The scales on the skin of a snake are an integral part of the snake's locomotive capabilities. It stands to reason that the integration of scales into the design of robotic snakes would open new properties to exploit. In this work, we present a robotic snake design that incorporates rigid scales in the casing of each module. To validate the impact of the scales, locomotion is tested under three conditions: with scales, covered in cloth with scales, and covered in cloth without scales. The performance of the snake robot, in each of the aforementioned scenarios, is evaluated based on its forward displacement while executing each of two pre-programmed gaits: inchworm and lateral undulation. The lateral undulation gait is tested under two additional conditions: pitched and un-pitched. Tracks of the experimental runs are presented followed by a statistical analysis demonstrating an increase in locomotive performance when incorporating scales in the chassis design.

In the rehabilitation field, determining the effectiveness of an intervention protocol begins by comparing the individual's movement characteristics against a baseline. In most settings, this baseline is determined through clinical studies involving a range of patients belonging to the same demographic group. Unfortunately, this leads to a process that is difficult to repeat for all patient demographics, or all movement characteristics, given the demands on clinicians' and patients' time for performing such clinical baseline measurement studies. To address this issue, we discuss a method that allows clinicians to objectively assess an individual's movements and compare the resulting outcome kinematic metrics to a kinematic baseline. Instead of collecting human patient data, we propose a robotic kinematic model that generates a baseline for different kinematic parameters in real-time as a function of the state of a given task. We evaluate our methodology on elbow and shoulder range of motion (ROM) angles obtained from eleven typically developing children. We compare the user's ROM angles to those generated by the proposed model, and discuss the potential of the model to be used in various intervention protocols.