scholarly journals Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1802 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes ◽  
Damir Torrico ◽  
Frank Dunshea
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Heart Rate ◽  
Video Analysis ◽  
Sensory Responses

scholarly journals Acute Hemodynamic Changes Associated with Zumba and ZOCA Dance Exercises among Females of Selected Gyms in Lusaka, Zambia

2020 ◽  
Vol 2 (1) ◽  
pp. 84-91
Author(s):  
Chanda Grace Chisunka ◽  
◽  
Gibson Sijumbila ◽  
Fastone Goma ◽  
◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Systolic Blood Pressure ◽  
Hemodynamic Changes ◽  
Aerobic Dance ◽  
Arterial Blood ◽  
Effective Dynamic ◽  
Blood Pressure Monitors ◽  
Blood Pressures

Background: Dynamic exercises are known to elicit hemodynamic changes such as an increase in arterial blood pressure and heart rate. Zumba and ZOCA are part of a fast growing group of dance fitness programmes designed to provide a cardiovascular dynamic workout. Despite their growing popularity, very few studies have been done to provide knowledge regarding the hemodynamic changes associated with these exercises. Methods: Case study in which 27 females took part in either a Zumba or ZOCA class. Using digital blood pressure monitors, recordings of blood pressure and heart rate were taken, firstly, before commencement of the exercise, secondly, after 30 minutes after exercise and thirdly, at the end of the class.Results: Mean baseline blood pressures were 118 (SD = 14) mmHg and 77 (SD = 7) mmHg, systolic and diastolic blood pressures, respectively. After 30 minutes of dancing, mean systolic blood pressure increased to 130 (SD = 19) mmHg (p˂ 0.05) while diastolic blood pressureonly rose to an average of 80 (SD = 8) mmHg (p˃ 0.05). At the end of the class (after the cool down phase) mean systolic blood pressure reduced to 109 (SD = 13) mmHg (p˂0.05) while diastolic blood pressure reduced to 74(SD = 12) mmHg (p˂ 0.05). Conclusions: Zumba and ZOCA elicited significant hemodynamic changes that can be attributed to these exercises stimulating the cardiovascular regulatory mechanisms (e.g central command and exercise-pressor) sufficiently and hence resulting in autonomic adjustmentsthat were concurrent with effective dynamic exercise. Keywords: Blood Pressure, Heart Rate, Aerobic, Dance Exercise

scholarly journals Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

2020 ◽  
Vol 10 (5) ◽  
pp. 4615
Author(s):  
Eisha Akanksha
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Cost Effective ◽  
Learning Approach ◽  
Contextual Data ◽  
Aggregated Data ◽  
Machine Learning Approach ◽  
Close Relationship ◽  
Control Message

Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.

scholarly journals Benefits of Nanogold Injections to Treat Rheumatism Arthritis Complaints: A Case Study on Covid19 Clinical Test Volunteers

2021 ◽  
Vol 328 ◽  
pp. 01017
Author(s):  
Titik Taufikurohmah ◽  
Rusmini Rusmini ◽  
Djodjok Soepardjo
Keyword(s):  
Rheumatoid Arthritis ◽  
Blood Pressure ◽  
Heart Rate ◽  
Clinical Trials ◽  
Healthy People ◽  
Clinical Test ◽  
Average Heart Rate ◽  
Rheumatic Arthritis ◽  
Normal Ranges

The reduction in rheumatoid arthritis was measured by the decrease in the diameter of the joint swelling expressed in percent. The Covid-19 status was obtained from the results of the PCR Swab. The results show that in general, COVID-19 volunteers with complaints of rheumatic arthritis are satisfied with nanogold injections because they reduce pain. Health in general; temperature, blood pressure and average heart rate during clinical trials within the normal ranges of healthy people. Volunteers for clinical trials during treatment were declared negative for COVID-19. It is proven that nanogold injections increase the immunity of Covid-19 volunteers with complaints of rheumatic arthritis. As for rheumatic arthritis complaints in general, all volunteers experienced a decrease. The decrease in swelling diameter of each volunteer differed from one another in the range of 20-36%. With reduced volunteers can stand straight, walk normally and no longer hurt when the joints are pressed.

Machine Learning Based IoT Geriatric Fall Intelligent System in Pandemic (Preprint)

2021 ◽  
Author(s):  
Gowri R ◽  
Rathipriya R
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Heart Rate ◽  
Real Time ◽  
Old Age ◽  
Sensor Data ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
The Real ◽  
Early Fall

UNSTRUCTURED In the current pandemic, there is lack of medical care takers and physicians in hospitals and health centers. The patients other than COVID infected are also affected by this scenario. Besides, the hospitals are also not admitting the old age peoples, and they are scared to approach hospitals even for their basic health checkups. But, they have to be cared and monitored to avoid the risk factors like fall incidence which may cause fatal injury. In such a case, this paper focuses on the cloud based IoT gadget for early fall incidence prediction. It is machine learning based fall incidence prediction system for the old age patients. The approaches such as Logistic Regression, Naive Bayes, Stochastic Gradient Descent, Decision Tree, Random Forest, Support Vector Machines, K-Nearest Neighbor and ensemble learning boosting techniques, i.e., XGBoost are used for fall incidence prediction. The proposed approach is first tested on the benchmark activity sensor data with different features for training purpose. The real-time vital signs like heart rate, blood pressure are recorded and stored in cloud and the machine learning approaches are applied to it. Then tested on the real-time sensor data like heart rate and blood pressure data of geriatric patients to predict early fall.

Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment

2021 ◽  
pp. 1-3
Author(s):  
Daniel S. Barron
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Heart Rate ◽  
Oxygen Saturation ◽  
Respiration Rate ◽  
Clinical Setting ◽  
Data Science ◽  
Data Gathering ◽  
Clinical Interview ◽  
Ethical Challenges

The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that ‘vitals’ are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxygen saturation. There are as many ways to approach a clinical interview as there are psychiatrists; and trainees can learn as many ways of performing and formulating the clinical interview as there are instructors (Nestler, 1990). Even in the same clinical setting, two clinicians might interview the same patient and conduct very different examinations and reach different treatment recommendations. From the perspective of data science, this mismatch is not one of personal style or idiosyncrasy but rather one of uncertain salience: neither the clinical interview nor the data thereby generated is operationalized and, therefore, neither can be rigorously evaluated, tested, or optimized.

scholarly journals Heart rate estimation from speech signals using machine learning and cloud computing

2020 ◽  
Author(s):  
Mohammed Usman
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Heart Rate ◽  
Cloud Computing ◽  
Early Diagnosis ◽  
Vital Signs ◽  
Machine Learning Techniques ◽  
Speech Signals ◽  
Learning Techniques ◽  
Heart Rate Estimation

Speech signals of individuals contain informationrelated to their physical, mental as well as emotionalhealth. A first step in clinical diagnosis is to measure vitalsigns, which provide an indication of vital body functions.The four vital signs - heart rate, blood pressure, body temperatureand respiratory rate are useful in assessing the healthof an individual and early diagnosis of deteriorating healthconditions. The objective of this work is to measure heartrate of individuals based on their speech signal using signalprocessing and machine learning techniques.

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