The process of physical rehabilitation in most cases consists of visits to a physical therapist and exercises the patient is expected to perform at home. To increase or maintain strength or flexibility these exercises need to be performed regularly. Practice teaches us that not all patients succeed here. In this project, we look into what is keeping them from succeeding and how we can support this process.

In literature, four categories of barriers are described, from which the psychological barriers are most interesting to investigate further within this project. Motivation plays a big part within this category.

The Fogg model gives structure to the different components of motivation and different factors influencing the execution of preferred behavior. It shows us triggers will fail if motivation is low or the task takes too much effort to complete.

In a lot of the cases, the intention to complete the exercises is present with the client, but the behavior does not reflect this intention. The intention-behavior gap can be bridged the same way the fogg model suggests triggers to be efficient, by motivating the user or making the task take less effort.

By providing support during the exercise - providing both motivation and decreasing effort - and giving the patient the opportunity to track their progress after doing the exercises for a longer amount of time we can increase the exercise adherence.

To provide support, we need to gather data on the movements that are made during the exercise. We developed a textile stitched strain sensor that tracks the angle of a joint. The sensor has conductive thread stitched in a tight zigzag pattern onto Kinesio tape. Since the tape is adhered to the skin, the sensor experiences minimal hysteresis.

Using the data gathered by the stitched strain sensor, we tested giving back different kinds of feedback using two different actuators. We found that giving the test subjects more precise data on their movement made them more accurate, but at the same time, made them experience their movement as less accurate. This on the one hand, gave them more motivation to improve, but also made them less confident in performing the exercise.

The project proves that using the real time data gathered by the stitched strain sensor can add to better executing of on exercise and has potential to with that data also contribute to long term exercise-adherence.