Mechanical or optical
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.
3: MEMS-based inertial measurement units provide precision six-
degrees-of-motion measurement in compact form factors suitable to
Sensor selection and system level processing
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.
indoor/outdoor, temperature, shock/vibration, interference sources
Performance Rating / Goals
accuracy, repeatability, speed, stability
assisted or autonomous, trained or untrained
Life Critical, Inaccessible, Redundancy
Cost/Time to Implement, Risk
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
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.
4: Precision motion detection begins with high-performance core
sensors, coupled with optimized sensor processing, and embedded
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
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.
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
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