Intuitive Control of Powered Legs for Improved Mobility

Powered assistive robots enable disabled individuals to complete a variety of different ambulation activities. However, proper control of these devices requires that they identify and respond to user intent. Here we present one such device, an open-source powered lower limb prosthesis, which was configured to acquire and respond to the user's neural information (acquired via electromyography) fused with other sensors available on the device. The control system was used across a range of ambulation activities and included seamless transitions between activities. More recently, we have used deep neural networks to investigate how control approaches can be used to create control systems that generalize across different users and devices to reduce the amount of calibration data required from each user.