Inadvertent lower extremity displacement (ILED) puts the feet of power wheelchair (PWC) users at great risk of traumatic injury. Because disabled individuals may not be aware of a mis-positioned foot, a real-time system for notification can reduce the risk of injury. To test this concept, we developed a prototype system called FootSafe, capable of real-time detection and classification of foot position. The FootSafe system used an array of force-sensing resistors to monitor foot pressures on the PWC footplate. Data were transmitted via Bluetooth to an iOS app which ran a classifier algorithm to notify the user of ILED. In a pilot trial, FootSafe was tested with seven participants seated in a PWC. Data collected from this trial were used to test the accuracy of classification algorithms. A custom figure of merit (FOM) was created to balance the risk of missed positive and false positive. While a machine-learning algorithm (K nearest neighbors, FOM=0.78) outperformed simpler methods, the simplest algorithm, mean footplate pressure, performed similarly (FOM=0.62). In a real-time classification task, these results suggest that foot position can be estimated using relatively few force sensors and simple algorithms running on mobile hardware.Clinical Relevance- Foot collisions or dragging are severe or life-threatening injuries for people with spinal cord injuries. The FootSafe sensor, iOS app, and classifier algorithm can warn the user of a mis-positioned foot to reduce the incidence of injury.