![]() About 20 to 40 percent of the steps we take each day occur in bouts of walking that are 20 seconds or less, which contributes significantly to the total energy expended every day. These latter indicators take up to a minute to reflect changes in activity, so if you got up from the couch and walked 10 steps they wouldn't detect that movement. ![]() This kind of direct analysis provides a much better way of tracking instantaneous energy expenditure over other measures such as heart rate or respiratory rate. Then we pass the leg motion from that step into our model that estimates energy expenditure. We determine when a step occurs by detecting when the leg stops rotating in one direction and starts rotating in the other direction, occurring around the time the heel touches the ground. (Our Python scripts are available for download from a public repository.) We pull in sensor data at a fixed rate of 100 hertz. We trained a linear regression model to perform gait detection to segment each step and make an estimate of the calories burned. We used the NumPy scientific computing library for storing data in a convenient format, and the Scikit-learn machine-learning library for analyzing motion data from the IMUs. The Pi, running a stock version of the standard operating system, also allowed us to use established Python libraries to do onboard data processing. Although the Pi is bigger and has a higher power draw than, say, a Teensy board, we chose it because it was easy to stream data wirelessly from the Pi and monitor it during testing and calibration.Ībout 20 to 40 percent of the steps we take each day occur in bouts of walking that are 20 seconds or less. The IMUs are connected to a Raspberry Pi using the I2C protocol. In our tests, we sometimes used toupee adhesive to hold various IMUs in place, but a Velcro strap works great, too! The counter uses two of them, one attached at midthigh, the other at midshank. This board combines two sensor chips, a six-degrees-of-freedom accelerometer/magnetometer, and a three-degrees-of-freedom gyroscope. ![]() We used Adafruit Precision NXP 9-DOF breakout board IMUs. Using a I2C expansion board, the IMUs feed data into a Raspberry Pi powered by a USB battery pack. Two small inertial measurement units are strapped to the user's thigh and shank. (For the full details of our analysis, see our recent paper in Nature Communications.) What's more, we could do it using inexpensive IMUs. We found that by looking at the motion of the thigh and the shank (lower leg) we could estimate caloric expenditure during aerobic activities with an accuracy of about 13 percent. With all of this data we looked at building a fundamental relationship between the movement of the body (and thus the activity of the muscles burning calories) and the actual energy expended by the whole body. We used respirometry, a lab-based method of measuring energy expenditure by monitoring the oxygen intake and carbon dioxide expelled with each breath, so as to get a ground-truth measure of the calories burned as participants moved. This included sensors that monitor muscle activity, inertial measurement units (IMUs) to monitor movement on different parts of the body, and instrumented insoles in shoes to monitor forces produced by walking and running. We broke out every possible sensor we had in the lab and attached them to participants. The road to the calorie counter began in our lab (the Human Performance Laboratory in the Stanford University School of Engineering), where we study things like the metabolic cost of walking. The answer is yes-and it's one you can build yourself with parts any maker can easily obtain. We decided to see if a better calorie counter could be made, at least for some forms of activity. The problem is that smartphones and smartwatches do a terrible job at calorie counting.Ī 2017 study looked at seven such typical devices, and found their counts were off between 27 and 93 percent, depending on the device. For such people there are a wealth of fitness and diet apps that rely on smartphone and smartwatch sensors to monitor activity levels and track the calories they have burned. Even without the lockdowns, many parts of the world have been facing an obesity epidemic, which has created a need to help people manage their weight. Physical activity is essential to both physical and mental health, something brought home to many people following sedentary pandemic lockdowns.
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