Welcome to installment number 21 of the Design Challenge Project Summary series here at Element14. For those of you who are new to my content, in this series I will pick a single Design Challenge project from the current challenge, and write a short summary of the project to date. Over the course of each challenge, I try to revisit each project at least once, and I am sure that some project summaries will get more than one update if they themselves are updated frequently. Some project creators like to keep their own project summary going, and this series is not meant to overshadow those post, but to highlight each project from an outsider's perspective.

 

The subject of this installment is project The Intelligent Elbow Motion-Assistance Actuator by F. Yao (fyaocn). The goal of this project is to develop a simple electric apparatus that will enable someone with a debilitating elbow injury to resume a normal lifestyle thanks to motion assistance provided by the device. The project will feature the NXP FRDM-KV31NXP FRDM-KV31  development board and FRDM-MC-LVPMSMFRDM-MC-LVPMSM. motor control board to control the mechanical motion of the device.

 

F. Yao’s first post to the project was an introduction to the project. He says that the project consist of “two parts. On part is intelligent arm gesture sensing and pattern matching implemented by FRDM-KV31. The other part is bi-direction elbow motion actuator that is implement by FRDM-MC-LVPMSM and motor with optional gear reducer if more power shall be provided.” He went on to say that it pains him to see seniors stuck in armchairs stuck at home because of loss of muscle mass, and the resulting loss of physical strength.

 

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In the project’s second update Yao quickly touched on how elbow assisting devices function, and how the programming has to work together to predict movement. Update number three was all about installing the Kinetis Design Studio and the KSDK1.3, while update number four focused on informing the readers about how the device’s hardware will work in conjunction with its software. Yao wrapped up the post by laying out a few key issues that will need to be tackled before the project can be considered a success.

 

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In the project’s fifth update post, Yao walked us through the installation of the Sensor Fusion Library, and how it will be utilized in the project. The “Sensor Fusion Library 5.0 supports  the Freescale FXOS8700CQFreescale FXOS8700CQ low-power, six-axis Xtrinsic sensor, which is interfaced through an I2C bus and two GPIO signals,” he said. “This is one of the key features for the implementation of this design, which requires the movement gesture capture and pattern matching. This library fusions the data of accelerometer and magnetometer, guiding the motor control for direction and auxiliary power.”

 

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Installment number six was a tutorial on how to install the Kinetis Motor Suite which includes firmware targeting the Kinetis V series of microcontrollers, and an intuitive PC-based graphical user interface. The tutorial was a basic overview of the installation process, but no motor control demo was shown due to Yao still awaiting the arrival of his challenger kit.

 

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In update number seven , Yao clues the readers in on the NXP hardware that will be utilized in his design. Like other competitors in this challenge, he notes that the supplied motor is simply too fast for his application, and he will need to gear it down using a gearbox or some other means of geared reduction. In addition, he discusses the use of a six axis accelerometer, but notes that movement from other parts of the body could cause false measurements, and cause the elbow assist device to not function as it should. He plans on fixing this using two hall effect sensors and a custom PCB.

 

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Update number eight introduced us to the Invensense MPU6050 breakout module. The MPU650 MPU650 is a MEMS accelerometer and MEMS gyrometer built into one nice SOP package that utilizes the I2C bus for communication with the MCU Yao says that a rotary frame is attached forearm and hind-arm The  FRDM-KV31FFRDM-KV31F is fixed to one part of the frame and MPU6050 shall be affixed to another one This is to provide status of the arm movement by comparing the discrepancy of two movement sensors

 

That is going to wrap up my project summary coverage of The Intelligent Elbow Motion-Assistance Actuator by F. Yao. I will revisit this project when more progress has been made. Until then, follow the project's progress by visiting its blog page. Tune in next week for another Design Challenge Project Summary here at Element14. Until then, Hack The World, and Make Awesome!