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Sensors/MEMS  

Medical electronics gets personal, goes mobile

Posted: 26 Feb 2013     Print Version  Bookmark and Share

Keywords:medical devices  Vital signs monitoring  heart rate monitor 

It's important to note that it is not just these extreme user situations which need insight into the nature of the sensor output and significant signal analysis and processing. Consider a "fall detector" aimed primarily at the elderly living alone. What makes such detectors possible, among other factors, are ICs such as the ADXL362 from Analog ('ue'?) Devices, an ultra-low power, ±2/±4/±8g, 3-axis MEMS accelerometer that consumes less than 3µA across its full range of output data rates, and just 300 nA in motion-triggered wake-up mode.

This device forms the sensing core of a continuous motion-and-fall detector, an application where portability and long battery life are critical features of the product. Its internal 12bit ADC provides 1 mg/LSB resolution on the 2g range, and interfaces to a microcontroller via an SPI port.

But it takes more than a sensor alone for a successful end product. OEM designers must implement algorithms which interpret and act on the waveforms provided by the device, and these waveforms are not trivial: they occur along multiple axes and with uncertain timings (figure 1).

Figure 1: Complex waveforms captured by a three-axis accelerometer during a "fall."

What about the gym or the car, which is where people increasingly spend their time, and which have increasingly become electronics-laden environments? For exercise equipment such as a treadmill or elliptical trainer, the user can grab the handles—and these can be a placement location for sensors. In the car, there is a built-in location for mounting VSM sensors: the steering wheel.

By attaching conducting electrodes, and an array of LEDs and photodiodes, to the steering wheel it's as if the driver's hands are being read by an old-fashioned palm reader, except in this case, the readings are not speculative. Of course, advances in the materials used and construction of the wheel will be needed to make this practical.

Measuring values and changes in skin capacitance, conductivity, temperature, and position allows such a configuration to determine heart rate, sleep/drowsiness state, and even stress levels. More sophisticated algorithms can combine these various parameters to present a more comprehensive picture of the driver's physical and even emotional well being.

Mixed-signal, low-cost ICs for this function make this application possible. For example, the AD8232 heart rate monitor (HRM) analogue front end (AFE) is an integrated signal-conditioning block for single-lead electrocardiogram (ECG) and other bio-potential measurement applications (figure 2). It converts the tiny, noisy signals from body electrodes into large, filtered signals that can be easily converted by a medium-resolution ADC.

Unlike clinical ECG units, which monitor up to 12 leads, designs based on the AD8232 are connected to just two or three electrodes. While this clearly simplifies the physical connection, here again we see the "conflict" among technical needs, product design, and user habits: The auto steering wheel (or gym machine) is certainly not a clinical setting. For viable ECG data for heart rate monitoring, the system needs both hands of the driver on the wheel or handlebars—which, in reality, is not often the case.

Figure 2: The AD8232 ECG AFE provides signal conditioning for the miniscule, noise-laden bio-potential signals of a single-lead electrocardiogram (ECG).

In addition, unlike the setting where the user is sitting still or lying down, the constant motion of the driver or a person on the machine affects the reading continuity. For this reason, the AD8232 includes a two-pole high-pass filter to eliminate these misleading signals. Once the signal has settled, the filter automatically switches to a lower cut-off frequency, to improve the overall noise behaviour – this is the "fast restore" function of the AD8232.

The smartphone opens another avenue
The soon-to-be-ubiquitous smart phone is potentially rich with apps that use native sensors to determine vital parameters such as activity levels or sleep patterns, with the built-in accelerometer used to detect heart rate via the image sensor.

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