Human motion not only contains a wealth of information about actions and intentions, but also about identity and personal attributes of the moving person. Research also indicates that there are positive relationships between the health of a person and their pattern of motion. In this research we utilize human walking data collected from volunteers to identify age categories and to detect possible changes in the individual's health condition. The approach is based on transforming biological motion data into a representation that subsequently allows for analysis using artificial intelligence techniques. Using wireless accelerometer sensors we were able to collect and transform the data into a numeric representation. We then applied the numeric data to various artificial intelligence algorithms to form classification models and use it for our analysis.