Internet of Healthcare Things (IoHT) Enabled Incessant Real Time Patient Monitoring System Using Non-Invasive Sensors

In recent years, hospitalization and medical treatment costs have become unbelievably high and costly. Enhancing the effectiveness of the infrastructures supporting healthcare and biological systems with the Internet of Things (IoT) is indispensable in many application areas. Using IoT and non-invasive sensor technologies, it is possible to monitor and deploy patient parameters to the cloud without any delay. The proposed IoHT framework is designed with a Raspberry Pi 4B System-on-chip (SoC) computer. The medical treatment is based on the measured value of body parameters like temperature, heart rate, and SpO2 levels. Patients confined to bed or the intensive care unit are the primary focus of this technique and those remotely located. This study uses the MediaPipe Hands hand and finger tracking system. To determine 3D landmarks of a hand from a single image, it uses machine learning (ML). Our solution delivers real-time performance on a cell phone and is even scalable to many hands. In contrast, the existing state-of-the-art relies mainly on robust desktop systems for inference. This also focuses on gesture-based room automation for the bedridden patients to do their necessities independently. To this end, we want to make these hand perception capabilities available to the broader research and development community to inspire the creation of novel use cases and lead to the discovery of exciting new research areas.