## 3 Design Process
### DAid(r) Smart Socks-Based Biofeedback System Parameter Selection
#### 3.3.5 Technical Innovations and System Architecture for Multi-User
The architecture also allows for future scalability, enabling additional sensors or participants to be integrated with minimal reconfiguration. For example, adding accelerometer data to measure the angle between the foot and lower limb to ensure the back leg is bent in the knee joint during the lunge. Coordinating data from multiple participants in real-time with little to no latency issues or data misalignment is critical for ensuring accurate and actionable feedback, especially due to the dynamic character of the specific FIFA11+ exercise program. The system's client-server architecture ensures synchronized data handling. The server receives sensor data, processes them, and transmits synchronized results to the client application. The PC application sends the data to the HMD with the live feedback in real time according to the training program, thus reducing the risk of receiving faulty data that may occur during pauses in between the exercises. This setup minimizes delays and ensures consistent feedback across all users. Accurate calibration of the DAid(r) smart socks is essential to ensure reliable COP measurements and feedback. One of the challenges throughout the project is variability in sensor performance or user-specific biomechanics that can impact data accuracy. The system includes a calibration feature that adjusts sensor readings based on user-specific parameters. Calibration data are stored locally, allowing the system to compensate for individual differences and maintain accuracy through session. The calibration is adjusted due to the specific needs of the exercises, as some of the exercises require both legs at the same time and some only one at a time; therefore, the program needs to have two different calibrations in order to be further used as the default depending on the exercise. The use of Bluetooth for sensor data transmission and Wi-Fi/TCP for HMD communication is optimized through a dual-layer communication architecture. The Bluetooth layer handles high-frequency data streams from the socks, while the Wi-Fi/TCP layer prioritizes processed data for real-time visualization on the HMD. The system incorporates adaptive data rate protocols for Wi-Fi communication, ensuring that bandwidth is utilized efficiently and that latency remains under 100 ms. This hybrid communication strategy balances bandwidth and latency demands, ensuring seamless integration between the smart socks, server, and the HMD. The team has held multiple brainstorming sessions on biomechanics, recognizing its critical impact on the system. These discussions focused on ensuring accurate data capture, personalized calibration, and precise center of pressure (COP) analysis, shaping the system's design and real-time feedback. The user interface must provide real-time feedback, be easy to navigate, and support complex data visualizations without overwhelming the user. The GUI and MR application were designed with user experience in mind. The MR interface delivers engaging visual feedback. Storing and retrieving high-frequency sensor data for future processing from the PC helps to minimize the processing that happens specifically on the HMD. High-resolution displays are essential for delivering clear and detailed feedback, ensuring users can accurately interpret biomechanical data such as center of pressure (COP) trajectories. The Meta Quest 3 HMD was chosen for its high-resolution displays and advanced rendering capabilities, which support dynamic visualizations without sacrificing clarity or precision.