Hello readers! thank you for viewing another post. This is part of a two-post series, in which exploring the Internet of Things (IoT) and medical wearable technology; this post, explores the IoT and the functional impact to medical wearable technologies, whilst the follow-up post explores the potential evolution of medical wearable technologies that the IoT can facilitate.
The two-post series references two previous posts on medical wearable technologies as case examples, including the FreeStyle Libre Sensor and Omnipod insulin pump; therefore, this two-post series will contain personal examples of my brother’s life, who has been using these wearable technologies to manage his Type 1 Diabetes.
The Internet of Things
The IoT is essentially the product of IT and IS connectivity, in which collecting data from devices and sharing it with the cloud, thus producing intelligent data sharing; however, the future of the IoT far outweighs this machine-to-machine connectivity, in which connecting everything around us into smart metropolises.
Let’s explore the average morning commute as an example, in which driving from point A to point B, whilst hoping for minimal issues such as congestion, hazards and road works. With the IoT, devices and embedded sensors will collect data about these issues and communicate it, thus cloud-based systems can analyse and create intelligent data; therefore, an immediate best route can be calculated and transmitted to our devices or future autonomous vehicles, in which maximizing the efficiency of our commutes.
Medical Wearable Technology and the Internet of Things
Medical wearable technology aims to improve the health of the user, in which collecting data, preventing ill-health and recommending remedies. In a previous two-post series, I explored the FreeStyle Libre Sensor (FLS) and Omnipod insulin pump wearable technologies.
The FLS is a sensor attached to the rear of the arm, which accurately collects blood glucose level (BGL) readings, whilst the Omnipod is a tubeless insulin pump attached to fatty areas of the body, such as the legs or bottom, that administers insulin based upon the user input of a Personal Diabetes Manager (PDM) device.
The BGL readings tracked by the FLS provides a collection of rich medical data, in which doctors can analyse and better determine future treatment plans; furthermore, the data provides early detection signs of healthcare risks or concerns, which was prudent for my brother, as the trend data of high BGL helped to identify liver bruising, thus preventing any further damage. I’ve added a primary slideshow below to show some of the rich medical data the FLS collects, including graphs of daily BGL readings, average glucose readings and sensor usage capture.