Design Article
MEMS enable medical innovation
Bob Scannell, Business Development Manager, MEMS Inertial Sensors, Analog Devices, Inc.
10/12/2012 5:01 PM EDT
Mechanical or optical
Click on image to enlarge.
Figure 3: MEMS-based inertial measurement units provide precision six- degrees-of-motion measurement in compact form factors suitable to surgical instrumentation.
Sensor selection and system level processing
There is a large variation in the performance levels of inertial sensors. Devices suitable for gaming are not able to address the high-performance navigation problem outlined here. The key MEMS specifications of interest are bias drift, vibration influence, sensitivity and noise. Precision industrial and medical navigation typically require performance levels that are an order of magnitude higher than is available from the MEMS sensors targeted for use in consumer devices. Table III below outlines general system considerations, which through analysis can help focus the sensor selection.
Most systems will implement some form of Kalman Filter to effectively merge multiple sensor types. The Kalman filter takes into account the system dynamics model, the relative sensor accuracies, and other application specific control inputs to make the best determination of actual movement. Higher accuracy inertial sensors (low noise, low drift, and stability over temperature/time/vibration/supply-variance) reduce the complexity of the Kalman filter, the number of redundant sensors required and the number of limitations placed on allowable system operational scenarios.
The two primary challenges found in any high-performance motion capture implementation are the conversion of raw sensor data to calibrated and stable sensor data, and the translation of precision sensor data into actual position/tracking information. Overcoming the first hurdle involves integration of optimized sensor processing electronics coupled with motion calibration, which is based on intimate knowledge of motion dynamics. The second hurdle requires merging an understanding of motion-dynamics with a deep knowledge of the peculiarities of the application at hand; as depicted in Figure 4.
Click on image to enlarge.
Figure 4: Precision motion detection begins with high-performance core sensors, coupled with optimized sensor processing, and embedded application intelligence.
MEMS inertial sensing is a highly mature technology in terms of both commercial viability and reliability. Beyond the well-known use cases in mobile devices and gaming, significantly more challenging needs exist in the medical and industrial fields. In these cases, substantially higher performance is required, along with much more complete integration and sensor processing.
The complexity of motion involved in medical navigation, for instance, dictates the need for starting with highly stable inertial sensors as a foundation, then building on this with optimized integration, sensor processing, and fusion.
The availability of highly accurate and environmentally robust sensor developments is driving a new surge in the adoption of MEMS inertial sensors within the medical field. These inertial MEMS devices are capable of offering advantages in precision, size, power, redundancy, and accessibility over existing measurement/sensing approaches.
Fortunately, many of the principles required for solving these next-generation medical challenges are based on proven approaches from classical industrial navigation problems, including sensor fusion and processing techniques.
About the author
Bob Scannell is theBusiness Development Manager for ADI's MEMs Inertial Sensor Products. He has been with ADI for more than 15 years in various technical marketing and business development functions ranging from Sensors to DSP to Wireless, and previously worked at Rockwell International in both design and marketing. He holds a BS degree in Electrical Engineering from UCLA (University of California, Los Angeles), and an MS in Computer Engineering from USC (University of Southern California).
Whether mechanical or optical alignment is used, approximately 30 percent of these procedures result in misalignment (defined as >3º error), which often leads to both discomfort and additional surgery. Reducing misalignment has the potential of offering less invasive and shorter surgery time, increasing post-operative patient comfort, and producing longer lasting joint replacements. Inertial sensors in the form of a full multi-axis inertial measurement unit (IMU), as shown in Figure 3, have demonstrated substantially improved accuracy for TKA.
Click on image to enlarge.
Figure 3: MEMS-based inertial measurement units provide precision six- degrees-of-motion measurement in compact form factors suitable to surgical instrumentation.
Sensor selection and system level processing
There is a large variation in the performance levels of inertial sensors. Devices suitable for gaming are not able to address the high-performance navigation problem outlined here. The key MEMS specifications of interest are bias drift, vibration influence, sensitivity and noise. Precision industrial and medical navigation typically require performance levels that are an order of magnitude higher than is available from the MEMS sensors targeted for use in consumer devices. Table III below outlines general system considerations, which through analysis can help focus the sensor selection.
|
System Variable |
Conditions/Considerations |
|
Environment |
indoor/outdoor, temperature, shock/vibration, interference sources |
|
Performance Rating / Goals |
accuracy, repeatability, speed, stability |
|
Operator |
assisted or autonomous, trained or untrained |
|
Safety |
Life Critical, Inaccessible, Redundancy |
|
Budget |
Cost/Time to Implement, Risk |
Most systems will implement some form of Kalman Filter to effectively merge multiple sensor types. The Kalman filter takes into account the system dynamics model, the relative sensor accuracies, and other application specific control inputs to make the best determination of actual movement. Higher accuracy inertial sensors (low noise, low drift, and stability over temperature/time/vibration/supply-variance) reduce the complexity of the Kalman filter, the number of redundant sensors required and the number of limitations placed on allowable system operational scenarios.
The two primary challenges found in any high-performance motion capture implementation are the conversion of raw sensor data to calibrated and stable sensor data, and the translation of precision sensor data into actual position/tracking information. Overcoming the first hurdle involves integration of optimized sensor processing electronics coupled with motion calibration, which is based on intimate knowledge of motion dynamics. The second hurdle requires merging an understanding of motion-dynamics with a deep knowledge of the peculiarities of the application at hand; as depicted in Figure 4.
Click on image to enlarge.
Figure 4: Precision motion detection begins with high-performance core sensors, coupled with optimized sensor processing, and embedded application intelligence.
MEMS inertial sensing is a highly mature technology in terms of both commercial viability and reliability. Beyond the well-known use cases in mobile devices and gaming, significantly more challenging needs exist in the medical and industrial fields. In these cases, substantially higher performance is required, along with much more complete integration and sensor processing.
The complexity of motion involved in medical navigation, for instance, dictates the need for starting with highly stable inertial sensors as a foundation, then building on this with optimized integration, sensor processing, and fusion.
The availability of highly accurate and environmentally robust sensor developments is driving a new surge in the adoption of MEMS inertial sensors within the medical field. These inertial MEMS devices are capable of offering advantages in precision, size, power, redundancy, and accessibility over existing measurement/sensing approaches.
Fortunately, many of the principles required for solving these next-generation medical challenges are based on proven approaches from classical industrial navigation problems, including sensor fusion and processing techniques.
About the author
Bob Scannell is theBusiness Development Manager for ADI's MEMs Inertial Sensor Products. He has been with ADI for more than 15 years in various technical marketing and business development functions ranging from Sensors to DSP to Wireless, and previously worked at Rockwell International in both design and marketing. He holds a BS degree in Electrical Engineering from UCLA (University of California, Los Angeles), and an MS in Computer Engineering from USC (University of Southern California).
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goafrit
10/14/2012 9:15 AM EDT
I surely agree - the field of pacemakers will never be the same with MEMS. MEMS is offering better ways of discovering abnormal heartbeats.
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agk
10/15/2012 9:28 AM EDT
Developing new sensors are quite challenging.The two primary challenges are clearly specified here by the Author. Out of these two the second one is, matching the sensor for a specific application is really a time consuming and interesting work. Once properly done it provides amazing results.
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