MEMS enable medical innovation

Capturing motion

Capturing motion to enable new instrumentation and diagnostic tools
Many medical applications such as accurately determining position and repetition rate in CPR, or the precise positioning of scanning equipment in relation to a patient’s body, can benefit from relatively basic, yet still precise, motion information.  In these cases, a single sensor type may be adequate, particularly if there are other sensor inputs, or at least fixed/known boundaries to the movement and use case.

Even with limited range of motion, or simpler motion dynamics, the individual sensors must have well understood and controlled drift factors, and it is often desirable to have embedded compensation within the sensor, as well as the ability to tune it to the application via embedded filtering.

Complex motion requires precision sensors
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 multiple axes of motion.   Being able to capture this complex, multi-dimensional motion can enable new benefits while maintaining accuracy in the most critical of environments.
 
In 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, accelerometers are sensitive to the Earth’s gravity, so they can be used to determine inclination angle. As a MEMS accelerometer is rotated through a ±1-g 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 later case, an accelerometer can be combined with a gyroscope, and post-processing of both devices can discern the linear acceleration from the tilt, based upon known motion dynamics models. This process of sensor fusion obviously becomes more complex as the system dynamics (number of axes of motion, types, and degrees of freedom of motion) increase.

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

For many applications, particularly those requiring performance beyond basic pointing (up, down, left, right) or simple movement (in motion, or stationary), multiple degrees-of-freedom motion detection is required.   For example, a six degree-of-freedom inertial sensor has the ability to detect linear acceleration on each of three (x, y, z) axes, and rotational movement on the same three axis, also referred to as roll, pitch, and yaw; as depicted in Figure 2.


Click on image to enlarge.
Fig. 2  Linear x, y and z motion, plus rotational roll, pitch and yaw make up the six degrees of motion measurement required for full motion assessment; often augmented by both magnetometers and a barometer.

Basic navigation principles
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 not affected by 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 II, 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. Inertial-based sensors perform dead-reckoning during the optical blockage, as well as enhancing system reliability by providing redundant sensing.


Table II:  Outlined are various navigational sensors widely used in industry, and their applicability to medical navigation.

Sensor Type	Major Advantage	Potential Limitations	Applicable to Medical Navigation?
GPS	Long Term Absolute Reference	Potential Blockages	No
Magnetic	No Required Infrastructure (except Earth)	Subject to Field Interference	Limited
Optical	Intuitive	Line of Sight Obstruction	Limited
Inertial	Self-Contained	Relative, not absolute  reference	Yes

One medical application outlined earlier in Table I, involves the 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 today’s 3º or larger error using 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.