为 DARPA计划开发的两个原型手臂使用了目标肌肉神经系统，这项技术是由芝加哥康复研究院Todd Kuiken博士研发的，内容包括从被切除手臂到未使用的伤害处的肌肉区域的残留神经的传输。在临床评估中，第一个原型能够使患者完成各种功能任务，包括从口袋里拿一个信用卡。
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).
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.
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.