Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

Preparation of NiO catalyst on FeCrAI substrate using various techniques at higher oxidation process

The cheap nickel oxide (NiO) is a potential catalyst candidate to replace the expensive available platinum group metals (PGM). However, the current methods to adhere the NiO powder on the metallic substrates are complicated. Therefore, this work explored the development of nickel oxide using nickel (Ni) on FeCrAl substrate through the combination of nickel electroplating and oxidation process for catalytic converter application. The approach was started with assessment of various nickel electroplating process based on the weight gain during oxidation. Then, the next experiment used the best process in which the pre-treatment using the solution of SiC and/or Al2O3 in methanol. The specimens then were carried out to short term oxidation process using thermo gravimetric analysis (TGA) at 1000 o C. Meanwhile, the long term oxidation process was conducted using an automatic furnace at 900, 1000 and 1100 o C. The atomic force microscopy (AFM) was used for surface analysis in nanometer range scale. Meanwhile, roughness test was used for roughness measurement analysis in micrometer range scale. The scanning electron microscope (SEM) attached with energy dispersive X-ray (EDX) were used for surface and cross section morphology analysis. The specimen of FeCrAl treated using ultrasonic prior to nickel electroplating showed the lowest weight gain during oxidation. The surface area of specimens increased after ultrasonic treatment. The electroplating process improved the high temperature oxidation resistance. In short term oxidation process indicated that the ultrasonic with SiC provided the lower parabolic rate constant (kp) and the Al2O3 and NiO layers were also occurred. The Ni layer was totally disappeared and converted to NiO layer on FeCrAl surface after long term oxidation process. From this work, the ultrasonic treatment prior to nickel electroplating was the best method to adhere NiO on FeCrAl substrate

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

A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter (EKF) and muscle activations obtained from the sEMG signals were taken as the inputs of the proposed NMS model to determine individual muscle force. The result shows that our NMS model can predict individual muscle force accurately, with the ability to reflect subject-specific joint dynamics and neural control solutions. Our method incorporates sEMG and motion data, making it possible to get a deeper understanding of neurological, physiological, and anatomical characteristics of human dynamic movement. We demonstrate the potential of the proposed NMS model for evaluating the function of upper limb movements in the field of neurorehabilitation

A neural prosthesis to improve gait in people with muscle weakness

Functional Electrical Stimulation (FES) can be used to induce contractions in muscles that do not receive adequate signals from the nervous system. People who have lost function in their foot and/or ankle following a stroke may benefit from a neural prosthesis. In this project, we designed, built and tested a neural prosthesis to assist people with muscle weakness who are at risk of falling. The neural prosthesis was designed to stimulate the gastrocnemius (GN) muscle during the push off phase using a manual switch. The neural prosthesis included a FES unit for muscle stimulation, electronic circuitry, and sensors including inertial measurement units (IMUs) and foot pressure sensors to detect movement characteristics. The neural prosthesis was tested on a healthy individual who walked in a straight line while the neural prosthesis collected data. Tests were performed with and without the neural prosthesis activated. The neural prosthesis was able to successfully collect gait data and cause contraction in the GN muscle. The test results showed that the GN muscle stimulation changed the gait by inducing a plantarflexion movement on the foot and expediting the toe off event. The performance of the neural prosthesis was evaluated using a commercial camera motion capture system. The IMUs and the motion capture system had and an average correlation coefficient around 95% which was close to some literature. This research showed that a neural prosthesis utilizing low cost IMUs was able to estimate the joint angle while walking. In addition, the device had an observable effect on the gait when tested on a healthy individual

Treadmill training augmented with real-time visualisation feedback and function electrical stimulation for gait rehabilitation after stroke : a feasibility study

Motor rehabilitation typically requires patients to perform task-specific training, in which biofeedback can be instrumental for encouraging neuroplasticity after stroke. Treadmill training augmented with real-time visual feedback and functional electrical stimulation (FES) may have a beneficial synergistic effect on this process. This study aims to develop a multi-channel FES (MFES) system with stimulation triggers based on the phase of gait cycle, determined using a 3D motion capture system. A feasibility study was conducted to determine whether this enhanced treadmill gait training systemis suitable for stroke survivors in clinical practice. The real-time biomechanical visual feedback system with computerised MFES was developed using six motion-capture cameras installed around a treadmill.;This system was designed to stimulate the pretibial muscle for correcting foot drop problems, gastro-soleus for facilitating push-off, and quadriceps and hamstring for improving knee stability. Dynamic avatar movement and step length/ratio were displayed on a monitor, providing patients with real-time visual biofeedback. Participants received up to 20 minutes of enhanced treadmill training once or twice per week for 6 weeks. Training programme, pre- and post-training ability, and adverse events of each participant were recorded. Feedback was also collected from participants and physiotherapists regarding their experience. Eight out of ten participants fully completed their programme.;In total, 67 training sessions were carried out. All participants had a good attendance rate. The number and duration of training sessions ranged from 5 to 20, and 11 to 20 minutes, respectively. The MFES system successfully improved gait patterns during training, and feedback from participants and physiotherapists regarding their experience of the research intervention was overwhelmingly positive. In conclusion, this enhanced treadmill gait training system is feasible for use in gait rehabilitation after stroke. However, a well-designed clinical trial with a larger sample size is needed to determine clinical efficacy on gait recovery.Motor rehabilitation typically requires patients to perform task-specific training, in which biofeedback can be instrumental for encouraging neuroplasticity after stroke. Treadmill training augmented with real-time visual feedback and functional electrical stimulation (FES) may have a beneficial synergistic effect on this process. This study aims to develop a multi-channel FES (MFES) system with stimulation triggers based on the phase of gait cycle, determined using a 3D motion capture system. A feasibility study was conducted to determine whether this enhanced treadmill gait training systemis suitable for stroke survivors in clinical practice. The real-time biomechanical visual feedback system with computerised MFES was developed using six motion-capture cameras installed around a treadmill.;This system was designed to stimulate the pretibial muscle for correcting foot drop problems, gastro-soleus for facilitating push-off, and quadriceps and hamstring for improving knee stability. Dynamic avatar movement and step length/ratio were displayed on a monitor, providing patients with real-time visual biofeedback. Participants received up to 20 minutes of enhanced treadmill training once or twice per week for 6 weeks. Training programme, pre- and post-training ability, and adverse events of each participant were recorded. Feedback was also collected from participants and physiotherapists regarding their experience. Eight out of ten participants fully completed their programme.;In total, 67 training sessions were carried out. All participants had a good attendance rate. The number and duration of training sessions ranged from 5 to 20, and 11 to 20 minutes, respectively. The MFES system successfully improved gait patterns during training, and feedback from participants and physiotherapists regarding their experience of the research intervention was overwhelmingly positive. In conclusion, this enhanced treadmill gait training system is feasible for use in gait rehabilitation after stroke. However, a well-designed clinical trial with a larger sample size is needed to determine clinical efficacy on gait recovery

Wearable vibrotactile stimulation for upper extremity rehabilitation in chronic stroke: clinical feasibility trial using the VTS Glove

Objective: Evaluate the feasibility and potential impacts on hand function using a wearable stimulation device (the VTS Glove) which provides mechanical, vibratory input to the affected limb of chronic stroke survivors. Methods: A double-blind, randomized, controlled feasibility study including sixteen chronic stroke survivors (mean age: 54; 1-13 years post-stroke) with diminished movement and tactile perception in their affected hand. Participants were given a wearable device to take home and asked to wear it for three hours daily over eight weeks. The device intervention was either (1) the VTS Glove, which provided vibrotactile stimulation to the hand, or (2) an identical glove with vibration disabled. Participants were equally randomly assigned to each condition. Hand and arm function were measured weekly at home and in local physical therapy clinics. Results: Participants using the VTS Glove showed significantly improved Semmes-Weinstein monofilament exam, reduction in Modified Ashworth measures in the fingers, and some increased voluntary finger flexion, elbow and shoulder range of motion. Conclusions: Vibrotactile stimulation applied to the disabled limb may impact tactile perception, tone and spasticity, and voluntary range of motion. Wearable devices allow extended application and study of stimulation methods outside of a clinical setting

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level