Mini-Grant Award: Leveraging Wearable IoT Solutions and AI for Improved Orthopaedic Care in Kenya

The proposed work seeks to develop two methods of approximating the human joint flexion angles and then conduct a comparative analysis of the two implementations with the aim of validating their performance so that the overall implemented system will ultimately be deployed in a clinical setting for use by an orthopaedic clinician or patient. The developed system will be immensely beneficial to the clinician, as they will not only be able to digitise measurements of joint flexion angles, but they will also be able to track the trajectory of recovery of a patient by analysing the flexion angle data that will be stored in the cloud.

The implementation will also be beneficial to the patient, as they will be able to see if they are making progress in recovery by having them access the cloud data via a view-only web application that we intend to develop. The patient can also acquire the system for their personal use, as they might want to regularly check if they are making progress in recovering maximum flexion of a joint without having to make multiple visits to a clinician, who will also have remote access to the flexion angle data that the patient is recording.

After developing the system with the stated requirements and specifications, the team intends to deploy it to an orthopaedic health centre and obtain flexion angle measurements from actual patients so as to test the accuracy and reliability of the system. Since the proposed system will handle patients’ personal and sensitive health data, there is a need to consider the ethical handling of medical data to ensure that all data security concerns are met. Taking this into account, they will seek clearance from the ethics review board of Dedan Kimathi University of Technology before testing on patients. Data security is one of the fundamental concerns in the growing digital healthcare space, and it is paramount to ensure the patient’s data privacy and security when collecting their medical data.


  1. Prof. Ciira wa Maina, Dedan Kimathi University of Technology
  2. Victor KulanKash, DeKUT
  3. Yuri Njathi, DeKUT
  4. Lorna Mugambi, DeKUT
  5. Dr Gachathi Wanjema, DeKUT

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