Patient condition during rehabilitation has been traditionally assessed using clinical scales. These scales typically require the patient and/or the clinician to rate a number of condition-related items to obtain a final score. This is a time-consuming task, specially if a large number of patients are involved. Furthermore, during rehabilitation, user condition is expected to change steadily in time, so assessment may require to run these scales several times to each user. To save time, much effort has been focused on developing clinical scales that require little time to be completed. This is usually achieved by measuring a reduced set of features, i.e., focusing the scales on specific features of a defined target population (Parkinson's disease, Stroke, and so on). However, these scales still require the therapist's intervention and may be tiresome for patients who have to fill them repeatedly. This paper proposes a novel approach to automatically obtain balance scales from the onboard sensors of a robotic rollator. These sensors are used to extract spatiotemporal gait parameters from patients using the rollator for support. These parameters are derived from the user forces on the rollator handles and its odometry. Resulting parameters are used to predict the Tinetti mobility clinical scale on the fly, without therapist intervention. Our approach has been validated with 19 rollator volunteers with a variety of physical and neurological disabilities at Hospital Civil (Malaga) and Fondazione Santa Lucia (Rome). Clinicians provided traditionally obtained Tinetti scores and the proposed system was used to estimate them on the fly. Results show a small root mean squared prediction error. This method can be used for any rollator user anywhere in everyday walking conditions to obtain the Tinetti scores as often as desired and, hence evaluate their progress.