Continuous and Unified Modeling of Joint Kinematics for Multiple Activities

Intuitive control of powered prosthetic lower limbs is still an open-ended research goal. Current controllers employ discrete locomotion modes for well-defined and frequently encountered scenarios such as stair ascent, stair descent, or ramps. Non-standard movements such as side-shuffling into cars and avoiding obstacles are challenging to powered limb users. Human locomotion is a continuous motion comprising rhythmic and non-rhythmic movements, fluidly adapting to the environment. It exhibits strong inter-joint coordination and the movement of a single joint can be largely predicted based on the movement of the rest of the body. We explore a continuous and unified kinematics estimation strategy for a wide variety of movements without the need for labeled examples. Our data-driven approach uses natural body motion from the intact limbs and trunk to generate a kinematic reference trajectory for prosthetic joints. Wearable sensors were worn by 63 subjects without disabilities to record full-body kinematics during typical scenarios (flat ground and stairs), and non-rhythmic and atypical movements (side shuffles, weaving through cones, backward walking). A Recurrent Neural Network (RNN) was trained to predict right ankle and knee kinematics from the kinematics of other joints as inputs. Results were assessed on 3 different test subjects previously unseen by the network. All predictions had a RMSE of less than 7.5 degrees and a high correlation across activities. These offline predictions were robust to subject-specific variations such as walking speed and step length. Additionally, to test the feasibility of using a data-driven reference towards prosthetic control in real-time, a systems test was designed with a single participant. The controller acquired live kinematics, generated predictions using a pre-trained neural network, and demonstrated the capability to actuate the knee joint of a powered prosthesis for the treadmill walking task.