Coupling & Crosstalk: Measuring Up

bathroom scaleCoupling & Crosstalk is my column in the MEPTEC Report. This column appears in the Spring 2013 edition on page 14-15.


Measuring Up

Tap to turn on. Wait for it to zero. Step on. I haven’t lost any weight, still 205 pounds even with all this exercise and careful eating? Step off, step back on. 212 pounds. Damn, wrong answer. Step off, step back on. 206 pounds. Okay maybe the first reading was right. Optimistically record 205 pounds. Does this nightly dance sound familiar? Not only are bathroom scales the bearer of bad news, their erratic behavior may make them one of the most despised home appliances.

I cannot say that the conversion of bathroom scales from purely mechanical systems to digital electronics has increased their accuracy. The precision of the data has increased moving from coarse analog dials to digital displays but scales do not appear to have improved accuracy or repeatability. Even though my scale displays weight to the nearest 0.1 pound (precision), the specified accuracy is only +/- 0.2 pounds. Many people, engineers included, often confuse the precision for the accuracy. (See my blog or this Wikipedia entry for a refresher on the difference between accuracy and precision.)

I haven’t done an analysis of variance (ANOVA) gauge repeatability and reproducibility (often shortened to “ANOVA gauge R&R” or simply “gauge R&R”) study of my bathroom scales and measurement techniques, but I just know the R&R is awful. Perhaps this may be a good elementary school science project for my children? In any case, it certainly is not user error… As a statistical process control (SPC) chart “junkie”, I plot each of my measurements by hand in real-time. I’m all for deep statistical analysis of data, preferably in as close to real time as possible. There is often a significant delay between when the measurement is made and when the statistics are run. By manually charting key parameters at the time of measurement, the user gains a “feel” for the data and insight into the stability of the process and measurement challenges. Beyond general optimism, I can pick the most likely “accurate” value for my weight.

The typical digital bathroom scale is based upon load cell technology where the resistance of a strain gauge changes due to the applied load. Four load cells are often connected in a Wheatstone bridge configuration whose resistance is then measured. From that resistance the strain can be calculated knowing the geometry of the strain gauge. This is certainly not terribly complex technology when compared to modern microelectromechanical systems (MEMS) based sensors. However, there are plenty of challenges in designing and producing a digital bathroom scale especially when considering the low average selling price (ASP).

Most MEMS based sensors measure fundamental forces – acceleration, rotation, and pressure – using miniature structures that move slightly. This movement results in a minute change to either capacitance or resistance that can be measured with high sensitivity electronics and used to calculate the movement. These sensors in turn provide measurements to calculate meaningful information about objects such as: How fast is an automobile moving or turning? Are the tires inflated properly? Sensor fusion adds a layer of computational intelligence to combine the data from multiple sensors in order to increase accuracy, eliminate spurious measurements, and provide greater insights into what has just happened. With my bathroom scale, I provide the “intelligence” to eliminate bad data.

For inanimate objects, MEMS sensing is fairly straightforward and accurate. But like measuring a person’s weight, measuring and providing meaningful information about people is significantly more complex. Did the wristband sensor actually measure several steps or was the user waving their arms? These measurement challenges may be why some technologists differentiate types of sensors as off-body, on-body (wearable), and in-body (implantable or digestible).

Most successful MEMS sensors to-date are off-body applications typified by automotive and smartphone applications. Even though a user may wear a smartphone, the data collected is more about the motion of the smartphone than the wearer. Not only is obtaining meaningful data easier in off-body applications, the devices may not need biocompatibility testing or medical regulatory approval.

With few exceptions, many of the mass marketed MEMS based systems today have coarse accuracy sufficient only for sensing large changes. Coarse accuracy is sufficient for idiot lights (such as low tire pressure), toys, and gadgets. I’ve noticed that my global position system (GPS) watch and sports measurement application on my smartphone (using sensor fusion of GPS and MEMS sensors combined with map data) are always slightly “off” in terms of distance for my bicycle rides. And neither measures exactly the same as my wheel based odometer.

The distance difference on these devices is minor compared to the ~2x difference in vertical climbing and ~3x difference between calculated calories. I could probably design a gauge R&R study and calibration method between the devices for distances, possibly for vertical climbing, but what about the calorie difference? As much as I am interested in improving my physical performance, perhaps I am better off enjoying my bike ride and the half-gallon of ice cream that the high calorie expenditure data permits. As the demand for self-awareness and quantification devices such as activity monitors and calorie counters grows, a greater number of enthusiasts are likely to push for increased accuracy. As MEMS sensor technology improves, market-leading product companies will find it easier to supply high accuracy and repeatable devices at reasonable costs. I look forward to the day when all of my devices have a much higher degree of correlation to each other.

As applications move to on-body or in-body their sophistication, accuracy, repeatability, and reliability need to increase significantly. This will permit many of the devices that are currently closer to toys and gadgets to become better diagnostic tools. The desire for self-administered medical diagnostics, often envisioned using a smartphone as the computing and connectivity engine, comes with significant system performance challenges. These devices may start out as “idiot lights” for our body – i.e. time to see the doctor for “check engine” – but greater specificity to provide “medical grade” measurements will be demanded over time. System accuracy and repeatability will be essential to detect acute symptoms and prevent false positives.

Once medical or mission critical reliability is proven for more than a handful of devices, MEMS will quickly move from on-body to in-body applications. At the same time MEMS has the opportunity to move from measurement to interaction. The unique size of MEMS may enable multiple measurement points and/or new therapeutic methods. High volume MEMS fabrication processes and packaging technologies that lower costs will increase the adoption rate of home or individual centric point-of-care. This greater access to advanced automated healthcare in non-clinical settings should reduce out-of-control medical spending.

Properly measuring, analyzing, and adjusting human activity and medical state are clearly challenging tasks. As a MEPTEC committee member, I’m looking forward to the upcoming conference “MEMS-enabled eHealth Revolution” focusing on sensors, actuators, and architectures that enable advanced healthcare applications. One particular interesting area is how biological sensors and actuators may differ greatly from “traditional” MEMS due to unique requirements of these “wetware” devices.

If your curiosity includes how to make these devices work better than your bathroom scale, I look forward to seeing you at the conference!

Let us continue the discussion below. I welcome your comments!

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