Osteoarthritis (OA) is a joint disease that causes cartilage degeneration, stiffness, and unpredictable, painful flare-ups. Current diagnostic methods provide only clinical snapshots. This leaves a lack of continuous insight into symptom changes in daily life. This project was driven by the hypothesis that elevated localised skin temperature and more frequent or intense acoustic joint emissions (crepitus) could indicate a flare-up and increased pain. To test this, a non-invasive smart wearable needed to be developed. By continuously measuring thermal and acoustic signals in daily life, this smart knee-wearable aims to objectively detect flare-ups and, in the future, bridge the gap between subjective pain and objective clinical data.

To achieve this goal, the Double Diamond design methodology was applied. After a discovery phase of literature research and expert interviews, a Programme of Requirements was established in the define phase. An iterative design process was followed for the development phase. Ideas were explored in Virtual Reality, and physical prototypes were made. These iterations were tested for technological feasibility and comfort, with input from both healthy individuals and OA patients.

The resulting prototype is a comfortable, breathable knee sleeve made of a spandex-like material and called Lola, meaning Long-term osteoarthritis logging assistant. The design has an open kneecap and knee hollow for optimal freedom of movement. With silicone anti-slip elastic to keep it in place. Integrated technology includes a XIAO ESP32-S3 microcontroller, SD card module, MEMS microphone, and several NTC temperature sensors. Conductive yarn, stitched in a zigzag stitch, enables seamless, flexible integration of components.

The validation showed that the wearable effectively records the desired data. During controlled movements, the acoustic algorithm distinguished a healthy knee from an OA knee, but continuous walking and friction from long trousers caused mechanical noise in the sound data. The temperature sensors also accurately recorded physiological heat changes. Furthermore, the wearable scored highly for comfort. Test subjects reported forgetting they wore it within 30 minutes. However, patients also reported that a wraparound model would be easier to put on and is an area for improvement.

In conclusion, developing a non-invasive wearable to measure crepitus and temperature for patients with OA is feasible. Although it is not a market-ready medical product, and noise-reduction software still needs refinement, this proof-of-concept shows that continuous, objective monitoring of osteoarthritis in daily practice is possible and valuable, provided that several aspects of the wearable are further investigated and optimised.