3D-Printed Sensing Systems for Upper Extremity Assessment
Due to the COVID-19 crisis the PhD defence of Gerjan Wolterink will take place (partly) online.
The PhD defence can be followed by a live stream.
Gerjan Wolterink is a PhD student in the research group Robotics and Mechatronics. His supervisors are prof.dr.ir. G.J.M. Krijnen and prof.dr.ir. P.H. Veltink from the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS).
Stroke is one of the main causes of disability in the world, resulting in many cases in the loss of motor function. A proper assessment of motor function is required to help to direct and evaluate therapy. Currently, this assessment is performed by therapists using observer-based assessment protocols on a time to time basis influenced by subjective observations and interpretations. Sensor-based technologies can be used to objectively quantify the presence and severity of motor impairments in stroke patients.
The main goal of this thesis is the development of a distributed sensing system for the upper limbs that integrates electromyography (EMG), kinematic and kinetic sensor modalities using soft, flexible and personalized sensing structures, to receive continuous information about the interaction of the human body with the environment. The Kinematic data was obtained using existing inertial measurement units (IMU’s). New soft and customizable EMG and kinematic sensors were developed using fused deposition modelling (FDM) 3D-printing technologies.
A minimally obstructive distributed inertial sensing system, intended to measure kinematics of the upper extremity, was developed and tested in a pilot study with 10 chronic stroke subjects performing arm-related tasks from a clinical assessment protocol (FMA-UE) with the affected and non-affected side. This study showed the high potential of this measurement system to assess pathological synergies and may provide more detailed clinical information with respect to observer based assessment protocols. Therefore, this system enables the possibility for a quantitative assessment of the upper limb synergies and improvements in the rehabilitation progress.
The emerging development of 3D-printing technologies and materials facilitate the creation of personalized sensing structures that can be potentially integrated in e.g. prosthetic and assistive devices. This technology is used to develop flexible carbon-black doped TPU-based surface electromyography (sEMG) sensing structures that have equal performance in signal amplitude as equal sized gold standard Ag/AgCl gel electrodes.
Sensors currently used for fingertip interaction force sensing lack compliance to the fingertip tissue and therefore result in loss of touch sensation. Furthermore, poor sensor to skin attachment leads to unwanted movements of the sensors around the fingertip caused by the external forces. To overcome these difficulties a new flexible 3D-printed finger sensor was developed that is compliant to the soft fingertip tissue, while trying keeping the loss of touch sensation low. This sensor measures both shear and normal forces by using the mechanical deformation of the fingertips caused by the normal and shear-forces.
A major challenge of these 3D-printed sensors is the nonlinear force to signal relation due to the use of (carbon doped) thermoplastic materials. Therefore, signal analysis and compensation models are needed to obtain a signal response correlated to the applied forces.
Insights gained in this research and the ongoing research on new sensor designs, signal analysis methods and the advent of 3D-printing technologies, will enable the future fabrication of personalized integrated sensing devices for applications in e.g. prosthetics, orthotics or soft robotics. These structures could transfer to clinical applications and potentially improve our knowledge, understanding and assessment of pathological synergies that may eventually enable personalized and targeted rehabilitation activities.