Recent trends in rehabilitation and therapy are turning to data-driven approaches to personalize treatment. Due to such approaches, data collection methods have become more complex and expensive, in terms of financial resources, technological knowledge, and time required to implement the data collection method. Such costs might deter clinical applications of otherwise good data collection methods. Hence, a method to collect data in a non-intrusive manner is proposed. Sensors are embedded into a commonly used rehabilitation tool, the walking trainer, for gait data collection. This study shows that, in principle, lower body joint angles can be collected in a non-intrusive manner, with a slight trade off to precision. In this study, the focus would be on the pelvic and hip movements, since the pelvic segment of the human body is implicated in a variety of gait problemsClinical relevance - The proposed usage model allows clinicians access to additional kinematic data, while minimizing changes to existing clinical evaluation processes and being non-intrusive. Having additional kinematic data would give further insight into a patient's current state, thereby improving the efficiency of individualized therapy.