Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology. In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical[GV1] modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a high-density electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles. With this high-density approach, muscle coverage is increased, placement time is decreased, and deep muscle activity can potentially be collected due to the high-density nature of the device[GV2] . Furthermore, the high-density electromyography device is built as a precursor to a high-density electromyography-electrical stimulation device for functional electrical stimulation. The customizable nature of the prototype in Chapter 3.2 allows for the implementation both recording and stimulating electrodes. Furthermore, signal results show that the electromyography data obtained from the device are of high quality and are correlated with gold standard surface electromyography sensors. One key factor in a device that can record and then stimulate based on the information from the recorded signals is an accurate movement intent decoder. High-quality movement decoders have been designed by closed-loop device controllers in the past, but they still struggle when the user interacts with objects of varying weight due to underlying alterations in muscle signals. In Chapter 4, I investigate this phenomenon by administering an experiment where participants perform a Box and Block Task with objects of 3 different weights, 0 kg, 0.02 kg, and 0.1 kg. Electromyography signals of the participants right arm were collected and co-contraction levels between antagonistic muscles were analyzed to uncover alterations in muscle forces and joint dynamics. Results indicated contraction differences between the conditions and also between movement stages (contraction levels before grabbing the block vs after touching the block) for each condition. This work builds a foundation for incorporating object weight estimates into closed-loop electromyography device movement decoders. Overall, we believe the chapters in this thesis provide a basis for increasing availability to movement assessment tools, increasing access to effective movement assessment and rehabilitation, and advance the medical device and technology field

Kinematic changes following robotic-assisted upper extremity rehabilitation in children with hemiplegia : dosage effects on movement time

Indiana University-Purdue University Indianapolis (IUPUI)Background: Rehabilitation Robotics (RR) has become a more widely used and better understood treatment intervention and research tool in the last 15 years. Traditional research involves pre and post-test outcomes, making it difficult to analyze changes in behavior during the treatment process. Harnessing kinematics captured throughout each treatment allows motor learning to be quantified and questions of application and dosing to be answered. Objective: The aims of this secondary analysis were: (i) to investigate the impact of treatment presentation during RR on upper extremity movement time (mt) in children with hemiplegic cerebral palsy (CP) and (ii) to investigate the impact of training structure (dose and intensity) on mt in children with CP participating in RR. Methods: Subjects completed 16 intervention sessions of RR (2 x week; 8 weeks) with a total of 1,024 repetitions of movement per session and three assessments: pre, post and 6 month f/u. During each assessment and intervention, subjects completed “one-way record” assessments tracking performance on a planar task without robotic assistance. Kinematics from these records were extracted to assess subject performance over the course of and within sessions. Results: For all participants, a significant decrease in mt was found at post-test and follow-up. No significant differences were found in mt for age, severity or group placement. A significant interaction was found between treatment day, block and group (p = .033). Significant mt differences were found between the three blocks of intervention within individual days (p = .001). Specifically, significant differences were found over the last block of treatment (p = .032) and between successive treatment days (p = .001). Conclusion: The results indicate that for children with CP participating in RR, the number of repetitions per session is important. We hypothesized that children’s performance would plateau during a treatment day as attention waned, the opposite proved to be true. Despite the high-number of repetitions and associated cognitive demand, subjects’ performance actually trended upwards throughout the 1,024 repetitions suggesting that children were able to tolerate and learn from a high volume of repetitions

模倣学習を用いた両腕ロボット着衣介助システムのデザインと開発

The recent demographic trend across developed nations shows a dramatic increase in the aging population and fallen fertility rates. With the aging population, the number of elderly who need support for their Activities of Daily Living (ADL) such as dressing, is growing. The use of caregivers is universal for the dressing task due to the unavailability of any effective assistive technology. Unfortunately, across the globe, many nations are suffering from a severe shortage of caregivers. Hence, the demand for service robots to assist with the dressing task is increasing rapidly. Robotic Clothing Assistance is a challenging task. The robot has to deal with the following two complex tasks simultaneously, (a) non-rigid and highly flexible cloth manipulation, and (b) safe human-robot interaction while assisting a human whose posture may vary during the task. On the other hand, humans can deal with these tasks rather easily. In this thesis, a framework for Robotic Clothing Assistance by imitation learning from a human demonstration to a compliant dual-arm robot is proposed. In this framework, the dressing task is divided into the following three phases, (a) reaching phase, (b) arm dressing phase, and (c) body dressing phase. The arm dressing phase is treated as a global trajectory modification and implemented by applying the Dynamic Movement Primitives (DMP). The body dressing phase is represented as a local trajectory modification and executed by employing the Bayesian Gaussian Process Latent Variable Model (BGPLVM). It is demonstrated that the proposed framework developed towards assisting the elderly is generalizable to various people and successfully performs a sleeveless T-shirt dressing task. Furthermore, in this thesis, various limitations and improvements to the framework are discussed. These improvements include the followings (a) evaluation of Robotic Clothing Assistance, (b) automated wheelchair movement, and (c) incremental learning to perform Robotic Clothing Assistance. Evaluation is necessary for our framework. To make it accessible in care facilities, systematic assessment of the performance, and the devices’ effects on the care receivers and caregivers is required. Therefore, a robotic simulator that mimicks human postures is used as a subject to evaluate the dressing task. The proposed framework involves a wheeled chair’s manually coordinated movement, which is difficult to perform for the elderly as it requires pushing the chair by himself. To this end, using an electric wheelchair, an approach for wheelchair and robot collaboration is presented. Finally, to incorporate different human body dimensions, Robotic Clothing Assistance is formulated as an incremental imitation learning problem. The proposed formulation enables learning and adjusting the behavior incrementally whenever a new demonstration is performed. When found inappropriate, the planned trajectory is modified through physical Human-Robot Interaction (HRI) during the execution. This research work is exhibited to the public at various events such as the International Robot Exhibition (iREX) 2017 at Tokyo (Japan), the West Japan General Exhibition Center Annex 2018 at Kokura (Japan), and iREX 2019 at Tokyo (Japan).九州工業大学博士学位論文 学位記番号：生工博甲第384号 学位授与年月日：令和2年9月25日1 Introduction|2 Related Work|3 Imitation Learning|4 Experimental System|5 Proposed Framework|6 Whole-Body Robotic Simulator|7 Electric Wheelchair-Robot Collaboration|8 Incremental Imitation Learning|9 Conclusion九州工業大学令和2年

New techniques for neuro-rehabilitation: Transcranial Electric Stimulation and Virtual Reality

Recovery of motor and cognitive performances after a neurological illness remains a significant challenge for rehabilitation specialists. The traditional rehabilitative interventions are usually delivered using a multidisciplinary approach, whose results are not always satisfactory. These limitations in functional recovery have led researchers to consider alternative approaches. The hypothesis of providing new therapeutic possibilities in the different patients treated is, as a rehabilitator, very rewarding and represents a challenge for the future. The application of simple and low-cost techniques, defined by the literature as "unconventional" or “novel”, can provide new ideas not only in the field of research but above all of application in clinical reality.A suitable approach to improve the rehabilitation outcome is to utilize these novel rehabilitation techniques that act as a substitute or an addition to the traditional ones. In this context, some recent approaches have been proposed that might increase the effectiveness of a traditional treatment. Among them, two techniques have been demonstrated to be very promising, namely non-invasive brain stimulation (NIBS) and Virtual Reality (VR).In light of the foregoing, my thesis has been divided into two main lines of research, namely: a) the study of the effects of transcranial direct current stimulation (tDCS) in different neurological conditions; b) the application of VR (used alone or combined with tDCS) in the treatment of some neurocognitive disorders. A semi-immersive VR tool (ReMOVES system) has been used as a user-friendly platform providing activities based on exergames

Human Machine Interfaces for Teleoperators and Virtual Environments

In Mar. 1990, a meeting organized around the general theme of teleoperation research into virtual environment display technology was conducted. This is a collection of conference-related fragments that will give a glimpse of the potential of the following fields and how they interplay: sensorimotor performance; human-machine interfaces; teleoperation; virtual environments; performance measurement and evaluation methods; and design principles and predictive models

Proceedings of the 5th international conference on disability, virtual reality and associated technologies (ICDVRAT 2004)

The proceedings of the conferenc