Monitoring Our Health
- Isle Bee Well
- Nov 18, 2025
- 21 min read
Updated: Jan 3
In the tri-island state of Grenada, Carriacou and Petite Martinique, the top cause of death from non-communicable diseases has steadily been Cardiovascular Disease (CVD), otherwise known as Heart disease. For a population around 117,000, more than 80% of all deaths on the island of is by caused by ischaemic heart disease– blood vessel blockages that supply the heart, better referred to as Coronary Artery Disease (CAD). Vessel blockages mainly from cholesterol build is the most prevalent contributor of CVD, beside structural deformations like stiff or leaky heart valves; irregular heart rhythms or arrythmias; and heart defects from birth congenital heart disease. Heart disease is commonly accompanied by other chronic diseases or comorbidities, such as Hypertension, Diabetes and Obesity, which contribute to this high mortality rate (World Health Organization - WHO, Grenada Health Statistics).
Lifestyle choices have long been the main determinant of both health and disease. Chronic, non-communicable diseases (NCDs) as those mentioned prior, pose lifelong challenges for people and the health sector. Chronic patients experience lower qualities of health and life, while their pharmaceutical management places burden on the sector and wider economy. Detection and diagnosis of Cardiovascular disease is often at its late-onset, most times after emergencies such as a cardiac arrest or heart attack. At the hospital, analysis of cardiovascular function is primarily done by the gold-standard Electrocardiogram (ECG or EKG), a bio-electrical analysis method for capturing heart function. Hospital-grade ECG monitors were meant for the clinical setting with high accuracy and reliability, although not readily accessible or affordable for use at-home for early analysis.
Empowering individuals to monitor their heart health from anywhere, espcially at-home, could improve early detection of heart health indicators. In 2025, Grenada's Ministry of Health (MoH) adopted the global HEARTS Initiative to improve on the early monitoring, detection and management of cardiovascular complications, aligning with 33 other countries globally on this initiative. In this light, this blog article aligns with Grenada's MoH for early heart health monitoring by proposes the potential of a Home Wellness Monitor, HiveHome Sense, for early heart analysis that is affordable and accessible for family members. Featured wellness monitoring parameters in our wellness station include: Pulse Oximetry, Body Temperature, Blood Pressure, EMG for muscle strength monitoring, EEG for mindfulness tracking and exercises, and ECG for heart health analysis. This wellness monitor is meant to securely integrate into Isle Bee Well's smart home hub via authorization and authentication login as well as end-to-end encryption. Complementing this wellness station is a health wearable smartwatch to non-invasively track and analyze bioparameters such as: Heart Rate, Body Temperature, Blood Pressure, Respiration, Blood Glucose, Blood Lipid, Uric Acid, Sleep cycles as well as ECG function with Artificial Intelligence (AI) and Machine learning capabilities for predictive instead of reactive health insight.
From Patient Reports to Biometric Insights
Monitoring our health and wellness has moved beyond subjective emotional states, to Biometric, data-driven insights. Technology and its advances have been influencing the field of Healthcare and Medicine, empowering clinicians and patients alike to be able to quantify their health status. The synergism of Quantitative health metrics along with Qualitative subjective patient reports is widening the health and wellness aperture. Broadened awareness into the health and wellness of individuals informs clinical discernment and health management outlook allowing for preventative care over diagnostic interventions. Biomedical engineering is a vital subdiscipline within the broader field of Bioengineering that concentrates on addressing complex challenges in healthcare and medicine through the innovative application of engineering principles.
This interdisciplinary field combines knowledge from biology, medicine, and engineering to develop technologies and devices that enhance patient care and improve health outcomes. With the rapid advancement of technology, data-driven insights are increasingly enabling healthcare professionals to make more informed clinical judgments and prognoses. This evolution in medical practice is transforming how clinicians plan and manage patients' health, leading to more personalized and effective treatment strategies. A critical aspect of this process is Signal Acquisition, which serves as the foundational step for capturing various physiological metrics that are essential for monitoring a patient's health status. This involves the use of Biosensors, devices designed to detect and measure biological signals that speak to one's health. These biosensors can monitor a wide range of physiological parameters, including heart rate, blood pressure, glucose levels, and more. Accurate and repeatable acquisition of these signals is paramount, as it lays the groundwork for subsequent analysis and interpretation. Signal Transduction is a key mechanism that empowers biomedical engineers to convert raw physiological signals into a format suitable for analysis. For example, signals related to blood vessel variability, unique chemical markers such as oxygen saturation levels, body vibrations, or the expansion of the chest during breathing are transformed into electromagnetic data.
Each of these transduced signals possesses a unique frequency signature, which can provide critical insights into an individual's health. However, it is important to note that physiological signals can often appear messy and entangled due to noise and interference from various sources, making the analysis more challenging. To address these challenges, Signal Processing plays a crucial role in the biomedical engineering workflow. This process involves the Decomposition, Filtering, Conditioning, and Amplification of specific frequencies of interest within the acquired signals. By applying sophisticated algorithms and techniques, biomedical engineers can isolate relevant information from the noise, allowing for a clearer interpretation of the underlying physiological phenomena. This refined data can then be subjected to Feature Extraction methods, which further distill the information into meaningful metrics that can be analyzed for clinical relevance.
These algorithms may include Machine Learning and Artificial Intelligence techniques to recognize patterns and trends within the data, leading to Predictive Analytics that can foresee potential health issues before they become critical. The integration of such technologies in biomedical engineering not only improves the accuracy of health assessments but also empowers healthcare providers to make timely interventions, ultimately enhancing patient care and outcomes. The interplay between biosensors, biosignal processing, and biomedical engineering represents a dynamic and rapidly evolving field. As technology continues to advance, the potential for innovative solutions that can improve healthcare delivery and patient outcomes is vast. Harnessing the power of engineering principles and data analytics, biomedical engineers are at the forefront of transforming healthcare, paving the way for a future where Personalized Medicine becomes the norm.
A promise of convenient and affordable family genomic sequencing exists with modern sequencing technologies and bioinformatics tools. Families could have affordable access to high-fidelity DNA sequencing that was previously difficult to achieve, unlocking this technology for a variety of applications including personalized medicine, hereditary conditions, or ancestry tracking. Monitoring our health enables and powers individuals to prioritize health and well-being in order to move with intent into higher states of holistic wellness.
Health Tracking Wearables
Bioparameters such as Heart Rate (HR/BPM), Body Temperature, Blood Pressure, Respiration, and Sleep cycles have empowered personal health tracking via Wearables. Health Wearable Technology has leveraged advancements in Biosensor and Biosignal Processing devices have unlocked additional health parameters such as Blood Glucose, Blood Fat and Uric Acid scores. These basic vital health screeners are now accessible to individuals as reference health data and are safely considered by major medical device regulators to be wellness devices. Furthermore, the inclusion of an Electrocardiography (ECG) function to a health wearable, is an impressive task which health wearable technology company's like Quantyvo, have accomplished. With the associated health and wellness tracking app, a person's health data can be analyzed and pattern-trained over time using Artificial Intelligence (AI) and Machine Learning (ML), to build Artificial Neural Networks. Health patterns and correlations can be recognized and predicted from individual behaviours and moods, positively reinforcing the artificial neural networks.
Photoplethysmography (PPG) in Healthwearables
Photoplethysmography, commonly referred to as PPG, is a non-invasive optical technique that employs biosignal processing methodologies to assess various physiological parameters. This innovative technology primarily focuses on measuring the dynamic changes in volume and flow within the body, particularly concerning blood circulation and respiratory volume fluctuations. By utilizing light absorption properties of tissues, PPG can effectively capture the pulsatile nature of blood flow, providing valuable insights into cardiovascular health. The fundamental principle behind PPG involves illuminating the skin with a light source, often a Light-emitting Diode (LED), and detecting the amount of light that is either transmitted or reflected back to a Photodetector. As blood volume changes with each heartbeat, the absorption characteristics of the tissue also vary, leading to fluctuations in the detected light intensity. These variations are then processed using sophisticated algorithms to extract meaningful data regarding heart rate, blood oxygen saturation, and even respiratory patterns.
One of the significant advantages of PPG is its ability to be integrated into wearable technology, such as smartwatches and fitness trackers, making it accessible for Continuous Health Monitoring. This capability allows users to track their cardiovascular health in real-time, providing insights that can help in the early detection of potential health issues. Furthermore, PPG is not limited to cardiovascular monitoring; it can also be utilized in various medical applications, including assessing peripheral blood flow, detecting arrhythmias, and monitoring respiratory conditions. Advancements in signal processing techniques have enhanced the accuracy and reliability of PPG measurements. The integration of machine learning algorithms and advanced filtering methods has significantly improved the signal quality, allowing for more precise analysis and interpretation of the data collected. As a result, researchers and healthcare professionals can potentially utilize PPG for a broader range of applications, including personalized health management, telewellness, and possibly clinical diagnostics.
From Lab to Wrist: Biochemical to Light Sampling
The traditional means of collecting health data from patients at clinics and hospitals, have been through biochemical sampling procedures, such as a Fasting Blood Glucose test for tracking HBA1C levels in the blood or Uric Acid, Cholesterol or Liver function tests also require blood samples. These body samples have to be analyzed in a medical laboratory, written up and then transferred to clinicians and/or patients as part of a medical checkup. Modern technological advances in healthcare are facilitating faster, accurate and non-invasive tests, bypassing the need for needle pricks and other inconvenient sampling procedures. The use of bioinert light waves of varying wavelengths and frequencies, emitters, detectors and biosignal processors have unlocked non-invasive sampling alternatives. Biosamples are detected and measured based on Light Absorption. Much like a Spectrophotometer apparatus used in many chemical laboratories, the quantity of a particular blood sample can be determined by the amount and frequency of light absorbed by the sample in question.
HiveHome Sense: Health Monitoring at Home
ECG, or Electrocardiography monitoring, has traditionally been associated with visits to the cardiology departments of hospitals and health clinics, where patients undergo extensive testing to assess heart health. This process often involved lengthy wait times and the inconvenience of travelling to medical facilities. The Electrocardiogram itself is a sophisticated biosignal capturing and signal processing device designed specifically for measuring the electrical activity of the heart. It provides crucial insights into heart function by recording the electrical impulses that trigger each heartbeat. The standard 12-lead ECG has long been the benchmark in this field, as it captures a comprehensive and detailed physiological biosignal from various angles of the chest, allowing healthcare professionals to diagnose a wide range of cardiac conditions, from arrhythmias to ischemic heart disease. However, with the advancements in technology, particularly in the realm of integrated circuit (IC) chips, the field of healthcare is witnessing a transformative shift in how ECG monitoring is conducted. These innovations have led to the miniaturization of ECG sensors, making it feasible to develop portable and user-friendly devices that can be used in the comfort of one’s home. In some cases, these modern ECG devices require fewer electrode leads—often as few as one or two—yet still manage to capture heart electrical activity effectively and reliably. This reduction in leads not only simplifies the setup process for users but also enhances the convenience of monitoring heart health on a regular basis.
The implications of accessible ECG monitoring at home are profound. For individuals with chronic heart conditions, this means they can continually monitor their heart health without the need for frequent hospital visits. Patients can track their heart rhythms and report any irregularities to their healthcare providers in real-time, facilitating timely interventions when necessary. Moreover, for those who may be at risk of developing heart disease, having a home ECG monitoring system allows for proactive management of their health, empowering them to take charge of their cardiovascular wellness. Additionally, these at-home ECG devices often come equipped with advanced features such as Bluetooth and Wifi connectivity, enabling seamless data transfer to smartphones or tablets. This connectivity allows users to easily share their ECG readings with healthcare professionals, who can provide personalized feedback and recommendations based on the data collected. Furthermore, many of these devices are designed with user-friendly interfaces, making them accessible to individuals of all ages, including the elderly, who may find traditional ECG monitoring intimidating or difficult to navigate.
The technological advances of ECG monitoring technology is paving the way for a new era of cardiac care. Accessibility of home-based ECG monitoring not only enhances patient convenience but promotes a more proactive approach to heart health management. As these technologies continue to advance, we can anticipate even greater integration of ECG monitoring into everyday life including on smartwatches and health wearables, ultimately leading to improved outcomes for patients and a deeper understanding of heart health on a population level.
The AD8232 ECG monitoring module enables the acquisition of biopotentials from the heart, amplifying those signals and filtering out noise. The AD8232 ECG module is a very affordable single lead, Analogue to Digital signal converter and signal conditioner designed for application in non-medical contexts. When properly integrated with Microprocessors such as the Raspberry Pi or Microcontrollers like Arduino or ESP32, and programmed using Python or C/C++ respectively, ECG monitoring at home become possible. To be used as a wellness reference, this ECG module could potentially offer early insights into heart health, possibly indicating unusual ECG waveform patterns that deviate from clinical heart health indicators, such as the 'PQRSTU complex'. Early spotting of an abnormal ECG trace at home could be referenced to inform decisions for consultation by clinicians for advanced medical-grade review and intervention.
Heart Electrophysiology for Health Monitoring
An Electrocardiogram (ECG) waveform allows for the visualization of the heart's electrical activity. Electrodes placed at key body locations, most commonly the chest region can detect biopotentials. The first ECG apparatus was developed by Willem Einthoven, using the inverted triangular shape created from arms and feet as contact points for measuring biosignals. Biopotentials from the heart space undergoes biosignal processing steps, to amplify, filtering and extract key waveform features to name a few. Heart electrophysiological activity represents the Depolarization and Repolarization of electrochemical channels found in cardiac muscle that correlate with the Contraction and Relaxation of heart muscle, creating pump for blood flow. The rhythmic contraction from depolarizing ion-gated channels along with cardiac resting periods when heart cells repolarizing and reset, sets up a pumping action that pushes blood through the Atrioventricular heart camber to be oxygenated by the lungs, to supply nutrients to the whole body and transport immunological players like platelets and white blood cells for immune defence.
An ECG waveform exhibits characteristic bumps, dips and spikes when visually observed, reflecting dynamic heart activity. Key points on the ECG waveform is annotated by clinicians as 'PQRST' waves, complexes and segments. The P wave signifies Artrial Depolarization; QRS complex represents Ventricular Depolarization and T wave as Ventricular Repolarization; PR segment reflecting the time taken for the electrical signal to propagrate from Atrial chambers down through the Purkinje fibers and Bundle of His, finally to the Ventricular chambers; and ST segment signifying the ventricular reset period after ventricles have contracted and when they repolarize. Notably, Atrial Repolarization is not captured on the most ECG traces, since repolarization of the atria takes place simultaneously with Ventricular Depolarization during the prominent QRS phase, masking Atrial Repolarization.
Heart health risks such as irregular heart rates or Arrythmias (Atrial Fibrillation, Tachycardia, Bradycardia); poor blood supply and oxygenation to the heart by coronary arteries (Myocardial Infarction, Coronary Artery Disease); and structural abnormalites like Cardiomegaly or enlarged heart, Cardiomyopathy from stiff heart muscles, heart valve abnormalities such as Stenosis, narrow value blood flow or Regurgitation, incomplete heart value closure.
Measuring EEG Brain Waveforms
Electroencephalography (EEG), is a bioinstrumentation measuring technique utilized in Neuroscience and Neuropsychology for capturing the real-time electrical activity of the brain and bioprocessed into a brain Wave Function. The basis of an EEG is predicated on Electromagnetic Frequency (EMF) theory, which stipulates that everything and every activity has a frequency signature. Waves are characterized by the hieght of their peaks or depth of their troughs (Amplitude), duration of time between wave amplitudes (Frequency) measured in wave cycles per second or Hertz (Hz). Voltage differences across brain regions during various brain states exhibit unique frequency band patterns. Signal Amplification, Decomposition, and Filtering make identifying unique brain frequency bands possible from a complex brain EMF cluster.
These frequency bands range from short wavelength (λ) frequencies to longer wavelengths and include five (5) major frequency bands, namely: Gamma, Beta, Alpha, Theta and Delta, respectively. Electrodes placed at key cranial spots can detect brain frequencies and real-time brain function, offering insight into mood, focus level and mindfulness states. Biofeedback approaches to holistic wellness as seen with EEG monitoring, allow family members at home for instance, to track, monitor and co-participate in regulating their mood and brain states at will with focus training games. Brain states are exemplified in the five (5) major brainwave forms, with Gamma frequencies (30Hz - 100Hz) reflecting hyperawareness, attention and focused learning. Beta wave frequencies (12Hz - 30Hz) are present during alertness, active listening, thinking and stressful states. Alpha frequencies (8Hz - 12Hz) can be observed during calm alertness, creative practices, yoga and deep breathing. Theta wave frequencies (4Hz - 12Hz) reflect subconscious states, lucid dreaming, deep meditation and REM sleep. Finally, Delta frequencies (0.5Hz - 4Hz) are present during deep unconscious sleep states, whole body recovery and tissue regeneration.
Telewellness for Informed Health Management
Modern advancements in health technology are transforming the way we approach healthcare, particularly through the innovative concept of Telewellness. This approach is allowing for long-distance tracking and monitoring of vital health metrics, which can significantly enhance the ability of clinicians to assess and understand the day-to-day health and well-being of their patients. With the integration of sophisticated devices such as smartwatches, fitness trackers, and other wearable technologies, individuals can continuously collect and transmit valuable health data, including heart rate, physical activity levels, sleep patterns, and more. One of the most transformative aspects of telewellness is the ability to synchronize and transfer health data from these wearables directly to Electronic Health Records (EHRs). This capability not only streamlines the process of data collection but also facilitates a centralized management system for patient health information. Clinicians can access real-time data, enabling them to make more informed decisions regarding treatment plans and interventions. This immediate access to comprehensive health profiles allows for a more proactive approach to patient care, where potential health issues can be identified and addressed before they escalate into more serious conditions.
Moreover, the integration of telewellness into healthcare practices encourages a more collaborative relationship between patients and healthcare providers. Patients become active participants in their own health management, equipped with tools and knowledge to monitor their well-being effectively. This empowerment can lead to improved adherence to treatment regimens, greater engagement in preventive care measures, and ultimately, better health outcomes. As patients regularly share their health metrics with their providers, clinicians can tailor recommendations based on individual data, fostering a personalized approach to healthcare. In addition to improving patient-provider interactions, telewellness also holds the potential to enhance public health initiatives. By aggregating data from a large population, health organizations can identify trends and patterns that may indicate emerging health concerns or outbreaks. This data-driven approach can inform community health strategies and resource allocation, ensuring that interventions are timely and effective. Furthermore, the ability to monitor health metrics remotely can be particularly beneficial for individuals in rural or underserved areas, where access to healthcare facilities may be limited. The evolution of telewellness through modern health technology is paving the way for a more informed and efficient approach to health management. As we continue to embrace these advancements, the healthcare landscape is poised to become more responsive, personalized, and accessible, ultimately leading to improved health outcomes for individuals and communities alike.
Patient Health Information and Health Data Security
The boon of digitized health data from health wearable technologies, telewellness, and the Internet of Things (IoT) has transformed the landscape of healthcare, offering unprecedented opportunities for monitoring patient health remotely. However, this advancement in technology comes with the serious risk of data breaches, posing serious threats to patient privacy and the overall security of health information systems. As healthcare organizations increasingly rely on electronic health records (EHRs) and interconnected devices, the potential for unauthorized access and cyberattacks has escalated dramatically. Cybersecurity attacks from threat actors can affect the Confidentiality, Integrity, and Accessibility of health data, commonly referred to as the CIA triad.
Confidentiality ensures that sensitive patient information is only accessible to authorized individuals, safeguarding it from prying eyes. However, with the rise of sophisticated hacking techniques, including phishing schemes and ransomware attacks, maintaining confidentiality has become a daunting challenge for healthcare providers. Data breaches can lead to the exposure of Personal Health Information (PHI), which can have devastating consequences for patients, including identity theft and financial fraud. Integrity of health data is equally critical, as it pertains to the accuracy and reliability of the information recorded and shared among healthcare professionals. Any alteration or corruption of this data, whether intentional or accidental, can lead to misdiagnoses, inappropriate treatment plans, and ultimately, compromised patient safety. For instance, if a hacker manipulates a patient's medication record, it could result in administering incorrect dosages, which could have life-threatening implications. Accessibility refers to the ability of authorized users to access health data when needed, ensuring that healthcare providers can deliver timely and effective care. Cyberattacks that disrupt access to health information systems can hinder patient care, delay critical medical interventions, and create chaos within healthcare facilities.
To combat these threats, healthcare players must adopt robust cybersecurity measures that include advanced encryption techniques, regular security audits, and comprehensive training programs for staff on recognizing and responding to potential security threats. Additionally, developing a culture of security awareness among healthcare providers and patients is essential, as human error often plays a significant role in data breaches. Furthermore, regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States set stringent standards for protecting patient health information. Compliance with these regulations not only ensures legal protection but also reinforces patient trust in the healthcare system. As the digital landscape continues to evolve, healthcare stakeholders must remain agile, continuously updating their security protocols to address emerging threats and safeguard the health data of their patients.
While the digitization of health data through wearable technologies, telewellness, and IoT presents remarkable benefits for patient care and health management, it simultaneously introduces significant risks related to data security. The ongoing challenge for healthcare providers is to strike a balance between leveraging technology for improved patient outcomes and implementing effective measures to protect sensitive health information from cyber threats.
Genomic Sequencing for Tracking Wellness
Our vision for monitoring the health of the family encompasses genetic screening. The genetic code, which is the basis of all life, including human life, can offer deep Quantitative and Qualitative insight into our health and wellness status. The human genome, decoded in 2003 by the Human Genome Project, is comprised of the molecule Deoxyribonucleic Acid (DNA) in all 23 pairs of chromosomes. DNA is a complementary, double-stranded, antiparallel, helical biomolecule with a Phosphate backbone, a Nitrogenous base and a Pentose or 5-carbon sugar. The phosphate backbone of DNA strands serves as a scaffolding framework to support the four nitrogenous bases: Adenine (A), Thymine (T), Guanine (G) and Cystosine (C). These bases when chemically bonded to a deoxyribose sugar are termed Nucleosides; when nitrogenous bases are bonded to a 5-carbon sugar and phosphate group the cluster is referred to as Nucleotides. These nucleotides rivet or bind the two strands of a DNA molecule by always pairing base pairs A-T and G-C throughout the genome, a characteristic known as Complementarity. The genome can be considered as a USB thumb drive, having the capacity to store, transmit and edit information. About 98% of the human genome is non-coding, and only 2% reflects coded regions for traits (Alleles) for skin colour, hair type or heart health predispositions. Genomic sequencing offers high-accuracy insight into health and disease patterns by leveraging Bioinformatics tools to perform big data computational biology on the 6 billion total base pairs of our genome. Modern lost cost and rapid genomic sequencing analysis holds the promise of being accessible and affordable for families who are subscribed to our Hive Home Eco-Wellness platform.
By adopting modern, innovative genomic sequencing technologies like nanopore sequencing platforms, such as those developed by Oxford Nanopore Technologies (ONT), families could have access to high-fidelity DNA sequencing that was previously difficult to achieve. The process of sequencing DNA involves feeding a strand of DNA through a nanopore on a microelectronics and microfluidics chip. This mechanism can be likened to how skin pores allow sweat to escape from the body; however, in this scenario, the nanopore serves a much more complex and critical role in the analysis of genetic information. As the DNA strand passes through the nanopore, it interacts with the pore in a way that allows the system to detect the individual nucleotides. Each of the four bases (ATGC) has a distinct electrical signature that causes fluctuations in the voltage across the nanopore based on molecular charge. These unique voltage signatures are key in identifying the specific sequence of the DNA. As the DNA moves through the nanopore, the nucleotides cause changes in the ionic current that flows through the nanopore. The ONT technology captures these changes in real-time, allowing for the rapid and accurate determination of the sequence of bases in the DNA strand. This process not only enhances the speed of sequencing but also improves the fidelity of the results, making it a valuable tool for families seeking to understand their genetic makeup. Moreover, the implications of using rapid nanopore sequencing extend far beyond mere identification of genetic sequences. Families can utilize this technology for a variety of applications, including genetic testing for hereditary conditions, personalized medicine, or even ancestry tracing. With the ability to generate long reads of DNA sequences, nanopore sequencing provides a more comprehensive view of the genome, particularly beneficial for identifying structural variations or mutations like Single Nucleotide Polymorphisms (SNPs) that may contribute to health issues.
In Conclusion
Monitoring our health and wellness has transitioned from depending on subjective feelings to using biometric, data-driven insights. Technological progress has greatly influenced Healthcare and Medicine, allowing both clinicians and patients to measure health status. The integration of quantitative health metrics with qualitative subjective patient feedback is broadening the understanding of health and wellness. This enhanced awareness of personal health and wellness improves clinical decision-making and health management strategies. By leveraging concepts in Biomedical Engineering such as Biosignal Processing, Bioinstrumentation, Photoplethysmography (PPG) clinic-based testing and reporting is transitioning to at-home, Telewellness monitoring. We suggest a home wellness station similar to a vital signs monitor, available to the whole family. It includes a pulse oximeter, body temperature sensor, EMG for muscle strength monitoring, EEG for mindfulness tracking and exercises, ECG for heart health analysis, and a blood pressure monitor. This wellness monitor connects with the smart home hub, providing secure access for family members through biometric authentication and end-to-end encryption for IoT purposes. Accompanying this wellness station is a health wearable that tracks and analyzes non-invasive bioparameters like heart rate, body temperature, blood pressure, respiration, blood glucose, blood lipid, uric acid, and sleep cycles. It also features an ECG function with artificial intelligence (AI) capabilities, utilizing machine learning to observe and learn individual heart health patterns and foresee potential cardiovascular risks. A promise of genomic sequencing exists with nanopore sequencing technologies and bioinformatics tools. Families could have affordable access to high-fidelity DNA sequencing that was previously difficult to achieve unlocking this technology for a variety of applications for hereditary conditions, personalized medicine, or ancestry. Monitoring our health enables and powers individuals to prioritize health and well-being in order to move with intent into higher states of holistic wellness.
Bsc. Biological Sciences (Hons.)
Cert. MBA Essentials
Cert. Whole Genome Sequencing
Cert. Bioengineering, Biotechnology and Biomedical Engineering
Cert. Molecular Spectroscopy
Cert. Arduino, Raspberry Pi and Internet of Things (IoT)
Cert. Bioinformatics
Cert. American Heart Association Heartsaver CPR and AED
Cert. Medical Software
Cert. Biosensors for Biomedical Applications
Cert. Epidemiology
Cert. Systems Thinking in Public Health
Cert. Design Thinking and Creative Innovation
Cert. Software Engineering
Cert. Digital Transformation
Cert. Cybersecurity
Cert. Generative Artificial Intelligence (AI)
Cert. Robotics
Cert. Electronics
Cert. Innovation Management
Cert. The Science of Well-Being
Cert. Healthcare Management
References
World Health Organization (WHO). Grenada Health Statistics. Available at: https://data.who.int/countries/308
Pan American Health Organization (PAHO). Grenada Launches HEARTS Initiative. Feburary, 2025. Available at: https://www.paho.org/en/news/18-2-2025-grenada-launches-hearts-initiative
Sörnmo L, Laguna P. Bioelectrical signal processing in cardiac and neurological applications. Elsevier Academic Press; 2005. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC1939853/
Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32(3):230–236. DOI:10.1109/TBME.1985.325532. (Landmark ECG signal processing algorithm widely used in embedded systems, including Arduino ECG prototypes) Wikipedia
Martis RJ, Acharya UR, Min LC. ECG beat classification using principal component, independent component and wavelet transforms. Biomed Signal Process Control. 2013;8(4):437–448. Available at: https://www.researchgate.net/publication/255738360_ECG_beat_classification_using_PCA_LDA_ICA_and_discrete_wavelet_transform#citations
Charlton P, Budidha K, Mejía-Mejía E, El-Hajj C, Kyriacou PA. Photoplethysmography signal processing and synthesis. In: Photoplethysmography. Elsevier; 2022. DOI:10.1016/B978-0-12-823374-0.00015-3. (Comprehensive PPG signal principles and processing methods)
Elgendi M, Norton I, Brearley M, Abbot D, Schuurmans D. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28(3):R1-R39. DOI:10.1088/0967-3334/28/3/R01. Available at: https://pubmed.ncbi.nlm.nigh.gov/22954270/
Mendonca AM, Pereira M, Delgado EV, Oliveira R. Photoplethysmography; an integrative review on generation mechanisms, measurement principles and signal processing. J Med Biol Eng. 2022;42(4).xxx-xxx. Available at: https://pubmed.ncbi.nlm.nigh.gov/35300400/
Niedermeyer E, da Silva FL. Electroencephalography: basic principles, clinical applications and related fields. 6th ed. Lippincott Williams & Wilkins; 2011. Available at: https://academic.oup.com/book/35515
Niedermeyer E, Lopez da Silva FH. The Neurophysiological bases of EEG and EEG measurement: a review. Clin Neurophysiol. 2014;125(8):1456-1468. Available at: https://pubmed.ncbi.nlm.nih.gov/25039563/
Ruiz-Garcia A, Lunadei L, Barreiro P, Robla JI. A review of wireless sensor technologies and applications in agriculture and health monitoring. Sensors. 2009;9(6):4728–4750. DOI:10.3390/s90604728. Available at: https://pubmed.ncbi.nlm.nih.gov/22408551/
Xie C. Biomedical signal processing: an ECG application. In: Celi L, Majumder M, Ordóñez P, Osorio M, Paik K, Somai M, editors. Leveraging Data Science for Global Health. Springer; 2020. DOI:10.1007/978-3-030-47994-7_17. Available at: https://link.springer.com/chapter/10.1007/978-3-030-47994-7_17
Cardarilli, Gian Carlo & Di Nunzio, Luca & Fazzolari, Rocco & Re, Marco & Silvestri, Francesca. (2019). Improvement of the Cardiac Oscillator Based Model for the Simulation of Bundle Branch Blocks. Applied Sciences. 9. 10.3390/app9183653. Available at: https://www.researchgate.net/publication/335620523_Improvement_of_the_Cardiac_Oscillator_Based_Model_for_the_Simulation_of_Bundle_Branch_Blocks
Brainwave Frequencies Infographic. The 5 brainwave states & how to use them for creative insight. Hunting the Muse. Available from: https://huntingthemuse.net/library/brainwave-states
Wang, Y., Zhao, Y., Bollas, A. et al. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol 39, 1348–1365 (2021). https://doi.org/10.1038/. Available from: https://www.nature.com/articles/s41587-021-01108-x#citeas
Comments