The overall goal of this procedure is to present an experimental setup and protocol in a library environment to evaluate neurally controlled artificial legs on patients with lower limb amputations. This is accomplished by first preparing for surface EMG signal measurement from the subject's residual lower limb muscles. Then the powered prosthetic leg on the recruited subject is aligned and calibrated.
Next, the training data is collected and the classifiers in the neural machine interface are trained. The final step is to test the performance of neural control of the powered prosthetic leg on the recruited amputee subject. Ultimately, the neurally controlled powered prosthetic leg is used to allow the subject to perform various activities such as standing level, ground walking ramp, ascent, and ramp descent safely and continuously in the laboratory.
The main advantage of this design engineering platform is that each function block can be easily debugged, modified, and updated. In addition, adding or deleting functions or changing the connection between models can be easily done in the computer program. The new electrode socket interface design can provide a high quality EMG signal recording, tight socket suspension, and good user comfort.
Therefore, this design can be used to investigate the muscle property or function in the rest limbs of lower limb amputees. Demonstrating the procedure will be deemed one. William Boatwright and Aaron Fleming.
The students from our lab Prepare the subject for testing by putting on a size fitted fall arrest harness and attaching it to the ceiling rail system. Next, select seven fully charged wireless EMG sensors. Enter the mon place the EMG sensors into the customized suction socket at the prepared locations.
Write down the order number of the sensors and associate them with EMG locations. After cleaning the skin of the subject's residual limb with isopropyl alcohol, attach the powered prosthesis to the suction socket with a pyramid adapter. Assist a subject in donning the suction socket and verify that the socket is firmly attached to the subject's residual limb.
Next turn on the real-time EMG analog data streaming software. Then ask the subject to perform hip flexion and extension, hip abduction and abduction, and to imagine knee flexion and extension, and examine the EMG signals to verify the EMG electrode contact and data transmission to align and calibrate the power prosthetic. Begin with the subject In a standing position holding an assistive walker walker, adjust a set of rotation screws on the adapter until the position of the prosthesis is geometrically aligned with the socket.
Ask the subject to lift the prosthesis off the ground and calibrate the load cell on the prosthetic pylon. Instruct the subject to practice walking on different terrains level ground ramp ascent, and ramp descent. When wearing the powered prosthetic leg, have the subject continue until he or she feels confident in walking with the powered device and yields a consistent gait pattern.
In each activity, explain the predefined walking path to the subject and instruct the subject to stand on the starting location of the walking path. Next, turn on the power prosthesis and load the parameters into the intrinsic controller. Run a training data collection computer program and set the intrinsic control to standing mode by clicking the standing button on the graphic user interface or gooey.
Then instruct the subject to walk over level ground at his or her self-selected, comfortable walking speed. At the same time, click the walking button on the gooey before toe off of the leading leg of the subject, which automatically sets the intrinsic control to level ground walking mode. When the subject approaches the edge of the ramp, click the ramp ascent button on the gooey before the toe off of the prosthetic leg, stepping on the ramp, which switches the intrinsic control to ramp as scent mode for safety.
Allow the subject to use a hand railing when walking on the ramp. When the subject comes to the edge of the ramp, click the walking button again. Before the heel of the prosthetic leg strikes the level platform, which switches the prosthetic intrinsic control to level ground walking mode.
At the end of the walking path, instruct the subject to stop and remain standing at the same time. Click the standing button before the double stance phase, which switches the intrinsic control back to standing mode. After approximately five seconds, terminate data collection by clicking the stop button.
Repeat the procedure as the subject walks in a reverse route back to the starting location. The only difference is switching the intrinsic control to ramp descent mode. When the subject walks on the down ramp, repeat the walking up and down the ramp 10 times and then examine the signal quality of the collected training data set.
Next, train the pattern recognition classifiers in the neural machine interface via an offline training module. Use the collected EMG and mechanical signals, the activity modes labeled during the training procedure and the detected phases to build a phase dependent pattern. Classifiers save the parameters of the classifiers automatically for later online testing session.
Begin the next set of testing by instructing the subject to stand at the starting point of the walking path. After turning on the powered prosthesis load the trained classifier to the online testing module and the parameters to the intrinsic controller. Next, instruct the subject to begin the testing trials in a standing position.
Then continuously transition to level ground walking, ramp walking level, ground walking again, and finally stopping. At the end of the walking path, instruct the subject to perform each activity at a comfortable pace. Allow rest periods between trials to avoid fatigue during each testing trial.
Display the activity modes of the prosthesis and knee joint angle readings on a monitor, save all of the measurements and control outputs for later evaluation purposes. Raw EMG signals recorded from the thigh muscles of subject's residual limb exhibit A characteristic pattern when the subject alternated between hip flexion and hip extension. Raw EMG signals recorded when the subject walked on a level ground walking path are shown here from these figures, it can be seen that the EMG electrode socket interface can provide good quality interface.
EMG signal measurements. The subject was asked to begin in a standing position, transition to level, ground walking ramp, ascent level, ground walking, and then stop at the end of the walking path. The subject then to the original starting point along the reverse route, the subject was able to smoothly switch the power transfemoral prosthesis control mode based on his intended activity modes.
The red dash line indicates the defined critical timing of each activity mode transition for transitions from level ground, walking to ramp, ascent, or descent, and from standing to walking. The critical timing was the beginning of the swing phase that is toe off for transitions from ramp, ascent or descent to level ground walking and from walking to standing. The critical timing was the beginning of weight acceptance that is heel contact on the level ground.
About 18 seconds into this trial, the prosthesis incorrectly switched to ramp ascent mode when the subject walked on level ground due to the erroneous recognition of the user's intent by the neural machine interface. Errors such as this did not elicit significant change in the walking kinematics of the subject and were not perceived by the subject. However, some errors which disturbed the subject's gait stability were observed in some testing trials, but none caused the subject to fall.
Our proof of concept platform experimental setup and protocol could provide convenient tools to further optimize the neuro control and intrinsic control of powder lower limb prothesis, and could help to develop a true bionic lower limb prothesis that can be operated by the users easily, reliably, and intuitively. After watch this video, you should have a good understanding of how to apply the developed engineering platform to evaluate neuro controled artificial leg patients with lower limb amputation safely and efficiently in a laboratory environment.
