Assignment Wearable Technology in the Medical Industry: An Autonomous Type 1 Diabetes Management System Using the Internet of Things

Hello, thank you for viewing this follow-up post. This post, explores the potential of an autonomous Type 1 Diabetes management system using the Internet of Things (IoT), whilst the previous post explores the IoT and the functional impact to medical wearable technologies.

Future of Medical Wearable Technology and the Internet of Things

The FreeStyle Libre Sensor (FLS) has a permanent thin teflon strip inserted under the skin, which remains until the device is removed, so the FLS has the current hardware potential to be autonomous.

The FLS could autonomously collect BGL readings every half hour and wirelessly transmit the data to the users mobile phone, thus the FreeStyle Libre Reader (FLR) could be removed from the process, in which replaced with a mobile application that is downloaded to the users mobile phone or smart watch. The mobile application will include the current functionality of the FLR, but with additional benefits like sending push notifications when the users BGL requires treatment.

Prior to the FLS, my brother could accurately feel when his BGL required attention, such as a state of hypoglycaemia, but with the additional control that the FLS provides, my brother cannot feel BGL irregularities; therefore, this has resulted in serious ill-health cases, that have only been detected due to routine FLS scans. Implementing autonomous BGL readings, alongside an IoT strategised mobile application, could reduce these serious cases of ill-health and potentially prevent life or death situations.

Autonomous Diabetic Management System

A collaboration between the FreeStyle Libre Sensor and Omnipod insulin pump technology, could provide an autonomous Type 1 Diabetes management system. The FLS could wirelessly connect with the Omnipod, whilst transmitting data to the cloud for a connected mobile application to receive; therefore, the FLS autonomously collects the BGL data, transmits the data to the cloud and wirelessly to the Omnipod.

The Ominpod analyses the data via algorithms and autonomously administers insulin accordingly, whilst the mobile application downloads the data from the cloud, stores and tracks it, then sends a push notification to the user instructing that insulin has been administered. I’ve depicted this system below in a rich picture design.

Rich Picture
Primary Rich Picture of Autonomous Diabetic Management System

Disruptive technologies have performance issues by nature, which in this case i’ve elaborated on further:

  • User trust. Relinquishing much control of a life threatening disease to a system requires substantial user trust; therefore, meticulous development and new legality measures would be required, thus ensuring user safety.
  • Data privacy. Strong cloud security would be required alongside security of the wireless data packets sent between the devices, thus preventing malicious interference, which could potentially cause serious harm to the user.
  • Manual Control. Manual FLS scans and insulin administers should still be available. For the FLS, this could be in the form of an additional sensor attached to the rear of the mobile phone, or a biometric scanner inclusion to the body sensor; for the Omnipod, this functionality could be included into the mobile application.

This disruptive innovation and systematic improvement could completely change the way in which Type 1 Diabetes is managed, which is only possible due to the IoT and the resulting intelligent data connectivity.

Thank you for reading this post! Be sure to check out the previous post regarding the IoT and the functional impact to medical wearable technologies if you haven’t already.

Please like and share if you’ve enjoyed reading and be sure to follow me here or on my other platforms.

Leave a Reply

Please log in using one of these methods to post your comment: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s