使用基于模型設(shè)計(jì)開發(fā)世界上最先進(jìn)的假肢

很少有人知道當(dāng)手臂拿起一個球時神經(jīng)、臂膀和傳感系統(tǒng)之間的交互。為了模擬這一自然反應(yīng)過程，可以通過微處理器、嵌入式控制軟件、執(zhí)行機(jī)構(gòu)和傳感器來構(gòu)造這一系統(tǒng)從而來研究它們之間的復(fù)雜關(guān)系。這也是美國國防高級研究計(jì)劃署（DARPA）革命性假肢計(jì)劃所面臨的挑戰(zhàn)。

美國約翰霍普金斯大學(xué)應(yīng)用物理實(shí)驗(yàn)室是領(lǐng)導(dǎo)性的全球團(tuán)隊(duì)，包括政府機(jī)構(gòu)、大學(xué)、私有企業(yè)，他們的任務(wù)是開發(fā)世界上最先進(jìn)的假肢，此假肢由神經(jīng)輸入控制，使佩戴者感覺是一個真的手臂一樣能夠以一定的速度、靈敏度和力去運(yùn)動。先進(jìn)的傳感反饋技術(shù)能夠感知物理輸入，如壓力、力和溫度。

這個項(xiàng)目中具有里程碑意義的關(guān)鍵部分是虛擬綜合環(huán)境的開發(fā)，一個完整的手臂系統(tǒng)的仿真環(huán)境使用The Mathworks工具和基于模型設(shè)計(jì)。虛擬綜合環(huán)境具有標(biāo)準(zhǔn)化的架構(gòu)和定義完善的界面，能夠使二十多不同領(lǐng)域?qū)＜液芎玫睾献鳌?/p>

The Mathworks工具基于模型設(shè)計(jì)也被用在其他開發(fā)階段，包括對臂的機(jī)械系統(tǒng)進(jìn)行建模、測試新的神經(jīng)解碼算法和開發(fā)與驗(yàn)證控制算法。

為 DARPA計(jì)劃開發(fā)的兩個原型手臂使用了目標(biāo)肌肉神經(jīng)系統(tǒng)，這項(xiàng)技術(shù)是由芝加哥康復(fù)研究院Todd Kuiken博士研發(fā)的，內(nèi)容包括從被切除手臂到未使用的傷害處的肌肉區(qū)域的殘留神經(jīng)的傳輸。在臨床評估中，第一個原型能夠使患者完成各種功能任務(wù)，包括從口袋里拿一個信用卡。

Virtual Integration Environment Architecture

The VIE architecture consists of five main modules: Input, Signal Analysis, Controls, Plant, and Presentation.

The Input module comprises all the input devices that patients can use to signal their intent, including surface electromyograms (EMGs), cortical and peripheral nerve implants, implantable myoelectric sensors (IMESs) and more conventional digital and analog inputs for switches, joysticks, and other control sources used by clinicians. The Signal Analysis module performs signal processing and filtering. More important, this module applies pattern recognition algorithms that interpret raw input signals to extract the user’s intent and communicate that intent to the Controls module. In the Controls module, those commands are mapped to motor signals that control the individual motors that actuate the limb, hand, and fingers.

The Plant module consists of a physical model of the limb’s mechanics. The Presentation module produces a three-dimensional (3D) rendering of the arm’s movement (Figure 1).

圖1 假肢三維視圖

Interfacing with the Nervous System

Simulink? and the VIE were essential to developing an interface to the nervous system that allows natural and intuitive control of the prosthetic limb system. Researchers record data from neural device implants while the subjects perform tasks such as reaching for a ball in the virtual environment. The VIE modular input systems receive this data, and MATLAB? algorithms decode the subject’s intent by using pattern recognition to correlate neural activity with the subject’s movement (Figure 2). The results are integrated back into the VIE, where experiments can be run in real time.

圖2 紐布朗斯威克大學(xué)開發(fā)了MATLAB應(yīng)用程序，記錄用于模式識別的運(yùn)動數(shù)據(jù)。

The same workflow has been used to develop input devices of all kinds, some of which are already being tested by prosthetic limb users at the Rehabilitation Institute of Chicago.

Building Real-Time Prototype Controllers

The Signal Analysis and Controls modules of the VIE form the heart of the control system that will ultimately be deployed in the prosthetic arm. At APL, we developed the software for these modules. Individual algorithms were developed in MATLAB using the Embedded MATLAB? subset and then integrated into a Simulink model of the system as function blocks. To create a real-time prototype of the control system, we generated code for the complete system, including the Simulink and Embedded MATLAB components, with Real-Time Workshop?, and deployed this code to xPC Target?.

This approach brought many advantages. Using Model-Based Design and Simulink, we modeled the complete system and simulated it to optimize and verify the design. We were able to rapidly build and test a virtual prototype system before committing to a specific hardware platform. With Real-Time Workshop Embedded Coder? we generated target-specific code for our processor. Because the code is generated from a Simulink system model that has been safety-tested and verified through simulation, there is no hand-coding step that could introduce errors or unplanned behaviors. As a result, we have a high degree of confidence that the Modular Prosthetic Limb will perform as intended and designed.

Physical Modeling and Visualization

To perform closed-loop simulations of our control system, we developed a plant model representing the inertial properties of the limb system. We began with CAD assemblies of limb components designed in SolidWorks? by our partners. We used the CAD assemblies to automatically generate a SimMechanics? model of the limb linked to our control system in Simulink.

Finally, we linked the plant model to a Java? 3D rendering engine developed at the University of Southern California to show a virtual limb moving in a simulated environment.

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