Project Auxilia - Jaiden\u27s Prosthetic Arm

The main objective of this project was to create a prosthetic arm for a 15 year old boy named Jaiden Foden. Jaiden was born with only one fully developed limb as a result of a genetic disorder, Hanhart Syndrome II. His right arm becomes a residual limb below the elbow, but has two fingers which act in a “claw-like” movement. Jaiden’s left arm becomes a residual limb above the elbow, and his left leg becomes a residual limb above the knee. The goal of the arm was to increase Jaiden’s overall independence and to help in completing daily tasks, such as brushing his teeth. Additional objectives were to design the prosthetic to be adjustable, such that he could continue to use it as he grows; design the prosthetic to be relatively inexpensive to offset the overall costs of amputations and limb loss; and design it to be light and portable in order to be easily carried around and potentially applied to additional tasks. Requirements of the device included that it must be easily attachable/detachable to the user, be lightweight/portable, be relatively inexpensive, be comfortable, be resistant to skin damage, be durable, reduce overall fatigue in the user’s current right hand, and resemble a hand aesthetically. If successful, hospital charges may decrease as replacement prosthetics will be cheaper, individuals who cannot afford proper treatment or accommodations can be considered to receive this device, children will be able to use their prosthetic for an extended period of time as they grow, and children with above the elbow residual limbs (like Jaiden) will be able to have more independence

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

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 Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG

Includes bibliographical references.The patent developed by Dr. L. John [1] allows for the the detection of deep muscle activation through the combination of specially positioned monopolar surface Electromyography (sEMG) electrodes and a Blind Source Separation algorithm. This concept was then proved by Morowasi and John [2] in a 12 electrode prototype system around the bicep. This proof of concept showed that it was possible to extract the deep tissue activity of the brachialis muscle in the upper arm, however, the effect of surface electrode positioning and effectual number of electrodes on signal quality is still unclear. The hope of this research is to extend this work. In this research, a genetic algorithm (GA) is implemented on top of the Fast Independent Component Analysis (FastICA) algorithm to reduce the number of electrodes needed to isolate the activity from all muscles in the upper arm, including deep tissue. The GA selects electrodes based on the amount of significant information they contribute to the ICA solution and by doing so, a reduced electrode set is generated and alternative electrode positions are identified. This allows a near optimal electrode configuration to be produced for each user. The benefits of this approach are: 1.The generalized electrode array and this algorithm can select the near optimal electrode arrangement with very minimal understanding of the underlying anatomy. 2. It can correct for small anatomical differences between test subjects and act as a calibration phase for individuals. As with any design there are also disadvantages, such as each user needs to have the electrode placement specifically customised for him or her and this process needs to be conducted using a higher number of electrodes to begin with

The Design and Realisation of a 3D-Printed Myoelectric Prosthetic Arm for Toddlers Utilising Soft Grippers

A prosthetic device aims to improve an amputee’s ability to perform activities of daily living, by mimicking the function of a biological arm. The use of a prosthesis has also been shown to minimise some of the issues facing amputees, such as poor posture and muscular skeletal pain. Active, myoelectric-controlled prosthetic arms have primarily focused on adults, despite evidence showing the benefits of early adoption in reducing the rejection rates and aiding in proper motor neural development. This work presents SIMPA, a low-cost 3D-printed prosthetic arm with a soft-gripper based end device. The arm has been designed using CAD and 3D-scaning and manufactured using predominantly 3Dprinting techniques. This all serves the aim of reducing cost and lead-time, both crucial aspects for prosthetic manufacturing, particularly with the rapid growth rates of young children. A voluntary opening control system utilising an armband based (surface electromyography) sEMG has been developed concurrently. This simple control system acts as a base for more advanced control structures as the child develops. Grasp tests have resulted in an average effectiveness of 87%, with objects in excess of 400g being securely grasped. Force tests have shown that the arm is performing in line with current adult prosthetic devices. The results highlight the effectiveness of soft grippers as an end device in prosthetics, as well as viability of toddler-scale 3D-printed myoelectric devices