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This paper describes an extensive analysis of the comfort level data of 7 subjects with respect to 12 robot behaviours as part of a human-robot interaction trial. This includes robot action, proximity and motion relative to the subjects. Two researchers coded the video material, identifying visible states of discomfort displayed by subjects in relation to the robot's behaviour. Agreement between the coders varied from moderate to high, except for more ambiguous situations involving robot approach directions. The detected visible states of discomfort were correlated with the situations where the comfort level device (CLD) indicated states of discomfort. Results show that the uncomfortable states identified by both coders, and by either of the coders corresponded with 31% and 64% of the uncomfortable states identified by the subjects' CLD data (N=58), respectively. Conversely there was 72% agreement between subjects' CLD data and the uncomfortable states identified by both coders (N=25). Results show that the majority of the subjects expressed discomfort when the robot blocked their path or was on a collision course towards them, especially when the robot was within 3 meters proximity. Other observations include that the majority of subjects experienced discomfort when the robot was closer than 3m, within the social zone reserved for human-human face to face conversation, while they were performing a task. The advantages and disadvantages of the CLD in comparison to other techniques for assessing subjects' internal states are discussed and future work concludes the paper.