MEMS Motion Sensing Enables Next-Generation Medical Systems

Precision navigation, typically associated with applications developed for land, air and sea vehicles, is increasingly being used in medical applications ranging from surgical instrumentation to robotics. And while the design requirements of a surgical navigation system share broad similarities with traditional vehicle navigation, there are also distinct new challenges posed by the environment and the level of required performance.

This article looks at the unique challenges of medical navigation applications and explores possible solutions ranging from various sensor mechanisms to necessary sensor processing to the unique system characteristics and data processing required. Critical sensor specifications will be reviewed and explained for their individual contribution, and more importantly, the potential error and drift mechanisms will be discussed to aid in sensor selection. Opportunities and approaches for sensor enhancement through integration, sensor fusion, and sensor processing (such as Kalman filtering) will be highlighted as well.


Translating the Detection of Linear and Rotational Motion into Healthcare Value

Silicon-based accelerometers and gyroscopes known as micro-electromechanical systems or MEMS (Figure 1) are commonly found today in a wide range of devices. These inertial sensors detect and measure motion, with minimal power and size, and are valuable to nearly any application where movement is involved, and even those where lack of motion is critical. Table 1 outlines some of the basic pertinent medical applications by motion type. Later, more advanced applications where combinations of motion in complex scenarios that present additional challenges will be discussed.

Most Motion is Complex in Nature

While simple motion detection – linear movement along one axis, for example –is valuable to a number of applications (such as detecting whether an elderly person has fallen), a majority of applications involve multiple types and axes of motion. Being able to capture this complex, multi-dimensional motion can not only enable new benefits, but is also key to maintaining accuracy in the most critical environments.

30_bIn many cases, it is necessary to combine multiple sensor types (linear and rotational, for instance) in order to precisely determine the motion an object has experienced. As an example, an accelerometer can be used to determine inclination angle since it is sensitive to the Earth’s gravity. As a MEMS accelerometer is rotated through a +/- 1g field, (+/- 90o), it is able to translate that motion into an angle representation. However, the accelerometer cannot distinguish static acceleration (gravity) from dynamic acceleration. In the latter case, an accelerometer can be combined with a gyroscope, and post-processing of both devices can discern the linear acceleration versus tilt, based upon known motion dynamic models. This process of sensor fusion obviously becomes more complex as the system dynamics (number of axes of motion and degrees of freedom of motion) increases.

It is also important to understand the environmental influences on sensor accuracy. Temperature is an obvious key concern, and can typically be corrected for; in fact higher precision sensors are pre-calibrated and will dynamically compensate themselves. A less obvious factor to consider is the potential for even slight vibrations to produce shifts in accuracy of rotational rate sensors. These effects, known as linear acceleration effect and vibration rectification, can be significant depending on the quality of the gyroscope. Sensor fusion is relied on to improve performance by using an accelerometer to detect linear acceleration and applying this knowledge, along with a calibrated understanding of a gyroscope’s linear acceleration sensitivity, for correction.


For many applications, particularly those requiring performance beyond basic ‘pointing’ (up, down, left, right) or simple movement (in motion, or not), multiple degreesof- freedom motion detection is required. For example, a six degree-of-freedom inertial sensor is defined as having the ability to detect linear acceleration on each of three (x,y,z) axis, and rotational movement on the same three axis, also referred to as roll, pitch and yaw; as depicted in Figure 2.

Navigation from Vehicles to Surgical Instruments

The use of inertial sensors as a navigation aid has become prevalent in industry. Typically, they are used in conjunction with other navigation devices such as GPS. When GPS access is unreliable, inertial guidance fills the gap in coverage with what is called “dead-reckoning.” Other sensors, including optical and magnetic, may be added depending on the environment and the performance goals. Each sensor type has its own limitations. MEMS inertial sensors provide the potential to fully compensate for these other sensor inaccuracies since they are free from many of the same interferences and do not require external infrastructure: no satellite, magnetic field, or camera is needed – just inertia). The major navigational sensor approaches are outlined in Table 2, along with their strengths and potential limitations.

As with the potential for GPS blockage in vehicle navigation, the medical corollary is optical guidance and the potential for line-of-sight blockages. Inertially based sensors perform dead-reckoning during the optical blockage, as well as enhance system reliability by providing redundant sensing.


Medical Navigation

One medical application outlined in Table 2 involves use of inertial sensors in the operating room for more accurate alignment of artificial knee or hip joints with a patient’s unique anatomical structure. The goal here is to improve joint alignment to less than 1º error from the patient’s natural alignment axis versus what is 3º or larger error today with purely mechanical alignment approaches. Greater than 95 percent of total knee arthoplasty (TKA) procedures today are done with mechanical alignment. Computer-assisted approaches using optical alignment have only slowly begun to replace some mechanical procedures, likely due to the equipment overhead required. Whether mechanical or optical alignment is used, approximately 30 percent of these procedures result in misalignment (defined as >3º error), which leads to both discomfort and often 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 been shown to provide substantial improvement in accuracy for TKA.


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 3 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 then 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.

MEMS Adoption in Medical Applications

Motion capture within the most complex medical applications poses both highly challenging and computationally intensive design problems. 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. Within medical navigation, the complexity of motion and the requirements on precision and reliability will drive the need for:

  • Multiple sensors
  • Additional sensor post-processing
  • Sophisticated algorithms
  • Complex test/compensation schemes


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.


Bob Scannell is a business development manager for ADI’s inertial MEMS 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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