Pressure-sensitive smart floors deployed within homes can give great insight to the health and activity level of individuals through gait and location information. Due to the ever-changing dynamic nature of household deployments involving furniture movement, floor tile shifts, and sensor drift, challenges arise in ensuring the constant reliability of floor sensor readings over time. This paper presents a procedure to automatically calibrate a smart floor’s force sensors without specialized physical effort. The calibration algorithm automatically filters out non-human static weight while retaining weight generated by human activity. This technique is designed to correctly translate sensor values to weight units even when direct access to the force sensors is not available and when a shared tile floor sits above the sensor grid. These calibrated sensor values can then feed machine learning techniques used to extract individual contact points generated by a person’s walking cycle. Using known human weights but no knowledge of the human’s location or walking trajectory, this calibration technique resulted in small percentage differences of -7.8%, - 4.8%, and -1.6% for the mean, median, and mode of calibrated smart floor walking sequences, respectively.