This graduation project focused on developing a functional, comfortable, and reliable textile knee sensor sleeve for use in rehabilitation after knee injuries. Knee injuries are common and have a big impact on daily life. Current progress measurements in knee rehabilitation are mainly based on subjective observations by physical therapists. Objective measurements, when performed, are often fragmented, limited to snapshots, time-consuming and presented in difficult-to-interpret data. This results in a lack of continuous insight into movement of the knee, which makes it difficult to monitor and evaluate progress during exercises and throughout the entire rehabilitation process.

The research focused on the use of textile strain sensors as an accessible and affordable alternative to more complex and expensive sensors like IMUs. These sensors detect strain via changes in contact points, enabling to track movements like joint flexion (bending) and extension (stretching). Because of the high number of knee injuries and the impact on daily life, where the knee is constantly under strain during movement, this joint was chosen as the focus of this project.

To optimize the design for both technical and user-friendliness, the project combined literature research, field research, experimental research, and user testing. This provided insight into user needs, technical constraints, and optimizations for the sensor. The concept was developed by integrating the sensor and associated electronics into a textile knee sleeve and investigating how knee movement influences the sensor output. A visual interface was also developed that presents the data to the user in an understandable way. Design choices were validated through feedback from potential users, measurements, and iterative testing.

The result of this project is KinetiK, a knee sleeve and accompanying interface that allows users to monitor their movements in real time via a live graph. Instant feedback allows users to improve and correct their exercise performance during the session. The interface also gives an overview of session results, such as the number of bends, movement consistency, and maximum flexion angle. Long-term trends and badges help the user track progress and achieve goals. User testing confirmed that the concept is valuable for both patients and physical therapists, as it provides insight into progress and increases motivation.

Validation tests show that the knee sleeve is currently capable of counting the number of flexions, estimating maximum knee angles, measuring movement consistency, and tracking range of motion symmetry. However, precise measurements of knee angles and movement symmetry need additional research and optimization.

All in all, this project shows that a textile knee sensor sleeve can be a useful, accessible, and motivating rehabilitation tool, with potential for further application in for example daily activity monitoring, as motivational game-based application, and future use in combination with other sensor technologies. The project combines technology with user-centered design and gives a solid foundation for further development of a desired data-driven rehabilitation aid.