# Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review
## 4 Discussion
### Future Directions  
With limited SHTs to monitor CVD patients in real time evaluated in research settings and accessible to patients, there is an urgent need to design, develop, and deploy effective solutions. Moreover, the disruption in healthcare due to COVID-19 and the severity of COVID-19 is an impetus for the innovation of IoT-based SHTs to address residents' day-to-day healthcare needs [80, 113]. An IoT-based SHT architecture comprises the perception layer that includes sensors such as radio frequency identification sensors, infrared sensors, cameras, global positioning systems, medical sensors, and smart device sensors, the network layer comprises wired and wireless technologies which communicate and store the perception layer captured information, and finally, the application layer interprets data and delivers application-specific services to the user [80]. Figure 3 illustrates an overview of an IoT-based SHT for CVD monitoring [88, 89, 90, 91]. The system comprises four layers: the perception layer, connectivity layer, processing layer, and application layer. The perception layer consists of all sensors in an SHT setting to capture vital recordings, such as BP, HR, heart rate variability (HRV), ECG, blood oxygen and other signals, such as sleep, step count, body temperature and weight. The connectivity layer comprises gateway and telecommunication networks that facilitate the transfer of the sensor data from the internal network to the processing layer over an external network. The processing layer stores the data and performs the specified analysis. Finally, the application layer displays the processed data to healthcare providers, who initiate necessary intervention through caregivers or patients. An IoT health monitoring system could leverage the features of AI, wherein developed algorithms emulate human cognitive function, encompassing but not limited to ML, DL, natural language processing, and computer vision, to deliver enhanced personalised healthcare services [34, 112, 113]. Moreover, the capabilities of ML algorithms facilitate the prediction of CVD [114, 115]. Furthermore, the application of DL could accurately estimate the CVD risk [116]. Constructively, COVID-19 has fuelled the development and utilisation of real-time patient monitoring systems, which are enabled by automated alert systems explicitly tailored to the patient's needs, which could be the cornerstone of a more continuous, patient-centric healthcare model subsequently [117]. Moreover, integrating adjunct technologies, including big data, cloud computing, smart sensors, AI, and virtual reality/augmented reality, with IoT could maximise the potential benefits [118]. Hence, developing SHT for CVD monitoring integrating IoT with adjunct technologies could facilitate real-time monitoring, facilitating the upkeep of the health and well-being of CVD patients [88, 89, 90, 91]. Figure 3: An IoT-based SHT for CVD monitoring. (The layers have specific functionalities. Perception layer: Consist of all sensors in an SHT setting to capture vital recordings, such as blood pressure, heart rate, heart rate variability, electrocardiogram, blood oxygen and other signals, such as sleep, step count, body temperature and weight. Connectivity layer: Transfer the sensor data from the internal network to the processing layer over an external network. Processing layer: Stores the data and performs the specified analysis. Application layer: Displays the processed data to healthcare providers, who initiate necessary intervention through caregivers or patients.) HRV could be used as a marker for cardiac status and for predicting cardiovascular outcomes quickly with wearable devices capable of measuring HR in real time; HRV is a measurable reflection of the balance between sympathetic and parasympathetic tone [119]. A meta-analysis of studies amongst CVD patients observed that lower HRV is associated with a higher risk of cardiovascular events and mortality, although the extent of the association is uncertain [120].