Abstract
In a world where life expectancy is increasing due to advancements in healthcare, the prevention and early diagnosis of sickness signs are becoming increasingly important. Here, the authors describe a wearable health monitoring system (WHMS) that can gather data, digitize it, pair it with a wearable medical device via Bluetooth, and measure a range of physiological indicators in patients, namely those with heart disease. The system's design, specifications, accepted technology, and implementation challenges are all presented and explored, demonstrating how well it supports healthcare. The suggested method collects heart disease patients' physiological parameters and transmits data to cloud services that enable the processing of data for medical analysis, giving patients and physicians safe access within an appropriate architecture. Research is being conducted on machine learning approaches to help preventative efforts and identify relevant illness signs early on so that prompt medical treatment can be initiated.
TopThe studies and frameworks related to smartphone-based health monitoring and digital phenotyping. Cornet et al., conducted a systematic review of smartphone-based passive sensing for health and well-being (Cornet and Holden, 2018), while Ferreira et al. developed the AWARE mobile context instrumentation framework (Ferreira et al., 2015). Hossain et al., introduced mCerebrum, a platform for digital biomarkers and interventions (Hossain et al., 2017). Recent studies have revealed significant progress in healthcare technology across various fields, including cognitive rehabilitation, cardiovascular disease management, stress detection, and the utilization of IoT devices. Notably, combining AI tools with IoT devices has proven crucial for cognitive cardiac rehabilitation, personalized healthcare through brain-computer interfacing in home automation setups, and the development of advanced cardiovascular disease classifiers for secure platforms (Bhowmick et al., 2023; Kumari et al., 2023; Rizhky et al., 2023). Ongoing research has also explored novel stress detection methods, particularly beneficial for cognitive rehabilitation amid the challenges brought by the COVID-19 pandemic. This includes the use of EEG-based smart advisor bots for stress monitoring during activities like gaming. Furthermore, the merging of IoT and AI technologies extends beyond healthcare to applications like the creation of solar-powered agriculture robots and sophisticated emotion detection systems using generative adversarial networks (Ghosh et al., 2022). These developments collectively highlight the ever-evolving landscape of technological advancements shaping the future of healthcare delivery. Kiang et al. delved into sociodemographic characteristics and missing data in digital phenotyping (Kiang et al., 2021). Majumder and Deen explored smartphone sensors for health monitoring (Majumder and Deen, 2019), and Panda et al., used smartphones for post-cancer surgery recovery metrics (Panda et al., 2020). Torous et al., contributed a scalable platform for data-driven research in psychiatry using smartphones (Torous et al., 2016), while Trifan et. al., conducted a systematic review of passive sensing for health outcomes through smartphones, discussing both current solutions and potential limitations (Trifan et. al.,2019).
Key Terms in this Chapter
MVC: A popular software design pattern for creating user interfaces splits the associated computer logic into three interrelated parts: model, view, and controller.
Medical Device Wearable: A wearable medical device is any autonomous, non-invasive gadget that is designed to carry out a specific medical task, like long-term monitoring or assistance. The word “wearable” suggests that the gadget is held up by clothing or the human body.
Big Data: Big data is the term used to describe extraordinarily vast and varied sets of semi-structured, unstructured, and organized data that keep growing rapidly over time. Conventional data management systems are unable to store, handle, and evaluate these datasets because to their enormous size and complexity in volume, velocity, and variety.
WHMS: Various flexible sensor types that can be affixed directly to the human body or incorporated into clothing, elastic bands, and textile fibre.
Machine Learning: Machine learning is a subfield of computer science and artificial intelligence (AI) that focuses on leveraging data and algorithms to make AI more accurate over time by mimicking human learning
ECG: An electrocardiogram, also known as an EKG or ECG, is a test used to capture cardiac electrical signals. It displays the rhythm of the heartbeat. Electrodes, which are sticky patches, are applied to the arms, legs, and even the chest. The patches are wired to a computer so that the results can be printed or seen.
IoT: The Internet of Things, or IoT, is the collective term for the network of interconnected gadgets as well as the technology that enables communication between devices and the cloud.