Faculty and Staff

While PhD dissertations are typically accessible many other terminal degree projects remain invisible and inaccessible to a greater audience. Over the past year and a half, librarians at Arizona State University collaborated with faculty and departmental administrators across a variety of fields to develop and create institutional repository collections that highlight and authoritatively share this type of student scholarship with schools, researchers, and future employers. This poster will present the benefits, challenges, and considerations required to successfully implement and manage these collections of applied final projects or capstone projects. Specifically, issues/challenges related to metadata consistency, faculty buy-in, and developing an ingest process, as well as benefits related to increased visibility and improved educational and employment opportunities will be discussed. This interactive presentation will also discuss lessons learned from the presenter’s experiences in context of how they can easily apply to benefit their respective institutions.

Digital technology has enabled us to record and share our memories and histories faster and in greater numbers than previously imagined. However digital files rely on hardware, software, and descriptive information to be used. As formats change and equipment to read them goes out of use we are all challenged to connect our present to our future. How long do you want your digital files to last? Decades or even a few years from now will you still be able to access and enjoy those pictures, documents and other digital items you create today?

Libraries, museums and archives spend countless hours and resources preserving physical items from the past and present, but may be forfeiting the longevity of our digital work and connecting to future generations through unintended neglect. Using practical examples and employing best practices of research institutions, participants will learn important first steps to digital preservation including the importance of metadata to personal history, recommended file formats, and approaches they can immediately use to ensure the work they create today will still be enjoyed tomorrow. Help yourself, your organization, and your patrons continue to connect their digital heritage to the generations yet to come.

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human–robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro.

The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.

Introduction: Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution.

Methods: We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach’s ability to control hand posture and finger forces.

Results: Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83–99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution.

Discussion: This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.