Background: For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS). Material/Methods: In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied. Results: (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach's alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore “gait speed” are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling. Conclusions: The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.