I might have a problem. My heart rate is 150 BPM, which means I might be suffering from tachycardia according to WebMD. Or maybe I’m just exercising and am well within my heart rate range. Without the context, that 150 BPM number is absolutely worthless.
To understand what the number really means, a doctor is going to have to rely on the information I tell her. And let’s be honest; I’m a liar. We all are when it comes to our physical fitness. We all omit that chocolate cake we had for breakfast while exaggerating that nightly run around the block into a full mile. It’s called the Hawthorne effect, and it means we’re generally useless when it comes to providing context for any of our biological metrics.
That’s where MEMS comes in. In general, MEMS sensors track motion within a 3D space. The figure below shows a 10-axis frame of reference, which means that by using four different MEMS devices (Accelerometer, Gyroscope, Magnetometer, and a Pressure Sensor), this device could potentially track motion along the XYZ axis along with the up and down pressure sensor.
Source: Freescale
This ability to track motion is revolutionizing our healthcare. We don’t even need to add all these sensors, though each one does provide an extra layer of context. By combining something as simple as an accelerometer (the average cost for a MEMS sensor is between $1 – $2) with a heart rate monitor, we automatically know the context for someone’s heart rate and can determine if that 150 BPM mentioned earlier is spiking when we’re sleeping or if we’re in the middle of a five-mile run.
In order to make a system like this work, we are also going to need a low power MCU to aggregate the data the sensors are collecting. Right now the most popular part is a 16bit MCU that costs about $0.60. Manufacturers are liking this part because of its low cost and low power consumption, while having just enough memory to not be overkill. These wearable appcessories are then going to send this aggregated data to a tablet or smart phone, where a more complex sensor fusion could take place. TI has a popular low cost Bluetooth chip (CC2540) that is under $2, so all together, this is a very low cost system that aggregates a ton of personal data and transmits it to a dashboard for our consumption (smartphone or tablet).
Texas Instruments is one of the vendors behind these types of systems. They have relationships with several MEMS vendors in order to off their SensorTag development kit and reference design for Bluetooth low energy sensor applications. The figure below uses TI’s CC2541, a 2.4GHz Bluetooth low-energy SoC combined with the Sensirion SHT21 humidity sensor, InvenSense’s IMU3000 Gyroscope, the Kionix KTXJ9 accelerometer, Freescale’s MAG3110 magnetometer, and the Epcos TS400 barometric pressure sensor. A product like this would take in the aggregated data from a healthcare appcessory and create a context-rich dashboard for the consumer or doctor.
Source: Texas Instruments
This is how easily MEMS is changing people’s lives for the better. On a larger scale, MEMS has the potential to change entire societies. Through fitness and health apps, the industry can aggregate everyone’s personal health stats and translate those into actionable items for communities. Imagine a community where everyone is wearing a Fitbit and the entire town knows who has the best walking rates. What if neighborhoods had their own fitness competitions? Towns can take these stats and compare them with national rates of obesity or illness and decide to take more relevant action like creating more walking and biking paths. In this manner local communities could choose to divert funds to the areas where the data tells them they are falling behind the curve.
When we talk about Big Data, this is what we’re talking about, and none of it is possible without the context MEMS provides. To find out just how large this market is going to be, read Semico’s recently completed Mobile Healthcare Study (hint: over 270 million units by 2017).
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