At the same time, his dedication and guidance in developing the audit approach presented in this thesis was invaluable. The manuscript in the chapter was presented at the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012). Other devices have been expanded to include the trajectory-based controllers of the Lokomat and Reha Stim GT.
As mentioned above, the existing control methodologies for the state-of-the-art RAGT systems are mainly trajectory-based. Third, a non-pathway-based controller may require the patient to coordinate the movement of the lower limb during walking. The preliminary work started with an investigation into actively reducing the passive dynamics of the exoskeleton and ensuring accurate phase detection in the user's gait cycle.
The exoskeleton used in this initial work was a prototype of the Indego exoskeleton referred to as the Vanderbilt Exoskeleton. This manuscript was presented at the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012).
ACTIVE COMPENSATION OF PASSIVE DYNAMICS OF A POWERED LOWER- LIMB EXOSKELETON LIMB EXOSKELETON
To evaluate the accuracy and robustness of the phase tracking system, a healthy subject was asked to walk on a treadmill while wearing the exoskeleton. In the following two subsections, the results of two experiments show that the ACPD controller is effective in reducing the effect of the passive dynamics of the exoskeleton on the user. As can be seen from the results, the presence of gravity compensation significantly reduces the effective weight of the exoskeleton (although it does not return it to the EMG level measured in the absence of the exoskeleton).
As such, it is hypothesized that a portion of the increased EMG seen in the gravity compensation experiment was due to the (sagittal plane) constraints on movement imposed by the exoskeleton. To reduce the effects of added inertia, hip and knee joint torques are augmented in proportion to the respective angular acceleration of the joint. To reduce the effects of additional friction, hip and knee joint torques are augmented in proportion to the respective angular velocity of the joint.
It is clear from the data that the joint trajectories in ACPD are much closer to unaffected walking than those of the exoskeleton without compensation (i.e. the passive exoskeleton). With the addition of the ACPD controller, the knee joint achieved 96% of its normal range of motion, while the hip joint achieved 108%.
PHYSIOLOGICAL SIGNAL RESPONSE TO VARYING TASK ENGAGEMENT IN A MULTI-LIMB COORDINATED MOTOR-LEARNING TASK IN A MULTI-LIMB COORDINATED MOTOR-LEARNING TASK
An experiment presented in the manuscript in this chapter found that in a multilimb coordinated motor learning task, certain physiological signals are related to task engagement. Subjects were asked to return to a control experiment in which they played a simplified rhythm at different rates in order to analyze whether the correlations observed in the experimental condition were due to physical exertion. It is believed that the patient's effort during physical therapy is an important factor in the rehabilitation process.
EMG amplitude in the facial muscles frontalis and corrugator supercilii has been shown to increase with task load during a two-choice serial reaction task [71]. In the experimental condition, nine healthy subjects performed a motor learning task of varying degrees of difficulty while physiological signals were recorded. Scatterplots for the data points used in the analysis of the control data are shown in Fig.
During the experiments, EMG amplitude in the corrugator superscilii was found to correlate negatively with increasing task load (ρ = -.39, p < .001). In the control condition, no statistically significant correlation was observed between the task speed (SPM) and any physiological signal. In the control condition, subjects reproduced the level of physical exertion obtained in the experimental condition, but no statistically significant correlation was observed between the task rate and any physiological signal.
This suggests that the response seen in the experimental condition was due to increased mental engagement and not simply due to increased physical exertion. EMG measurement of the corrugator superscilii muscle, HR and SCL were shown to correlate with task load in a multi-limb coordination motor learning task, whereas EMG measurement in the frontalis muscle was not. The lack of statistically significant correlation in the control data further suggested that there was little to no reason to believe that this correlation was due to physical exertion.
This may be due to the measure of task load used in the experiment, indicating that further experiments should be conducted. Alternatively, the low correlation value may be due to increasingly erratic behavior in the signals at higher task loads. This manuscript was published in Issue 3 of Volume 24 of IEEE Transactions on Neural Systems and Rehabilitation Engineering.
DEVELOPMENT AND PRELIMINARY ASSESSMENT OF A NON- TRAJECTORY BASED CONTROLLER FOR A POWERED LOWER-LIMB EXOSKELETON TRAJECTORY BASED CONTROLLER FOR A POWERED LOWER-LIMB EXOSKELETON
Robotic versions of this therapy involve robotic manipulation of the legs instead of manual manipulation. The overall intent of the exoskeleton is to help a patient regain the neural coordination associated with walking. As such, the goal of the control approach presented here is to provide movement support to the patient (to compensate for muscle weakness and improve stability), without providing a desired movement path or trajectory.
The relevant components of the control approach and the state machine within which they operate are described in the following sections. Within each state, the control torque may consist of a combination of several auxiliary torque components. Note that the mass of the hip segment is equally distributed between both legs during the double-support phases of walking (ie, state 2).
Finally, note that this component of the control law does not vary by substate. To provide locomotion assistance without dictating joint trajectories, one of the components of the exoskeleton controller is partial weight compensation of the affected leg limb during the swing phase of gait. Because the weight of the limb helps with movement when the limb is present.
The switching conditions that describe movement between the final states of the state machine are shown in Fig. Designing the exoskeleton was previously described in the context of providing leg mobility to people with paraplegia [1, 10]. The exoskeleton is used in conjunction with ankle foot orthoses (AFOs), which provide stability at the ankle joints and transfer the weight of the exoskeleton to the ground.
These data suggest the nature of the interaction between the exoskeleton and the subject. Some of the steering components as shown in the graphs include: a) hip flexor torque associated with gravity compensation; b) power dissipation associated with the gravitational compensation of the mass of the exoskeleton;. Note that the exoskeleton generally generates and dissipates power at different times of the gait cycle, but on average provides net power to the user (ie, on average, is support rather than resistance).
As such, the purpose of the control method presented here is to provide the patient with motion assistance without providing a desired joint angle path or trajectory. First, the NTB controller produced functional improvements in subject gait parameters that matched, exceeded, or fell short of the improvements produced by existing trajectory-based controllers.
A PRELIMINARY CROSSOVER STUDY COMPARING THE EFFICACY OF TRAJECTORY-BASED AND NON-TRAJECTORY BASED CONTROL IN A LOWER-LIMB TRAJECTORY-BASED AND NON-TRAJECTORY BASED CONTROL IN A LOWER-LIMB
The TB controller was developed to provide freedom of the unaffected leg while still enforcing trajectory-based control of the affected-side hip and knee joints in the sagittal plane. States 1, 2 and 3 correspond to the swing phase of the affected leg, the double support phase and the swing phase of the unaffected leg respectively. Substate 1a consists of the portion of the affected leg swing phase in which the knee bends.
Substate 1b includes the part of the affected leg swing phase in which the knee extends. Substate 3a includes the part of the unaffected leg swing phase in which the knee is flexed. Substate 3b includes the part of the unaffected leg swing phase in which the knee extends.
The intact leg is assisted exclusively by exoskeleton gravity compensation (ie, designed to negate the weight of the exoskeleton), which is only active during state 3 (the swing phase of the intact leg). As with the NTB controller, the intact leg is assisted exclusively with exoskeleton gravity compensation (i.e., designed to negate the weight of the exoskeleton), which is only active during state 3 (the swing phase of the intact leg). However, that study did not explicitly show that these benefits were a result of exoskeleton-assisted therapy.
The protocol for the TB controller was almost identical to that of the NTB controller. As is also clear, the NTB controller produced the largest average gains per session in FGS, SL, and ASKE, producing results comparable to those of the NE condition in ASHE. The subject experienced a decrease in FGS after training in all three conditions, and had similar decreases in SL when trained with the trajectory-based controller or the NTB controller.
Despite the equivocal results from subject 2, the NTB controller produced statistically significant improvements in ASKE and ASHE over the TB controller (based on paired t-tests at 90% confidence) when averaged over all subjects. Furthermore, the use of the XSENS motion capture system allowed a deeper analysis of the user's gait than was possible in the study presented as Manuscript III. Graphs show Subject 1's affected side hip (a.) and knee (b.) angles when walking without the use of the exoskeleton (green), when walking with the track-based controller (red), and when walking with the non-track-based controller (blue), each averaged over 10 consecutive steps.
The results revealed a statistically significant correlation between a set of physiological signals and the difficulty of the task. Silberberg, “Revised estimate of the prevalence of multiple sclerosis in the United States,” Annals of Neurology , vol.
