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

对于失去上肢的人来说，有各种各样的假体可用。这些设备包括那些只需打开和关闭手的设备，这些设备具有多个握手选项和手腕运动。然而，由于没有客观的标准，很难确定适合个人的最佳装置。造成这一缺陷的主要原因有两个。首先，目前还没有方法来评估个别假肢功能对用户性能的影响。例如，在不改变系统重量和体积的情况下，目前不可能单独增加假体手腕的运动。因此，如果患者拒绝使用假体，则不清楚是因为使用困难还是因为重量。其次，评估上肢假体的性能可能很困难，因为没有一个单一的任务可以代表所有上肢的日常生活活动。这项提议的目标是开发一种新的衡量标准，称为整体指标，它量化了个人对假体的表现和感知。为了实现这一点，采用了电缆驱动的假肢仿真器，它可以模拟假体的不同物理特性，并使用机器学习来理解设计特性和用户性能之间的关系。该提案中开发的系统将使PI团队能够通过了解个人独特的机器人-人类交互来找到适合个人的最佳假肢。这项提议的研究成果将通过面向K-12学生的可穿戴机器人夏令营和面向女高中生的女性(女性在数学、工程和科学方面更出色)活动来传播，以鼓励下一代的STEM教育。参与该项目的研究生和本科生的学习经历将是一个独特的机会，可以获得多学科的技能集，通过机器人学、数据科学和生物力学的师生合作建立专业网络，并培养基于人工智能的可穿戴机器人的跨学科领导者。该提案的目标是通过使用可解释的机器学习(ML)模型确定可量化的整体指标，设计一个用于假肢参数优化的人在环(HITL)框架。整体指标是假体优化的指标，它包含了身体和认知的定量反应。整体框架的创新之处在于反映了假体使用过程中的多个关键因素，以优化上肢假体感兴趣的特定设计参数。该框架将通过使用电缆驱动假肢仿真器手臂收集上肢截肢患者的数据来开发，智能可转换假肢仿真器(ICope)是由PI的实验室开发的。ICope具有非机载电子设备，可以通过软件轻松更改特定的设计参数，同时保持其余的设计参数。来自iCope的数据将用于训练可解释的ML模型，以确定整体指标。同样的数据集也将从非截肢者参与者那里收集，以识别截肢者参与者为个性化假肢设计而进行的独特的机器人-人类交互。该项目还将包括利用HITL框架进行的试点研究，并在客观指标将与用户共享时调查用户对优化设计的看法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.