagnosis of COVID-19

6. Biosensors for coronavirus detection

7. Detection and measurement of bios-Species at the circuit level

8. Engineered nanomaterials with an artificial intelligence prediction model

9. Prediction and analysis with a machine learning approach

10. Conclusion

Chapter 13. Artificial intelligence: A boon for COVID-19 disease management

1. Introduction

2. Background

3. Some AI technologies that are helping to combat COVID-19: Part A

4. Some AI technologies that are helping in combating COVID-19: Part B

5. Global health challenges and AI

6. Limitations of AI

7. Other updates about the COVID-19 vaccine

8. Future work and conclusion

Chapter 14. Radiotherapy for pelvic malignancies in a COVID-19 pandemic scenario: Focus on rectal and cervical cancers

1. Introduction

2. Neoadjuvant treatment for locally advanced rectal cancer in a COVID-19 scenario. Is it time for a step back after a more aggressive approach?

3. Multimodal treatment of locally advanced cervical cancer in a COVID-19 outbreak: Therapeutic options and arguments

4. Radiotherapy: A cornerstone of treatment for cervix cancer

5. The treatment of locally advanced cervical cancer in the COVID-19 era: A radiobiological approach

Chapter 15. A warehouse of information, COVID HUB: Data handling made simple!

1. Introduction

2. Literature survey

3. Proposed methodology

4. Conclusion

Chapter 16. Publisher software application for Braille devices

1. Introduction

2. Theoretical foundation of the application

3. Description of the application

4. Experimental results

5. Conclusions

6. Future work

Chapter 17. Challenges caused by the pandemic for the recovery program of cervical disc herniation

1. Introduction

2. Clinical aspects of cervical disc herniation

3. Examination for cervical disc herniation

4. General treatment

5. Research

6. Data analysis

7. Conclusions of the research

Chapter 18. Strength training program for postmenopausal women with osteoporosis

1. What is osteoporosis?

2. Osteogenesis and osteolysis

3. Risk factors

4. The influence of exercise on muscle mass and bone cells

5. Strength training program for postmenopausal women with osteoporosis

Chapter 19. The role of vitamin D and physical activity on osteopenia and osteoporosis

1. Bone physiology

2. Epidemiology of osteoporosis

3. Bone mineral density

4. Vitamin D and osteoporosis

5. Physical activity and osteoporosis

Chapter 20. Osteoporosis: Evaluation and complications

1. Osteoporosis: Evaluation

2. Assessing the quality of life among patients with osteoporosis

3. Osteoporosis: Possible complications

Chapter 21. Sedentarism—A predominant factor in difficult post-COVID-19 recovery

Conclusions

Chapter 22. Improving the body’s immunity against SARS-CoV-2 through exercise

Chapter 23. Respiratory dispositive with intelligent shape-memory alloy wires to help artificial ventilation during sleep for SARS-CoV patients

1. Introduction

2. Shape-memory alloys (SMA) in hot shape (austenitic structure)

3. Programmable belt-type device for improving artificial ventilation with optoelectronic control

4. Conclusions

Chapter 24. Smart sensing and actuators for people with hand motion impairment: assessment and support

1. Introduction

2. Prosthetic systems

3. Electromyography

4. Smart sensing and actuators for people with hand motion impairment

5. Prosthesis realization

6. Testing and results

7. Conclusion

8. Future directions

Chapter 25. Inner and outer conflicts born during the COVID-19 lockdown: A thematic analysis perspective

1. General overview

2. Local context of the pandemic

3. Methodology and thematic analysis

4. Themes that emerged from the interviews

5. Conclusions

Chapter 26. Impact of the COVID-19 pandemic on psychomotric components in chess games for children aged 8–10

1. Conceptual delimitation regarding the psychomotor training of chess-playing children aged 8–10

2. Personal contributions on identifying aspects of psychomotor skills in the game of chess

3. Psychomotor skills applied in physical education and sports

4. Biopsychomotor features of children aged 8–10

Initial conclusions

5. Methodology used for research

6. Methods and means specific to physical education and sports regarding the psychomotor training of children aged between 8 and 10 years online and on site within the COVID-19 pandemic conditions

7. Means used to develop psychomotor skills in the offline environment (normal face-to-face)

8. Means used for the development of psychomotor qualities in the online environment

9. Description of the main psychomotor qualities and their development

10. Tests applied to research on psychomotor testing of children aged 8–10

11. Organizing and conducting research: Research subjects

12. Final conclusions

Chapter 27. Impact of physical activities on overweight people during the COVID-19 pandemic

1. Current conceptions on obesity

2. Obesity in children and adolescents

3. Obesity in adults

4. Adipose tissue – anatomy and physiology

5. Combating obesity through physical activities and other complementary techniques

6. Methods of investigation and evaluation

7. Conclusions

Chapter 28. Vulnerabilities and new critical security challenges of the Internet of Things (IoT)

1. Introduction

2. IoT strategies

3. IoT concept

4. Using blockchain techniques to increase IoT security

5. Conclusions

Chapter 29. Impact of the COVID-19 pandemic on the physical and mental health of the elderly

1. Introduction

2. Impact of the pandemic

3. Nutrition and lifestyle

4. Physical activity

5. Effects of aging

6. Recommendations on daily activities

7. Conclusions

Chapter 30. A smart virtual vision system for health monitoring

1. Introduction

2. The need for smart systems

3. The Internet of Things

4. Integration of smart devices and virtual vision system

5. Healthcare monitoring

6. COVID-19 applications

7. Challenges and opportunities

8. Summary and conclusion

Index

Copyright

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Contributors

Moses Kareem Abiodun,     Department of Computer Science, Landmark University, Omu Aran, Kwara State, Nigeria

Gheorghe Agache,     Gymnasium School no.17, Botoşani, Romania

Abidemi Emmanuel Adeniyi,     Department of Computer Science, Landmark University, Omu Aran, Kwara State, Nigeria

Maricel Agop,     Gheorghe Asachi Technical University of Iaşi, Iaşi, Romania

Gbemisola Janet Ajamu,     Department of Agricultural Extension and Rural Development, Landmark University, Omu-Aran, Kwara State, Nigeria

Teodora Apopei,     Stefan cel Mare University of Suceava, Suceava, Romania

Muhammad Arif,     Pokhara University, Nepal; School of Computer Science, Guangzhou University, China; IIS, India

Sandhya Avasthi

Amity University, Noida, Uttar Pradesh, India

ABES Engineering College Gaziabad, Uttar Pradesh, India

Joseph Bamidele Awotunde,     Department of Computer Science, University of Ilorin, Ilorin, Kwara State, Nigeria

Femi Emmanuel Ayo,     Department of Computer Science, McPherson University, Seriki-Sotayo, Ogun State, Nigeria

Valentina Emilia Balas,     Department of Automation, Industrial Engineering, Textiles and Transport, Aurel Vlaicu University of Arad, Arad, Romania

S. Beena,     Department of Chemistry, Amrita Vishwa Vidyapeetham, Amritapuri, India

Calin Gheorghe Buzea

National Institute of Research and Development for Technical Physics, Iaşi, Romania

Prof. Dr. N. Oblu, Clinical Emergency Hospital, Iaşi, Romania

Marius Mihai Cazacu,     Gheorghe Asachi Technical University, Iaşi, Romania

Ritu Chauhan,     Amity University, Noida, Uttar Pradesh, India

Roxana Chiş,     Aurel Vlaicu University of Arad, Arad, Romania

Nicanor Cimpoeşu,     Department of Materials Science, Gheorghe Asachi Technical University of Iaşi, Iaşi, Romania

Ramona Cimpoeşu,     Department of Materials Science, Gheorghe Asachi Technical University of Iaşi, Iaşi, Romania

Mihai Constantinescu,     Stefan cel Mare University of Suceava, Suceava, Romania

Anastasia Cotov,     Electronic and Telecommunication Department, Constanta Maritime University, Constanta, Romania

Sorin Curea,     Stefan cel Mare University of Suceava, Suceava, Romania

Edgar Demeter,     Aurel Vlaicu University of Arad, Arad, Romania

Namrata Dhanda,     Amity University, Noida, Uttar Pradesh, India

Kanav Dhir,     Department of Chemistry, DAV College, Chandigarh, Punjab, India

Tiberiu Dughi,     Aurel Vlaicu University of Arad, Arad, Romania

Anca Egerău,     Aurel Vlaicu University of Arad, Arad, Romania

Haroon Elahi,     Department of Computing Science, Umea University, Umea, Sweden

Armand Enache,     Grigore T. Popa University of Medicine and Pharmacy of Iaşi, Iaşi, Romania

Oana Geman,     Stefan cel Mare University of Suceava, Suceava, Romania

Deepika Ghai,     School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, Punjab, India

R. Girija,     School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

Mihaela Hnatiuc,     Electronic and Telecommunication Department, Constanta Maritime University, Constanta, Romania

Iulian Ştefan Holubiac,     Stefan cel Mare University of Suceava, Suceava, Romania

Dragos Teodor Iancu

Grigore T. Popa University of Medicine and Pharmacy of Iaşi, Iaşi, Romania

Regional Institute of Oncology, Iaşi, Romania

Roxana Irina Iancu

Grigore T. Popa University of Medicine and Pharmacy of Iaşi, Iaşi, Romania

St. Spiridon Clinical Emergency Hospital, Iaşi, Romania

G. Ignisha Rajathi,     Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Sadaf Iqram,     Amity University, Noida, Uttar Pradesh, India

Diana Roxana Izdrui,     Stefan cel Mare University of Suceava, Suceava, Romania

Anubha Jain,     Pokhara University, Nepal; School of Computer Science, Guangzhou University, China; IIS, India

S.L. Jayalakshmi,     School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

Vijay Jeyakumar,     Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

R. Johny Elton,     Indsoft Technologies, Tirunelveli, Tamil Nadu, India

Florin Valentin Leuciuc,     Stefan cel Mare University of Suceava, Suceava, Romania

Ramona Lile,     Aurel Vlaicu University of Arad, Arad, Romania

Roxana Maier,     Aurel Vlaicu University of Arad, Arad, Romania

Vasile Marineanu,     University of Bucharest, Bucharest, Romania

Namrata Mendiratta,     School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, Punjab, India

Camil Ciprian Mireştean

University of Medicine and Pharmacy, Craiova, Romania

Clinical Hospital of the Romanian Railways, Iaşi, Romania

Antoanela Naaji,     Vasile Goldis Western University of Arad, Arad, Romania

Simona Nastase,     Stefan cel Mare University of Suceava, Suceava, Romania

K. Nirmala,     Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

R. Nithya,     Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

Opeyemi Eyitayo Ogundokun,     Agricultural and Rural Management Training Institute, Ilorin, Kwara State, Nigeria

Roseline Oluwaseun Ogundokun,     Department of Computer Science, Landmark University, Omu Aran, Kwara State, Nigeria

Ciprian Paraschiv,     Department of Physical Education, Grigore T. Popa University of Medicine and Pharmacy of Iaşi, Iaşi, Romania

Petronela Paraschiv,     Department of Teaching Staff Training, Gheorghe Asachi Technical University of Iaşi, Iaşi, Romania

Marius Popescu,     Vasile Goldis Western University of Arad, Arad, Romania

Octavian-Adrian Postolache,     Iscte-Instituto Universitario de Lisboa and Instituto de Telecomunicaçoes, Lisboa, Portugal

Alin Dan Potorac,     Stefan cel Mare University of Suceava, Suceava, Romania

Marius Prelipceanu,     Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, Suceava, Romania

Dana Rad,     Aurel Vlaicu University of Arad, Arad, Romania

Gavril Rad

Aurel Vlaicu University of Arad, Arad, Romania

West University of Timişoara, Timişoara, Romania

Yagyanath Rimal,     Pokhara University, Nepal; School of Computer Science, Guangzhou University, China; IIS, India

Alina Roman,     Aurel Vlaicu University of Arad, Arad, Romania

Adi Sala,     Vasile Goldis Western University of Arad, Arad, Romania

A. Santhy,     Department of Chemistry, Amrita Vishwa Vidyapeetham, Amritapuri, India

Tanushree Sanwal,     Amity University, Noida, Uttar Pradesh, India

Sînziana-Călina Silişteanu,     Stefan cel Mare University of Suceava, Suceava, Romania

Ovidiu Toderici,     Aurel Vlaicu University of Arad, Arad, Romania

Suman Lata Tripathi,     School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, Punjab, India

R. Vedhapriyavadhana,     School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

Yegnanarayanan Venkatraman,     Department of Mathematics, Kalasalingam Academy of Research and Education, Deemed to be University, Krishnankoil, Tamil Nadu, India

Dragoş Vicoveanu,     Stefan cel Mare University of Suceava, Suceava, Romania

Elena Vizitiu,     Stefan cel Mare University of Suceava, Suceava, Romania

Krithicaa Narayanaa Yegnanarayanan,     Department of Biomedical Sciences, Sri Ramachandra Institute for Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, India

Chapter 1: Physical activities for the elderly in a pandemic context during a relaxation of restrictions

Florin Valentin Leuciuc     Stefan cel Mare University of Suceava, Suceava, Romania

Abstract

SARS-CoV-2 is the virus that causes COVID-19 infection, which has a higher mortality rate for the elderly with comorbidities (chronic respiratory diseases, diabetes, hypertension, cardiovascular disease, chronic kidney diseases). The elderly (those over 65 years old) represent around 80% of hospitalizations and more than 70% of deaths related to COVID-19. A healthy lifestyle and good physical condition are necessary to achieve well-being, and the incidence of diseases is significantly lower for active people. In order to monitor the efficiency of an activity, it is recommended to use electronic devices (bracelets, fitness trackers). This kind of information could help the elderly to be more motivated in practicing daily physical activities according to their ability.

Keywords

Aging; Elderly; Monitoring; Outdoor; Pandemic

1. SARS-CoV-2 and the pandemic context in Romania

2. Physical fitness

3. Energy systems required during exertion

4. Physical fitness workout principles

5. The effects of the aging process

6. Goals for a healthy lifestyle

7. Assessment of physical fitness of the elderly through tests

8. Outdoor physical exercise and monitoring the effort through electronic devices

References

1. SARS-CoV-2 and the pandemic context in Romania

SARS-CoV-2 is the virus that causes COVID-19 infection, which has a higher mortality rate for the elderly with comorbidities (chronic respiratory diseases, diabetes, hypertension, cardiovascular disease, chronic kidney diseases) [1–5].

Another category of the population that is more affected by this virus is represented by the obese [6–11], and in this context it is important to adopt a healthy lifestyle that includes physical activities.

The severity of coronavirus disease depends on a patient's age. The elderly (those over 65 years old) represent around 80% of hospitalizations and more than 70% of deaths [12–14] due to COVID-19, because their immune system does not have the capacity to effectively fight against the virus [15] and also because most of them present comorbidities [16–22].

To prevent infection with SARS-CoV-2 virus it is recommended to respect hygienic measures, physical distancing, and to practice physical activities in order to maintain health status and the physical fitness level.

For the elderly, individual physical activities are recommended in order to prevent the spread of coronavirus, because the immune system of the body is not as effective at that age. Also, most elderly people have chronic diseases which may affect their health status.

During the relaxation phase of the coronavirus (SARS-CoV-2) pandemic (which started in Romania mid-May, 2020), the population was able to practice physical activities outdoors due to law no. 55 of May 5, 2020, which established an alert state as the response to the emergency situation of special magnitude and intensity.

In the second phase of the pandemic (after mid-May) the authorities allowed the population to practice physical activities outdoors; because summer was close, weather conditions were suitable to practice different physical activities outdoors. Practicing physical activities outdoors enables beneficial influences of sun, wind, and water on human the body and has health benefits.

According to their physical fitness level, health status, and personal wishes, the elderly can practice individually, in safe conditions, a wide range of exercises.

Outside, it is imperative to respect the rules of social distancing and hygienic measures in order to prevent contacting of coronavirus.

Respecting the rules of social distancing (the author prefers use of the term physical distancing) with at least 10 square meters per person, the elderly can practice various exercises in small groups in order to socialize.

2. Physical fitness

Physical fitness is typically considered a set of characteristics that people gain through various physical efforts. In fact, physical fitness consists of a variety of measurable components, some of which are skill-related and others which are health-related [23].

The term motor capacity (used in Central and Eastern Europe) is represented by the concept of fitness, and designates a set of attributes by which the individual copes with the physical and functional demands of daily or sports activities, depending on his/her anatomical, physiological and psychological condition [24].

Health-related physical fitness is generally assumed to be fitness related to some aspect of health characteristics and behaviors, and is concerned with the quality of function of the fundamental organ systems that constitute human physiology, and most particularly the muscular, nervous, cardiovascular and respiratory systems. Thus measures of health-related fitness include strength and endurance of skeletal muscles, joint flexibility, cardiovascular endurance, and body composition [25].

A good physical condition implies a good level of fitness for all its components, meaning good life quality and benefits, both physically and mentally.

Depending on the individual objectives, motivation, and level of training we can talk about:

• Fitness for sports, also called performance-specific fitness, in which the effort parameters are in accordance with the particularities of the practiced sport branch and the energy systems have different influences in performing specific tasks. Sports fitness components are: speed, agility, balance, reaction time, aerobic endurance, local muscular endurance, muscle strength and power, and flexibility [26].

• Fitness for health aims at ensuring the individual's ability to perform daily tasks and thus reduce the incidence of certain diseases. The components of fitness for health are: aerobic endurance, local muscular endurance, muscular strength and power, flexibility, body composition, and mental health [26].

3. Energy systems required during exertion

The energy systems that provide the sources for achieving muscle contraction are [23]:

• Anaerobic alactacid—in which ATP (adenosine triphosphoric acid) is the immediate source of energy that ensures muscle contraction and turns into ADP (adenosine diphosphoric acid), phosphate, and energy (ATP → ADP+phosphate+energy); phosphocreatine (PC) contributes to the regeneration of ATP (ADP+PC → ATP+creatine), contributing to the achievement of muscle contractions in an anaerobic regime for up to 30s;

• Anaerobic lactacid—this uses as energy sources the glycogen stored in the muscles and liver, which is transformed into energy through a process called glycolysis (glycogen → ATP+lactic acid). Accumulations of lactic acid in muscles and blood lead to muscle fatigue. Its elimination is done through moderate physical effort. The lactacid anaerobic system is an intermediate, transitional one, providing energy for up to 5min to achieve muscle contractions;

• Aerobic—this comes into action gradually, after 5min, having glucose and fatty acids (triglycerides) as energy sources.

Depending on the duration and intensity of the effort, the share of the three energy systems is different, being gradually involved in supporting the effort. In very short efforts, the anaerobic system is involved, in the short and medium ones, the anaerobic alactacid and anaerobic lactacid systems act, and in the long ones all three energy systems are involved.

For example, in a 100m speed race, 98% of the energy is provided by the anaerobic alactacid system and 2% of the energy by the anaerobic lactacid system; in a 200m race swimming, the share of the energy systems is as follows: anaerobic alactacid, 30%; anaerobic lactacid, 65%; aerobic, 5%; and in a cross-country skiing marathon, the share of the lactacid anaerobic system is 5% and that of the aerobic one is 95% [23].

A synthetic presentation of the energy systems used by the body in performing physical effort is made in Table 1.1.

4. Physical fitness workout principles

Throughout the preparation for improvement of physical condition, and therefore of fitness, four principles must be observed [23]:

Table 1.1

• Overload—involving a demand beyond the limits to which the body is accustomed and which will lead, over time, to functional adjustments and an increase in the efficiency of actions. The requirements of this principle are achieved through actions on the effort parameters during physical exercise practice.

• Specificity of training—practicing a certain category of exercises (specific to a sport) will lead to the emergence of metabolic and physiological adaptation for that specific effort that has effects in terms of performance and fitness.

• Individual particularities—the result of a workout program will not be the same for all practitioners, so the program must be individualized according to the particularities of each person (physical condition, age, health).

• Deconditioning—this is seen if the frequency of training preparation is decreased or if it is not performed at all. Thus, in order to maintain an optimal level of physical condition, it is necessary that the physical exercises be performed regularly.

Starting from these principles of optimizing the physical condition, it is necessary in training/training sessions to apply the principles of FITT (Frequency, Intensity, Time, Type).

5. The effects of the aging process

Aging is a natural process that produces changes at the physiological level of the body; its negative effects can be limited by a careful diet and exercise. It should not be associated with weight gain, or a weak and limited body in terms of efficiency regarding the physical activity; these factors are the consequence of sedentarism and not of aging [27].

Worldwide, the percentage of the elderly population (over 60 years old) is 11.7% (men, 10.7%; women, 12.8%). The percentage distribution differs depending on the degree of development of countries as follows: developed countries, 22.9%; developing countries, 9.4%; poorly developed countries, 5.4%. At the continental level there are major differences: Africa, 5.4%; Asia, 15.5%; Europe, 22.9%; Central and South America, 10.6%; North America, 19.9%; Australasia and Oceania, 15.9% [28].

Maintaining weight and body composition in optimal margins is the key to good health and fitness for the elderly. It should be borne in mind that the daily caloric requirements for the elderly are reduced by 100 calories for every decade. After 35 years old, if the body is not constantly subjected to exercise, it begins to lose muscle mass, which has implications for the need for calories. By exercising, it is possible to maintain or even increase muscle mass that will require additional caloric intake [27].

If no measures are taken to counteract the effects of aging through an active lifestyle, the effects will be visible on the quality of life and general health of the body (obesity, cardiovascular disease, diabetes).

The elderly population is the most exposed category to chronic diseases, and by exercising we want to improve the physical condition with positive results on quality of life and life expectancy [28].

At the level of the cardiovascular system, the pulse at rest is lower, the blood vessels are no longer as mobile, and atherosclerosis sets in, which also leads to the appearance of hypertension. The number of red blood cells is reduced, thus causing anemia and blockages of peripheral circulation resulting in the forming of clots and inflammation of the veins (phlebitis). Due to calcification of the airways, they lose their flexibility, a visible effect on the muscles involved in performing respiration. For these reasons, the vital lung capacity suffers, decreasing by 35% until the age of 50, and by 60% at the age of 70. Changes are also visible on the musculoskeletal system, where, after 40 years of age the muscles begin to gradually atrophy (sarcopenia), so that by 80 years of age, the individual retains half the muscle mass of their youth. At the bones and joints level, there is a reduction in bone density (because the body can no longer absorb the same amount of calcium), which causes osteoporosis, with the risk of fractures and cartilage damage. Ligaments and tendons lose their elasticity, and the individual experiences a reduction in joint mobility. At the level of the digestive system there is a decrease in the tone of the intestinal muscles, with the effect of slowing down peristaltic contractions which is one of the causes of constipation and hemorrhoids. With every decade after 50 years of age, the caloric requirement is reduced by 10% due to a metabolism reduction, body weight, and a lower activity lifestyle. After 30 years of age, the number of neurons begins to decrease (with an effect on the volume and weight of the brain), and as a consequence, reductions in the acuity of the senses, reaction time, and reflexes are gradually observed. These physiological effects also influence the efficiency of the immune system, which is reduced by up to 50%, which is why the body no longer has the same ability to fight disease [29].

6. Goals for a healthy lifestyle

Achieving goals is related to how they are set, including whether they are realistic and can be achieved by the practitioners.

In this sense, there are three steps that must be taken to obtain visible effects [27]:

• at the beginning choosing a goal that is easy to achieve;

• working only to achieve a single goal;

• if there are difficulties in achieving the objective, the applied workout strategy must be reviewed.

The actions to be taken to achieve the objectives include [27]:

• forming a group of friends and acquaintances who will support you;

• making time for the planned activity; making a change of priorities if necessary;

• creating an action plan/motivation plan that suits you;

• constantly monitoring the progress you make;

• rewarding yourself for achieving a goal;

• using a long-term vision to improve your quality of life.

7. Assessment of physical fitness of the elderly through tests

Tests have been identified for every physical fitness component according to age characteristics (Table 1.2).

Lifting from a chair for 30s —this is used to determine the strength of the lower limbs by performing lifts while sitting on a chair (without arms, 43cm high) for 30s.The score obtained is given by the number of repetitions performed;if the subject performs the repetitions at the end of the session, the repetitions are counted.

Weight flexion of the arm (2.3kg, female; 3.6kg, male)—this is used to assess the strength of the upper limbs by performing as many repetitions as possible in 30s. The score obtained is given by the number of repetitions performed; if the subject performs the repetitions at the end of the session, the repetitions are counted.

Sit-and-reach test —this helps to assess the flexibility of the lower limb from sitting on a chair with the legs outstretched when the subject tries to touch the toes and maintain the position for 2s. Use a ruler of 50cm, oriented with gradation 0 toward the toes and mark the distance from them with + (if it exceeds the toes) or – (if it does not reach the toes).

Posterior mobility of the arms —this it evaluates the mobility of the subject's shoulder joints, one arm being placed in the upper part and the other in the lower part of the trunk (dorsal part); the goal is to see if the subject can touch the fingers of the other hand. A 50cm ruler is used for the measurement and the distance to the fingers is marked with a + (if it exceeds the fingers) or – (if it does not reach the fingers of the other hand).

6-Minute walking test —this helps determine aerobic endurance by moving (walking) for 6min. If the subject is not able to walk continuously, they can stop to rest, the total distance being counted in meters.

2-Minute walk —this is an alternative way of assessing aerobic endurance, applied by walking on the spot, where the level to which the foot (knee joint) should be raised is the middle of the distance between the patella and the iliac crest, which should be marked by a landmark. Perform as many steps as possible while meeting the landmark throughout the 2min.

8-Step lifting and moving test —this is a test that assesses strength, speed, agility, and dynamic balance from a sitting position followed by lifting, moving to a cone located at eight steps (2.45 m), and then returning and back to sitting.

Table 1.2

Table 1.3

Body mass index —this is calculated using the anthropometric parameter as height and weight, based on the formula BMI = W (kg)/H (m)².

The average values of the samples for the elderly are presented in Tables 1.3 and 1.4 [30].

8. Outdoor physical exercise and monitoring the effort through electronic devices

The elderly can practice a wide range of physical exercises outdoors according to their physical fitness level, motivation, health status, and desire.

Before starting to practice exercises, it is recommended to warm-up all body parts properly in order to avoid injuries.

Some examples of exercises are:

• Walk fast, distance 1–3km;

• Jogging, 2–4km;

• Running, 3–5km;

• Alternate walking/jogging/running, 4–6km;

• Variable tempo running (fartlek), 3–5km;

• Swimming, various styles, 200–1000m;

• Cycling, 5–20km;

Table 1.4

• Standing, step forward with left foot (hold 10–15s), return, step forward with right foot (hold 10–15s), return, three to four repetitions;

• From standing, left side bent (hold 10–15s), return, right side bent (hold 10–15s), return, three to four repetitions;

• On the knees, sitting on the heels, stretch forward with the arms in the extension of the head, chest pressed to the ground, hold for 20s, three to four repetitions;

• On the knees, sitting on the heels, lower the torso forward with palm resting and torso extension, hold for 20s, three to four repetitions;

• On the floor with one leg (right) outstretched forward, the other (left) with the knee on the ground. Leaning to the outstretched leg and holding for 10–15s, then lateral twist to the flexed leg; the same movement for the other leg, three to four repetitions;

• Sitting with one leg (right) outstretched forward, the other (left) sideways with the knee on the ground. Leaning to the outstretched leg and holding for 10–15s, then lateral twist to the flexed leg; the same movement for the other leg, three to four repetitions;

• Lying on the back, left leg on the ground and the right leg raised vertically and grasped with the hands; hold the position for 15s, three to four repetitions;

• Lying on the back, with (left) leg outstretched, and (right) leg outstretched, with right leg flexed and the knee brought sideways, pressing with hand on the same side and holding for 20s, three to four repetitions;

• Lying on the back, stretching the legs, torso, and arms in extension of the head holding for 20s, three to four repetitions.

Listening to different musical genres has strong motivational effects that are given by musicality, rhythm, cultural impact, and psychic associations. Each of these components brings benefits at different times of exercise: musicality is important depending on the time of the training session (warm-up, main part, cool down), contributing to better engagement in the effort, supporting the main effort, and then returning after the effort; the rhythm of the song is associated with the heart rhythm; also, the cultural impact has volitional effects, giving the individual the ability to continue the effort; and the psychic associations with the different musical genres ensure better adaptation and synchronization with the effort, and also the ability to perform an entire proposed program [31].

Various studies conducted over time have highlighted the benefits of associating music with exercise in all population categories regardless of age, sex, level of training, or level of fitness [32–35].

• Reduction of oxygen consumption (up to 7%) when making an effort with the same intensity, duration and complexity.

• Distracting attention from the feeling of fatigue, thus making the activity more comfortable and enjoyable.

• Increasing the motivational level by up to 15% regarding the practice of physical exercises and reducing the symptoms of fatigue and exhaustion.

• Stimulating effect at a psychomotor level.

• Increasing the level of concentration and attention during the exertion.

• Changes in the tempo of the same song (±10%) have significant effects on the intensity of the effort.

• Increasing the number of practitioners at the training sessions held with a musical background.

• Increasing the duration of the sessions due to a decrease in fatigue.

• Increasing the number of weekly workout sessions.

• The use of music is associated with fun, relaxation, and good mood.

• Increasing the intrinsic motivation of practitioners.

• Cardio effects, endurance effort that is associated with well-being and satisfaction.

• Improves positive moods (happiness, confidence) and tempers negative ones (anger, depression, fear).

• Better engagement in the warm-up part, followed by an optimal increase in heart rate in the main part, and a faster recovery after exertion in the cool down part.

• Increasing the intensity of the effort without being additionally felt by practitioners.

• Physiological beneficial effects: perception of effort at a lower level (measured on the Borg RPE scale), increase of resistance to effort, improvement of physical condition.

In order to obtain the beneficial effects of the use of music during physical exercises, it must be chosen according to the time of the session (warm-up, main part, cool down), and its characteristics must be in accordance with the particularities of each moment (warm up, gradual transition from rest to effort; main part, reaching the optimal level of exertion; cool down, accelerating recovery after the main effort). The music must be chosen according to the content to be used in practice (strength, endurance, speed, skill, cardio, fitness, stretching) with a strong motivational effect, and for the end part melodic lines must be chosen to induce a state of relaxation for practitioners. In order to obtain the positive effects of using music in while practicing physical exercises, it must be chosen according to the particularities of the group they work with (age, sex, and level of physical condition), so that practitioners feel its beneficial influences during workout sessions [36].

Regular practice of physical activities had many benefits for individuals: optimal health status, a good level of physical fitness, harmonious physical development, social and professional integration, good quality of life, well-being, mental balance, ability to complete daily tasks more easily, reduced incidence of diseases, and optimal body mass index.

In order to monitor the efficiency of an activity, it is recommended to use some electronic devices (bracelets, fitness trackers). These kinds of devices offer real-time information concerning the physical activity of the individual, such as caloric expenditure, number of footsteps, pulse, blood pressure, length of activity, and distance; there are also objective tools to assess the level of the exertion and allow individuals to observe more easily their progress.

This kind of information could help the elderly to be more motivated in practicing daily physical activities according to their personal ability (health status, physical fitness level). The recommendation is to practice outdoor physical activities, in nature, because it has many benefits for the body and mind.

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Chapter 2: Intelligent remote system for assessing a subject's health during sleep

Mihaela Hnatiuc, and Anastasia Cotov     Electronic and Telecommunication Department, Constanta Maritime University, Constanta, Romania

Abstract

A subject's behavior during bed rest gives information about their physical and mental health. This chapter presents a system for identifying the position of a subject during bed rest and the state of agitation using resistive sensors. The purpose of the sensor network is to generate the best possible response in the areas where the human body exerts greater pressure on the horizontal support on which it is located. The data obtained from the sensors are processed and classified using machine learning algorithms. Python in the Spyder Integrated Development Environment is used for data processing.

Keywords

Expert system; Neural network; Position identification; Subject behavior

1. Comfort and disease prevention using smart systems

2. Methods and algorithms used in data analysis

2.1 Artificial neural networks

2.2 Gradient descent and backpropagation

2.3 Stochastic gradient descent algorithm implementation

2.4 The KNN –(k nearest neighbors) algorithm

3. Project description

3.1 System description

3.2 Data collection

3.3 Data organizing

3.4 Building and training an ANN

3.4.1 Data preprocessing

3.4.2 ANN designing and training

4. Conclusions

References

Further Reading

1. Comfort and disease prevention using smart systems

For centuries, scientists have tried to create different systems to replace people's involvement in various activities. Today there is a wide variety of these, some of which have emerged due to the development of a new branch of science—artificial intelligence. Nowadays, the technical revolution of neural networks is spreading rapidly in various directions of human activity, proposing optimal solutions to solve various problems. Medicine is a very important field, in which scientists require research, talent, energy, and financial resources. Scientists are working to create devices in order to facilitate human activity and improve quality of life. Sleep quality is a branch of medicine that also has attracted the interest of scientists, because, using special equipment, it is possible to monitor patients in bed in a hospital or even at home. The latest innovations in sleep quality use machine learning algorithms, which take analog and digital data from various sensors to be subsequently processed [1, 2].

Body posture during sleep is one of the most important factors that determine the quality of sleep, reduce sleep disorders, and prevent the formation of eschars. Because the treatment of eschars, sleep disorders, etc., is extremely difficult and expensive, the ideal solution is their prevention. Moreover, researchers have found that sleep quality is related to sleeping position and frequent postural changes during sleep [3,4]. For example, snoring or regular body movements can lead to a shorter sleep duration. People who report an unsatisfactory sleep quality experience spend more time in a straight-headed position, and people who sleep on the left side have a significantly higher rate of nightmares (40.9%) than those who sleep on the right side [5]. That is why, hospitals use a set of equipment with established programs, which change the pressure of special pillows or mattresses to avoid the development of injuries to patients who are immobilized in bed. In addition, the position of the body during sleep of patients should be changed regularly, as a prevention of the development of ulcers due to pressure.

New mattresses are developed for comfort and take account of aging or are designed specifically for those with disabilities [6–9]. The described system is built with an air mattress divided into zones corresponding to the body, with each zone being inflated/deflated with a minicompressor, and the pressure sensors give information about the volume of air (Fig. 2.1).

Figure 2.1  The zones of the mattress with the pressure sensor values indicated for each zone [6].

The monitoring of the human body at rest is achieved with the help of a smart mattress that collects data from 10 resistive sensors—flex (FSR). This type of sensor is used in applications when it is necessary to measure the conforming and stresses of the mattress.

In this chapter a sensor system built into a mattress, which recognizes the posture of the human body, using a neural network and algorithm, is described. These algorithms run in Arduino and Spider integrated development environments (IDEs), using the Python and C programming languages.

The aim of the project is to design equipment made from materials, sensors, and microcontrollers that are accessible on the market and have a low cost. The device is designed to monitor people immobilized in bed or during sleep.

2. Methods and algorithms used in data analysis

2.1. Artificial neural networks

A neuron has n inputs that are the sensor values or the values sent from other neurons in the network. In the neuron itself, a mathematical function is computed and the result is transmitted to the output or to another neuron [10, 11]. The process described represents a single iteration (Figs. 2.2 and 2.3).

Figure 2.2  Artificial neural network with one hidden layer three basic elements of a neuron model is presented in Fig. 2.3.

Figure 2.3  Neural network.

1) A number of synapses characterized by a synaptic weight , where is the input signal corresponding to the synapse j, which is connected to the neuron k. If then the weight is excitatory, if then the weight is inhibitory.

    where k is the neuron number, and j is the input sensor number (input neuron that holds the sensor value).

2) An adder to sum the input signals weighted with synapse weights:

(2.1)

    Matrix form:

(2.2)

3) The activation function is used to limit the amplitudes of the output signals from the neuron. In general, the normalized amplitude of the signal at the output of the neuron is between the values [0, 1] or [–1, 1].

(1.3)

2.2. Gradient descent and backpropagation

The gradient descent algorithm adjusts the values of the weights, through an algorithm called backpropagation, until the value of the error function (cost function) becomes minimal or optimal [12–14]. The backpropagation learning algorithm is based on the concept of an error correction learning algorithm. In general, the process of an error correction algorithm in the backpropagation consists of two steps that run the entire neural network, in opposite directions: forward propagation and backward propagation.

Forward propagation—compiling the neural network in the direction of the output (Fig. 2.4), the input vector is applied to the sensory nodes of the network, and its effect can be propagated through the network, layer by layer. At the final neuron(s), a set of outputs is obtained as a real response of the network (y_k outputs).

During forward propagation, the synaptic weights of the network are all fixed, the signals are processed based on the transition from neuron to neuron. The signal flow through the network progresses in a forward direction, from left to right.

Figure 2.4  Forward propagation.

The neural network is compiled as shown in Fig. 2.4, in the direction of the yellow arrow until the final step of the first stage is calculated (y k ), namely the cost function, denoted by C, which represents the difference between the predicted value of the network and the real value y r . If the value of the error is known, the accuracy of the function can be found. An ANN is better trained when the value of the cost function is the lowest possible. If C= 0, then ANN has the best accuracy of 1 (100%).

The purpose of the gradient descent algorithm is to evaluate the optimal cost, namely the smallest possible error between the ANN predicted value and the real value. When one iteration occurs, the formula for the cost function represents an average function, where the difference between y k   − y r is the error of the ANN (2.4).

(2.4)

If there are m iterations in the training algorithm, then there are two methods to determine the cost function: after each iteration i (2.5) or after m iterations (2.7).

(2.5)

(2.6)

In the case of stochastic gradient descent, to find the optimal value of the cost function, this function is calculated at each iteration and accordingly, the synaptic weights are recalculated at iteration i from a neuron k according to formula (2.8), where α is the constant defining the step of learning.

(2.7)

The cost function is convex, therefore, the points on the left side of the optimal minimum value have a tendency to descend to the right direction, and those on the right side descend to the left direction. This tendency is due to the fact that the derivatives are negative on the left side of C minimum and positive on the right.

For example, for and the following formulas are used:

(2.8a)

(2.8b)

Back pro pagation—the algorithm that runs the neural network in the opposite direction, from output to input, and all synaptic weights are adjusted according to the error correction rule. Specifically, the network response is subtracted from a real (target) value to produce an error signal based on which the cost function is calculated. This error signal is then propagated back through the network, in the direction of the synaptic connections—hence the name error back propagation After evaluating the cost and error function, the values of the synaptic weights are adjusted to minimize the subsequent error and to obtain a y k closer to the real value y r .

The direction of compiling and the operations performed in the algorithm to adjust the weights after a single iteration i are presented below. After finding the value of the cost function according to formula (2.7), in the first step of the algorithm, the cost function is derived with respect to the predicted network value y k of the iteration i in formula (2.10). In the next step the cost function is derived with respect to the sum of the weights v k in relation (2.10), in the last step the cost function is derived with respect to the synaptic weight, and the values of the derivatives with which the weights are adjusted are obtained, as in the case of formulas (2.8a and b).

Figure 2.5  Back propagation.

(2.9)

(2.10)

(2.11)

Fig. 2.5 shows the step diagram of the back-propagation algorithm for a single iteration and a single neuron k, with the respective formulas, described above.

2.3. Stochastic gradient descent algorithm implementation

The stochastic gradient descent algorithm consists of completing the seven steps with a data file, on the basis of which the artificial neural network is trained. The data supplied to the artificial neural network must be preprocessed to avoid errors during the learning process. All data require scaling to avoid the dominance of one variable over another. During preprocessing, the data are divided into two classes for training and testing. Then the accuracy of the response prediction is calculated by the created network.

2.4. The KNN –(k nearest neighbors) algorithm

In deep learning, the KNN algorithm is a method utilized for classification and regression problems. In the case of a regression problem, the output is the value of the current object and it is calculated as the mid-value of k nearest neighbors. If it is a classification problem, the output is the class of the training set [ 15–18 ].

3. Project description

The posture of the body during bed rest represents one of the most important aspects that determines the quality of sleep. In order to obtain the best possible response from the FSR sensors network built into the mattress, these sensors are placed in areas where the human body exerts greater pressure on the horizontal support on which it is located. Ten Flex sensors are placed in the five zones, with two sensors for each zone.

3.1. System description

Ten Flex sensors, placed in the most important regions of the mattress are connected in parallel (Fig. 2.6).

The resistance of the sensor changes depending on the bending, this sensor is represented in the wiring diagram as a variable resistor, a potentiometer. Each of the 10 sensors, together with a 10kΩ resistor, forms a voltage divider. With the voltage divider formula, each voltage divider is powered by a 5V DC voltage provided by the Arduino Mega development board.

In order to detect a change in the sensor state, it is sufficient to observe the change of the voltages at the terminals of the variable resistor. Therefore, in order to monitor the evolution of the state of the 10 sensors, it is sufficient to read the voltage at the terminals of each sensor. If the sensor bends, the resistance increases, as does the voltage because it depends linearly on the resistance (Ohm law).

The voltage level on each potentiometer is converted into digital values by the 10-bit analog-to-digital converter built in an Arduino Mega board. Thus, using Arduino IDE, we read data from the analog inputs of the board with values between 0 and 1023, where 0 corresponds to no voltage on the resistor, and 1023 corresponds to a value of 5V (Fig. 2.7).

3.2. Data collection

The efficient training of a neural network is possible due to the data table, which contains the values of the 10 sensors at a certain time, together with the real response and the position of the body on the mattress at that point in time. Having the input values (data from the sensors) and real output values with which, later, the predicted result of RNA can be compared, we obtain the supervised learning algorithm.

In order to have a well-trained network, we create a data table with 12,000 rows and 11 columns. The first 10 columns show the 10 sensor values, and the last column is reserved for the actual output value. Therefore, each row represents an iteration of the artificial neural network.

Figure 2.6  Electric circuit of the Flex sensors system placed in a mattress.

Figure 2.7  The mattress system.

The output data have four possible values, each characteristic of a position of the person immobilized in bed:

• Dreapta—when the person is resting on the right side of the body;

• Stânga—when the person is resting on the left side of the body;

• Spate—when the person is resting on their back;

• Nimeni—when no person is on the mattress.

First, to create the data table we need data which are collected by the Arduino IDE, with code being written in the C language, for reading the ADC (analog-to-digital converter) states on the 10 analog pins that are connected to the sensors. For each of the four output states, a total of 3000 pieces of data are collected, and the values of the sensors and the output (state) are displayed in the serial monitor of the software (Fig. 2.8).

To remove the noise the moving average filter (MAF) preprocessing filtering method is used. At the output of the filter results a mediated value, in our case MAF, takes a number of m =10 input samples and calculates their average.

3.3. Data organizing

All data are written in tabular form, in a csv file—comma-separated values—to be evaluated by the training algorithm of the artificial neural network. This type of document is created when there is a necessity to process a large amount of data.

First, a text file is created, where all the values of the comma-separated (or semicolon-separated) variables are stored. After that, this file is converted into a DATA file (Fig. 2.9) and loaded into an Excel file by changing the comma separation to column separation. In this mode, we obtain a table with 11 columns and 13,000 rows.

The table in Fig. 2.9 contains an example of data used for deep learning, using the python programming language in Spyder IDE.

3.4. Building and training an ANN

3.4.1. Data preprocessing

Before the algorithm is executed, the collected data are preprocessed. Therefore, the data table is imported into Spyder IDE, before preprocessing, and the matrices X and y are defined, where X corresponds to the input and y values of the output ones (Fig. 2.10).

Figure 2.8  Code sequence and data displaying.