Due to the complexity of humans, rehabilitation, biological signals and wearable devices, a plethora of control techniques have been proposed in the literature. As a control engineering, it is difficult to determine which control method will suit the application best as no platform for comparison is currently available. The main goal of the project is to develop motion assistance control systems for wearable mechatronics devices through comparison of their components and structures. By decomposing these control systems into similar behavioural components it becomes easier to study the interactions between therapists, patients and wearable upper limb devices.
In order to address these issues and determine optimal control techniques for rehabilitation, the WearME control system framework has been developed. The framework ensures functional decomposition of all control system types to enable more efficient implementation and modification of control systems. Using the framework, control systems can be modified for control of multiple devices or facilitate comparison of control system components during control of one device. This research will help to form interfaces for patients and therapists using our devices, tools to evaluate the control system performance and methods for comparison of control system components, such as motion models. Obtaining the optimal control system solution will translate into better device performance and enhancement of outcomes for musculoskeletal rehabilitation using wearable devices.
Today, there are countless patients with neuromuscular disorders and injuries. These patients require continuous rehabilitation in order to heal the affected bodily regions without causing permanent disabilities. Past research has shown that the use of mechatronic devices are vastly beneficial to both speed and quality of recovery. My research is focused on the development of wearable exoskeleton devices for upper limb rehabilitation. Specifically, I am designing a standalone mechatronic elbow brace that will be lightweight and thin enough to be worn by a patient for constant rehabilitation throughout their daily lives. The fundamental goal is to have the brace be as unnoticeable as possible to the patient and bystanders.
1. Tremor data acquisition and analysis: collecting Parkinsonian tremor and Essential tremor from upper limb joints in the format of EMG, inertia and torque. The data are analyzed using nonlinear analysis tool.
2. Mechatronic design of the WTSD: integrating both mechanical design and sensor network
3. Tremor estimator design: mathmatical design, simulation and validation of an adaptive tremor motion estimator.
4. Control system design and validation: design, integration and validation of the control system of the WTSD
Musculoskeletal disorders (MSDs) of the upper limb greatly limit one’s self-sufficiency by making it challenging to preform activities of daily living required to care for one’s physical well-being, and participate in recreational activities and the workforce. While physical rehabilitation can help restore the limb to the pre-disordered state, at-home patient compliance is low. This is due to lack of resources, motivation, and markers of progress. A wearable monitoring tool can be used to increase compliance by increasing the user’s sense of agency through access to quantifiable metrics of their recovery.
The objective of this research is to develop and validate such a system for the upper limb. It will contain an untethered, modular signal acquisition sleeve, which collects and stores physiological, biofeedback, and temperature data. In order to obtain accurate, low-noise electromyography (EMG) signals comfortably, this work will include the development and validation of a custom non-contact EMG module will be developed to insert into the sleeve. The collected data will be integrated through multi-modality sensor fusion techniques such that it can be presented to the user in a meaningful manner. This feedback will be accessible to the user and clinician via a PC tool for the visualization of condition-relevant biometrics.