Multi-axis inertial sensors have become increasingly popular in the medical application market. Inertial MEMS components with multi-axis sensing capability, regardless of size, power consumption, accuracy and reliability, can meet the stringent requirements of medical applications, such as precision medical instruments such as surgical navigation tools. Import.
Navigation is related to cars, trucks, airplanes, ships and people. However, it has also begun to play an important role in the field of medical technology, where precision surgical instruments and robots require navigation. The design requirements for surgical navigation tools have a wide range of commonalities with traditional vehicle navigation, but the former also presents unique challenges, such as indoor use, and the inability to obtain Global Positioning System (GPS) support, which requires higher performance.
This article examines the unique challenges of medical navigation applications and explores possible solutions from sensor mechanisms to system characteristics, and introduces methods to enhance sensing performance, such as Kalman filtering.
Multi-axis MEMS sensor transforms medical information
Micro-Electro-Mechanical Systems (MEMS) have become a mature technology that most people encounter every day. It makes cars safer, enhances the usability of mobile phones, and optimizes the performance of tools and sports equipment to improve the level of medical care for patients.
MEMS components for linear motion detection are typically based on a micromachined polysilicon surface structure that is formed over a germanium wafer and suspended by a polysilicon spring on the surface of the wafer to provide resistance to acceleration forces. At acceleration, the deflection of the MEMS shaft is measured by a differential capacitor consisting of a separate fixed plate and a moving quality connection plate. As a result, the motion unbalances the differential capacitor, causing the magnitude of the sensor output to be proportional to the acceleration.
For example, when the car suddenly decelerates suddenly due to a collision, the MEMS shaft in the airbag sensor will generate the same motion, causing the capacitor to be unbalanced, and finally the signal will trigger the airbag to open. This basic accelerometer structure can be adjusted according to different application performance parameters, and after adding data processing functions, it can accurately indicate the inclination, speed and even position. Another technically relevant structure is a gyroscope that detects the rate of rotation in the form of degrees per second.
The ability to accurately detect and measure motion through a tiny component that consumes very little power is valuable for almost any application involving motion. Table 1 lists basic medical applications by type of exercise.
Although simple motion detection is valuable, such as linear motion on one axis, most applications involve multiple types of motion on multiple axes. Capturing this multi-dimensional motion state not only brings new benefits, but also maintains accuracy where off-axis disturbances can affect single-spindle motion measurements.
To accurately measure the motion experienced by an object, multiple types (such as linear and rotational) sensors must be combined, such as accelerometers that are sensitive to the Earth's gravity and can be used to determine the tilt angle. In other words, when a MEMS accelerometer is rotated in a ±1 g gravitational field (±90 degrees), it can convert the motion into an angular representation.
However, accelerometers cannot distinguish between static acceleration (gravity) and dynamic acceleration. Thus, the accelerometer can be combined with a gyroscope to utilize the additional data processing capabilities of the combined components to resolve linear acceleration and tilt (ie, when the output of the gyroscope shows that the rotation coincides with the apparent tilt of the accelerometer record). As the dynamics of the system (the number of axes of motion and the freedom of motion) increase, the sensor fusion process becomes more complicated.
It is also important to understand the impact of the environment on the accuracy of the sensor. One obvious factor is the temperature, which can be calibrated. In fact, high-precision sensors can be recalibrated and dynamically compensated by themselves. Another less obvious consideration is the potential vibration, even a slight vibration that shifts the accuracy of the rotation rate sensor. This effect is called linear acceleration effect and vibration correction, and its effect can be severe. It depends on the quality of the gyroscope. In this case, sensor fusion can also improve performance by using an accelerometer to detect linear acceleration and then using this information and calibration information of the gyroscope's linear acceleration sensitivity for correction.
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