| dc.description.abstract | Neuromuscular and sensorimotor degeneration significantly reduce physical, cognitive, and social
well-being across the life span. Assistive technologies have proven useful in augmentation or
supporting patients as they work towards positive therapeutic outcomes. However, the efficacy of
these tools is undermined by a lack of quantifiable feedback regarding the degree of intervention
at one point and the effectiveness of various training parameters (such as timing, intensity, etc.).
In this dissertation, the human-robot interaction (HRI) is studied to establish an efficient, safe,
and quantifiable therapeutic approach that accounts for variation in biomechanical and bio-signal
parameters. The research involves five main contributions: (1) identifying effective task-based trajectories
for rehabilitation while performing different tasks within the range of the targeted limb,
(2) developing an intervention prediction package based on human factors, (3) establishing an
interfacing system to reproduce the selected effective trajectories through robots with an assist-asneeded
(AAN) intervention protocols using the predictive model, (4) any possible complication
due to using such interventions either through robot end effector, or exoskeletons is assessed from
the human anatomy prospective; and (5) the same AAN protocol is implemented in a human robot
collaboration task where muscle fatigue affects performance of the human counterpart and accordingly
robot is required to adjust its contribution.
Preliminary results have indicated that both the task and the interaction forces are crucial in
recruiting and affecting the muscles; A predictive model based on the interaction force and set
EMG threshold values is derived through experiments in the cases of no assistance, partial assistance,
and full assistance scenarios and is employed to drive contributing factor ( ) to modify the
Euler-Lagrange equation and implement the AAN protocol. The human-robot joint alignments and
their effects on biomechanical and bio-signal parameters are also studied through musculoskeletal
model analysis. The developed predictive model based on the human fatigue and neural network
was able to estimate the interaction forces with 80% accuracy. This estimated interaction force in
then utilized in the design of an Assist-as-Needed controller. | |
