Week 1 and 2
14th November — 28th November
User Requirements
Upon finishing the HCI component of our project, we have been looking towards the MoSCoW requirements. We discussed this during our meeting with the project partner and module lead (Dean). There was a particular emphasis on the recording of data and the useful ways of formatting the data, to help the user’s visualise their movement or progress.
Utilising our MoSCoW requirements, we started to look at Use-Cases of our system, to visualise the requirements of the system and to further understand the context under which the system will be used. We promptly produced a Use-Case diagram and hence a Use-Case list, so we could map the steps required for users to achieve each Use-Case.
One thing which came across in doing this work, was that the application we’re designing should have a strong emphasis on being a useful app outside of physiotherapy games. The data should be useful in mapping trends and monitoring patients’ progress. This is an example of a high utility feature, to help all parties using the application.
We were therefore able to see what parts of our previous prototype needed optimising. There was lots of room for improvement so with this in-mind we looked at our low-fidelity sketches from the HCI portion and looked into optimising them to meet the requirements discussed with our partners and the Use-Cases. A few days of work went into this, as we wanted to make sure we had a meticulous vision in mind. The designing was done using Figma.
Literature Review
Knowing our MoSCoW requirements, we started conducting a literature review, in order to gage an idea about previous solutions that we could use as inspiration for our project deliverables. A main issue that needed tackling was being able to clone the camera stream as the camera would be used inside the Microsoft Teams Meeting and also for MotionInput. Although there was limited information regarding how to do this, we were able to find a solution using cloud technology. By storing the camera stream on the cloud, and then cloning it as many times as needed. Despite, the article explaining to do this in Java, we were confident there was a way to do this for our project.
Furthermore, we researched whether any studies related to our project existed. We were able to locate a trial in Italy, where Kinect technology was used for shoulder motion. This article noted methods used to measure motion progress. For example, the Fugl Meyer approach. This we feel can be a main method we use when recording data in the app for the physiotherapists.
We are excited to see what we can do and look forward to updating this blog more!
Written by Adil
