A powered balancing mobility device that can provide the user the ability to safely navigate expected environments of daily living including the ability to maneuver in confined spaces and to climb curbs, stairs, and other obstacles, and to travel safely and comfortably in vehicles. The mobility device can provide elevated, balanced travel.

The Electric Motor Medical Chair with Hand Brake is propelled by FIG. 4 L & R electric motors configuration.

It is designed to be control and stop by FIG. 2 L & R which will turn left, right, back, and forward. The feet can rest in FIG. 6. The front wheel as in FIG. 5 gives it better control.

The chair in FIG. 3 is equipped to turn out for mounting and will adjust for comfort. It's Powered by FIG. 1 Lithium battery.

The Electric Motor Medical Chair with Joystick is propelled by FIG. 3 L & R electric motors. The footrest in FIG. 5 for comfort while traveling and the Joystick in FIG. 1 will turn left, right, back and forward. The wheelie bar as in FIG. 6 is for slops and balance while inclined.

Disclosed is a system for controlling a mobility device including a processor configured for a workflow process configured for filtering user commands based on a status of the mobility device and defining filtered commands. A mobility device command process is configured for issuing commands based on the filtered commands. The mobility device responds to the commands that are configured for controlling moving the mobility device.

Methods for controlling a mobility device are presented, the method including: providing the mobility device; selecting a mode of operation; and operating the mobility device in accordance with the selected mode. In some embodiments, the mode of operation is selected from the group consisting of: a learning mode, a novice mode, a standard mode, an advanced mode, and a default mode. In some embodiments, when the learning mode is selected, operating the mobility device includes: collecting real-time learning data for a learning interval; slotting the real-time learning data; averaging the real-time learning data; training a learned anomaly detection model; and establishing the novice mode, the standard mode, and the advanced mode.