Informing Ankle-Foot Prosthesis Prescription through Haptic Emulation of Candidate Devices.
Informing Ankle-Foot Prosthesis Prescription through Haptic Emulation of Candidate Devices.
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DOI：
10.1109/icra.2015.7140104
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Collins SH
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文献类型：
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作者：
Caputo JM;Adamczyk PG;Collins SH
机器人假肢可以改善截肢者的行走能力，但这些设备的处方由于其高昂的成本和个人受益程度的不确定性而受到阻碍。典型的处方流程无法很好地预测个人对他们从未使用过的设备的反应，因为它的决策基于对个人当前活动水平的主观评估。我们提出了一种新方法，其中个人使用假肢模拟器“试驾”候选设备，同时对他们的步行表现进行定量评估，并提取结果以告知处方。在该系统中，假肢行为由软件而不是机械实现控制，因此用户可以快速体验各种设备。为了测试该方法的可行性，我们开发了一个原型模拟器和评估协议，利用我们之前为基础科学实验开发的硬件和方法。我们演示了各种商用假肢的仿真，包括传统假肢（例如 SACH）、动态弹性假肢（例如 FlexFoot）和动力机器人假肢（例如 BiOM® T2）。仿真相对于参考数据表现出较低的误差，并提供了每个设备的主观上令人信服的表示。我们演示了一种评估协议，该协议根据定量性能指标区分每个人的设备类别，提供可用于制定客观、个性化设备处方的反馈。
Robotic prostheses can improve walking performance for amputees, but prescription of these devices has been hindered by their high cost and uncertainty about the degree to which individuals will benefit. The typical prescription process cannot well predict how an individual will respond to a device they have never used because it bases decisions on subjective assessment of an individual’s current activity level. We propose a new approach in which individuals ‘test drive’ candidate devices using a prosthesis emulator while their walking performance is quantitatively assessed and results are distilled to inform prescription. In this system, prosthesis behavior is controlled by software rather than mechanical implementation, so users can quickly experience a broad range of devices. To test the viability of the approach, we developed a prototype emulator and assessment protocol, leveraging hardware and methods we previously developed for basic science experiments. We demonstrated emulations across the spectrum of commercially available prostheses, including traditional (e.g. SACH), dynamic-elastic (e.g. FlexFoot), and powered robotic (e.g. BiOM® T2) prostheses. Emulations exhibited low error with respect to reference data and provided subjectively convincing representations of each device. We demonstrated an assessment protocol that differentiated device classes for each individual based on quantitative performance metrics, providing feedback that could be used to make objective, personalized device prescriptions.