Low-Cost Objective Measurement of Prehension Skills

This thesis aims to explore the feasibility of using low-cost, portable motion capture tools for the quantitative assessment of sequential 'reach-to-grasp' and repetitive 'finger-tapping' movements in neurologically intact and deficit populations, both in clinical and non-clinical settings. The research extends the capabilities of an existing optoelectronic postural sway assessment tool (PSAT) into a more general Boxed Infrared Gross Kinematic Assessment Tool (BIGKAT) to evaluate prehensile control of hand movements outside the laboratory environment. The contributions of this work include the validation of BIGKAT against a high-end motion capture system (Optotrak) for accuracy and precision in tracking kinematic data. BIGKAT was subsequently applied to kinematically resolve prehensile movements, where concurrent recordings with Optotrak demonstrate similar statistically significant results for five kinematic measures, two spatial measures (Maximum Grip Aperture – MGA, Peak Velocity – PV) and three temporal measures (Movement Time – MT, Time to MGA – TMGA, Time to PV – TPV). Regression analysis further establishes a strong relationship between BIGKAT and Optotrak, with nearly unity slope and low y-intercept values. Results showed reliable performance of BIGKAT and its ability to produce similar statistically significant results as Optotrak. BIGKAT was also applied to quantitatively assess bradykinesia in Parkinson's patients during finger-tapping movements. The system demonstrated significant differences between PD patients and healthy controls in key kinematic measures, paving the way for potential clinical applications. The study characterized kinematic differences in prehensile control in different sensory environments using a Virtual Reality head mounted display and finger tracking system (the Leap Motion), emphasizing the importance of sensory information during hand movements. This highlighted the role of hand vision and haptic feedback during initial and final phases of prehensile movement trajectory. The research also explored marker-less pose estimation using deep learning tools, specifically DeepLabCut (DLC), for reach-to-grasp tracking. Despite challenges posed by COVID-19 limitations on data collection, the study showed promise in scaling reaching and grasping components but highlighted the need for diverse datasets to resolve kinematic differences accurately. To facilitate the assessment of prehension activities, an Event Detection Tool (EDT) was developed, providing temporal measures for reaction time, reaching time, transport time, and movement time during object grasping and manipulation. Though initial pilot data was limited, the EDT holds potential for insights into disease progression and movement disorder severity. Overall, this work contributes to the advancement of low-cost, portable solutions for quantitatively assessing upper-limb movements, demonstrating the potential for wider clinical use and guiding future research in the field of human movement analysis

Robot-aided neurorehabilitation of the upper extremities

Task-oriented repetitive movements can improve muscle strength and movement co-ordination in patients with impairments due to neurological lesions. The application of robotics and automation technology can serve to assist, enhance, evaluate and document the rehabilitation of movements. The paper provides an overview of existing devices that can support movement therapy of the upper extremities in subjects with neurological pathologies. The devices are critically compared with respect to technical function, clinical applicability, and, if they exist, clinical outcome

Sensory Augmentation for Balance Rehabilitation Using Skin Stretch Feedback

This dissertation focuses on the development and evaluation of portable sensory augmentation systems that render skin-stretch feedback of posture for standing balance training and for postural control improvement. Falling is one of the main causes of fatal injuries among all members of the population. The high incidence of fall-related injuries also leads to high medical expenses, which cost approximately $34 billion annually in the United States. People with neurological diseases, e.g., stroke, multiple sclerosis, spinal cord injuries, and the elderly are more prone to falling when compared to healthy individuals. Falls among these populations can also lead to hip fracture, or even death. Thus, several balance and gait rehabilitation approaches have been developed to reduce the risk of falling. Traditionally, a balance-retraining program includes a series of exercises for trainees to strengthen their sensorimotor and musculoskeletal systems. Recent advances in technology have incorporated biofeedback such as visual, auditory, or haptic feedback to provide the users with extra cues about their postural sway. Studies have also demonstrated the positive effects of biofeedback on balance control. However, current applications of biofeedback for interventions in people with impaired balance are still lacking some important characteristics such as portability (in-home care), small-size, and long-term viability. Inspired by the concept of light touch, a light, small, and wearable sensory augmentation system that detects body sway and supplements skin stretch on one’s fingertip pad was first developed. The addition of a shear tactile display could significantly enhance the sensation to body movement. Preliminary results have shown that the application of passive skin stretch feedback at the fingertip enhanced standing balance of healthy young adults. Based on these findings, two research directions were initiated to investigate i) which dynamical information of postural sway could be more effectively conveyed by skin stretch feedback, and ii) how can such feedback device be easily used in the clinical setting or on a daily basis. The major sections of this research are focused on understanding how the skin stretch feedback affects the standing balance and on quantifying the ability of humans to interpret the cutaneous feedback as the cues of their physiological states. Experimental results from both static and dynamic balancing tasks revealed that healthy subjects were able to respond to the cues and subsequently correct their posture. However, it was observed that the postural sway did not generally improve in healthy subjects due to skin stretch feedback. A possible reason was that healthy subjects already had good enough quality sensory information such that the additional artificial biofeedback may have interfered with other sensory cues. Experiments incorporating simulated sensory deficits were further conducted and it was found that subjects with perturbed sensory systems (e.g., unstable surface) showed improved balance due to skin stretch feedback when compared to the neutral standing conditions. Positive impacts on balance performance have also been demonstrated among multiple sclerosis patients when they receive skin stretch feedback from a sensory augmentation walker. The findings in this research indicated that the skin stretch feedback rendered by the developed devices affected the human balance and can potentially compensate underlying neurological or musculoskeletal disorders, therefore enhancing quiet standing postural control

Sensor based systems for quantification of sensorimotor function and rehabilitation of the upper limb

The thesis presents targeted sensor-based devices and methods for the training and assessment of upper extremity. These systems are all passive (non-actuated) thus intrinsically safe for (semi) independent use. An isometric assessment system is first presented, which uses a handle fixed on a force/torque sensor to investigate the force signal parameters and their relation to functional disability scales. The results from multiple sclerosis and healthy populations establish relation of isometric control and strength measures, its dependence on direction and how they are related to functional scales. The dissertation then introduces the novel platform MIMATE, Multimodal Interactive Motor Assessment and Training Environment, which is a wireless embedded platform for designing systems for training and assessing sensorimotor behaviour. MIMATE’s potential for designing clinically useful neurorehabilitation systems was demonstrated in a rehabilitation technology course. Based on MIMATE, intelligent objects (IObjects) are presented, which can measure position and force during training and assessing of manipulation tasks relevant to activities of daily living. A preliminary study with an IObject exhibits potential metrics and techniques that can be used to assess motor performance during fine manipulation tasks. The IObjects are part of the SITAR system, which is a novel sensor-based platform based on a force sensitive touchscreen and IObjects. It is used for training and assessment of sensorimotor deficits by focusing on meaningful functional tasks. Pilot assessment study with SITAR indicated a significant difference in performance of stroke and healthy populations during different sensorimotor tasks. Finally the thesis presents LOBSTER, a low cost, portable, bimanual self-trainer for exercising hand opening/closing, wrist flexion/extension or pronation/supination. The major novelty of the system relies on exploiting the movement of the unaffected limb to train the affected limb, making it safe for independent use. Study with LOBSTER will determine its usability for home based use.Open Acces