Supervisors: Drs. Machiel Van der Loos, Jaimie Borisoff
There are a large number of people all around the world who rely on wheeled mobility assistive devices (WMAD) to perform their daily life activities. The use of WMADs impacts various aspects of peoples’ life including their personal autonomy. In many cases, autonomy – that is, peoples’ choices and controls over what they want to do – is determined by the type of mobility assistive device they are using. Therefore, it is essential to recognize, assess, and address the true autonomy-related needs of mobility device users in the process of assistive device development.
In my research, I’m reviewing the literature to identify the main contributing factors to the autonomy of WMAD users. Next, I compare the design and performance characteristics of existing WMADs across these factors. This knowledge provides an insight into the existing gap between the users’ needs and what is available to them. To address this gap, I plan to establish an autonomy-based framework for mobility assistive technology development. Use of this framework could lead to the design and development of mobility assistive devices that provide a more balanced sense of autonomy to the users
Supervisors: Drs. Machiel Van der Loos, Jaimie Borisoff
Powered lower limb exoskeletons (LLEs) are wearable robotic aids that provide mobility assistance for people with mobility impairments. Despite their advanced design, LLEs are still far from being effective assistive devices that can be used to perform activities of daily living. The main challenge in the operation of a LLE is to ensure that balance is maintained. However, maintaining an upright stance is not always achievable and regardless of the quality of user skill and training, inevitably falls will occur. Currently, there is no control strategy developed or implemented in LLEs that help reduce the user’s risk of injury in the case of an unexpected fall.
In this thesis, an optimization methodology was developed and used to create a safer strategy for exoskeletons falling backwards in a simulation environment. Due to the data available regarding the biomechanics of human falls, the optimization methodology was first developed to study falls with simulation parameters characteristic of healthy people. The resulting optimal fall strategy in this study had similar kinematic and dynamic characteristics to the findings of previous studies on human falls. Rapid knee flexion at the onset of the fall, and knee extension prior to ground contact are examples of these characteristics. Following this, the optimization methodology was extended to include the characteristics of an exoskeleton. The results revealed that the hip impact velocity was reduced by 58% when the optimal fall strategy was employed compared to the case where the exoskeleton fell with locked joints. It was also shown that in both cases of optimal human and human-exoskeleton falls, the models contacted the ground with an upright trunk with a near-zero trunk angular velocity to avoid head impact. These results achieved the thesis goal of developing an effective safe fall control strategy. This strategy was then implemented in a prototype exoskeleton test device. The experimental results validated the simulation outcomes and support the feasibility of implementing this control strategy. Future studies are needed to further examine the effectiveness of applying this strategy in an actual LLE.
The first prototype consisted of a model of a triple-link inverted pendulum and a control system, designed and fabricated by an undergraduate Engineering Physics student team at UBC. The mechanical test setup characterized a half-plane and half-scale model of a human body. Three joints of the triple-link inverted pendulum replicated the motion of the hip, knee, and ankle joints. Similar to the three-link model of a human fall, the hip and knee joints of the inverted pendulum were actuated and the ankle was a passive joint. The hip and knee joint angles were read through the actuator’s encoder and the ankle joint angle was read by a potentiometer that was installed at the joint. The controller was programmed to start the safe fall control strategy once the ankle angle passed beyond a specified angle. Therefore, the ankle angle sensor was constantly monitored subsequent to the initialization of the hip and knee joints. When the ankle angle exceeded the specified limit, the position control strategy was activated to control the hip and knee joint angles throughout the fall.
Large deviations were observed between the experimental and optimal values of the joints angular velocity throughout the fall duration. This is mainly due to hardware and software limitations of this prototype.
To address the abovementioned issues and to further improve controller performance a second prototype was built by an undergraduate Mechanical Engineering student team at BCIT. The second prototype includes a scaled and adjustable exoskeleton with the same actuation setup as the first prototype and will execute the fall routine, as well as a release mechanism to zero the system and initiate the fall. Currently, we are working on implementing the developed safe fall strategy in this prototype.
M. Khalili, Y. Zhang, A. Gil, L. Zhao, C. Kuo, H.F. M. Van der Loos, J.F. Borisoff, “Development of a Learning-Based Intention Detection Framework for Power-Assisted Manual Wheelchair Users.” 2020 The 29th IEEE International Conference on Robot & Human Interactive Communication
M. Khalili, K. T. McConkey, K. Ta, L. C. Wu, H. F. M. Van der Loos and J. F. Borisoff, “Development of A Learning-Based Terrain Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 4762-4765, doi: 10.1109/EMBC44109.2020.9175678.
Khalili, Mahsa, et al. “Towards the Development of a Learning-Based Intention Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs.” 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 2019.
Khalili, Mahsa, HF Machiel Van der Loos, and Jaimie F. Borisoff. “Studies on practical applications of safe-fall control strategies for lower limb exoskeletons.” 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 2019.
Khalili, Mahsa, and Peter M. Ostafichuk. “Improving Class Participation by Using an Online Interactive Platform.” Proceedings of the Canadian Engineering Education Association (CEEA) (2018).