Collaborative Research: A holistic human-in-the-loop framework for optimizing a personalized prosthetic arm

A wide variety of prostheses are available to persons with upper-limb loss. These include devices that simply open and close the hand to those with multiple grip options and wrist movement. However, it is difficult to determine the optimal device for an individual because there is no objective standard.

There are two main reasons for this shortcoming. First, there is currently no means to evaluate the effect of individual prosthetic features on user performance. For example, it is currently not possible to solely increase prosthetic wrist motion without changing the system weight and volume.

Accordingly, if the patient rejects the prosthesis, it is unclear whether they are rejected due to difficulties in use or because of the weight. Second, it can be difficult to assess performance with upper limb prostheses as there is no single task that is representative of all upper limb activities of daily living. The goal of this proposal is to develop a new metric, called holistic indicator, which quantifies an individual’s performance and perception of a prosthesis.

To make this possible, a cable-actuated prosthetic emulator is employed which can mimic different physical characteristics of the prosthesis and use machine learning to understand the relationship between design characteristics and user performance. The system developed in this proposal will enable the PI Team to find the optimal prosthesis for an individual by understanding their unique robot-human interaction.

The research outcomes of this proposal will be disseminated through Wearable Robotics Camp for K-12 students and FEMMES (Females Excelling More in Math, Engineering, and Science) events for female high school students to encourage STEM education among the next generation. The learning experiences of graduate and undergraduate students working on this project will be a unique opportunity to acquire multidisciplinary skill sets, build professional networks through collaboration between faculty and students in robotics, data science, and biomechanics, and foster trans-disciplinary leaders of AI-based wearable robotics.

The goal of this proposal is to design a human-in-the-loop (HITL) framework for prosthetic arm parameter optimization by determining a quantifiable holistic indicator using interpretable machine learning (ML) models. A holistic indicator is a metric for prosthesis optimization that incorporates both physical and cognitive quantitative responses. The innovation of a holistic framework is to reflect multiple critical factors during prosthesis use for optimizing a specific design parameter of interest of the upper limb prosthesis.

The framework will be developed by collecting data from individuals with upper limb amputation using a cable-actuated prosthetic emulator arm, Intelligent COnvertible Prosthetic Emulator (ICOPE), developed in the PI’s laboratory. ICOPE features off-board electronics to easily change only specific design parameters through software while maintaining the rest of the design parameters.

The data from ICOPE will be used to train interpretable ML models to determine the holistic indicator. The same data set will be collected from non-amputee participants as well, to identify the unique robot-human interaction of amputee participants for personalized prosthetic designs. This project will also include pilot studies utilizing the HITL framework with longer training periods and investigate user perception of the optimized design when the objective metrics will be shared with the user.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.