Colenak: GPS tracking model for post-stroke rehabilitation program using AES-CBC URL encryption and QR-Code

Abstract

Stroke is one of the biggest causes of death in the world. Due to lack of supervision and family support to rehabilitate stroke patients, the number of deaths caused by stroke is increasing. Its because stroke is a neurological disorder that takes a long rehabilitation to get motor skill acquisition for each patient. For those whose goals are to walk again, having physiotherapists help is the way to do so. Unfortunately, there has not been a tool in physiotherapy to conduct medical records measuring travel and mileage of the patient walking outdoor. It is needed for further physiotherapists analysis and recommendation. In this study, we proposed a model called Colenak. It is proposed a Global Positioning System(GPS) tracking model to record the walking (physio)therapy of the stroke patients. This model combines the use of Advance Encryption Stanford (AES) Cipher Block Chaining (CBC) mode and Quick Response (QR) Code. Advance Encryption Standard (AES) CBC algorithm is applied to encrypt the URL. While Uniform Resource Locator (URL)s are known only to patients given QR Code, the QR code will be created based on patient identification and physiotherapy system encrypted along with the URL. By using GPS, the stroke patients who are practicing to walk can be tracked and the tracking result of each patient is expected to support the advance physiotherapy method within this model of physiotherapy.