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The purpose of this paper is to extend the Human Biometric Sensor Interaction (HBSI) model to various modalities, in this case, hand geometry. As the data was collected at different times, there was a slight modification in training between group 1 and group 2. Therefore, a secondary purpose of this paper was to examine the differences in the HBSI metrics when individuals are given two different types of training (one using video training, and the other using small group lecture-style training). 151 individuals were asked to perform an enrollment transaction and three successive post-enrollment verification attempts with the hand geometry machine, and an observational analysis was performed on their interactions. This type of analysis is novel to the field of biometrics and the human interaction component has only recently received attention . Using a framework developed specifically for studying various human interaction errors, the observations from hand recognition device placements were analyzed and mapped onto the HBSI error framework. Instead of categorizing a user error as a failure to enroll (FTE) or failure to acquire (FTA), a more comprehensive categorization of these errors were developed. Both incorrect and correct interaction errors were coded and binned in appropriate categories by a human observer. The results showed that hand geometry modality could fit the existing HBSI model. Furthermore, the experiment highlighted slight variations in errors due to training, which will be investigated further in another paper.