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Better Building: Making An Accurate Wearable

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Identify:

Heart rate measurements are a very versatile and useful tool for both starting and veteran athletes. Among many other uses [1], it can be used as a general gauge of how hard you are working out at that moment, and it can help determine improvement as your max heart rate increases. But putting a hand to your chest and counting the beats per minute is an impractical way to measure heart rate. So to help us measure this useful metric, we have heart rate monitors that can take our heart rate for us.

On the market, there are two types of heart rate monitors: Chest-strap monitors and wrist-strap monitors. Most people who aren’t even athletes probably have a wearable that can monitor heartbeats. Even most modern phones have the ability to measure your heart beats. However, despite their ubiquity, they have one major downfall: Wrist-strap monitors simply aren’t incredibly accurate. Besides the point of location, the wrist has several different factors [2] that make it difficult to get a good reading, ranging from skin-tone to motion. All of these factors produce noise that helps to muddy the results and can produce odd and inaccurate readings. To ensure we get the most accurate results to accurately determine our exercise maximum, it is this noise that we must turn to curb. But how do we go about reducing noise?

Formulate:

Modern wearable HRMs use a technology called photoplethysmography or PPG. In essence [3], the device shines a light through a bulb (usually an LED), that passes through the skin and muscles. A majority of this light will be absorbed by the surrounding skin and muscles. The blood absorbs the light that does make it through, which is the crux. By measuring the amount of light that is absorbed, the device can pick up on pulses in the blood based upon the differences in these absorbances. This technology is very similar to how spectrophotometers work, with both measuring the absorbance that a material has. But unlike a spectrophotometer which measures the absorbance through the material, PPG measures constantly, listening in for very small changes in absorbance that can be used to determine heart rate.

An example of PPG. Pulses within the vessel cause even more light to be absorbed than would normally. This is what the wearable is measuring.

PPG is very sensitive to physical sensations like bumps and pumps, which produces noise. When the sensor is jostled due to motion, the small amount of signal that is recorded gets muddled, which produces the majority of the noise that we are trying to cut down. This noise can cause very odd results, such as heart rates that vary wildly [4]. The noise is not present while not in motion, but for athletes who want to know how high their heart rate can get, this is not acceptable. As noise caused by motion is the leading cause of these very odd discrepancies in heart rate, we need to lower it as much as possible.

In order to solve this, we are going to separate the signals read between three metrics: a DC, an AC, and a noise component. We shall be ignoring noise caused by the optical signal, as it is subtracted by the sensor through the use of ambient light measurements [5]. Thus, the incoming current that is read can be simplified as:

Incoming Current = DC component + AC component + Noise

The DC component of the signal comes from changes in respiration in the vessel, while the AC component comes from variation in blood volume due to the heartbeat.

Solve:

If we want to reduce the amount of noise, we are going to have to pass the incoming signal through a filter. When the signal comes back to the wearable, it must interpret the signal, from raw absorbance values to electrical signals that are then interpreted by the wearable. And since we assume that optical light noise is negligible, we can focus on removing noise due to motion. Using biophysical-signal characterization techniques [6] as our filter, we can compare the incoming signal, and remove noise caused by motion. The remaining non-noise components will have to be amplified in order to get a complete, accurate signal. We do this in order to fill in the gaps made when we remove several points of noisy data.

By using a current filter, we hope to minimize the amount of noise that is detected alongside the heart rate of the monitor. If we can eliminate the noise read alongside the signal, we can get more accurate results, as the signal interpreted would consist only of the AC and DC components. However, this approach focuses only on noise made by motion. As stated before, there are several different sources of noise that interfere with accurate measurements. While motion is one of the primary sources of noise, other factors like skin tone, gaps between the sensor and the skin, and the location of the sensor can introduce more noise. This solution also has the possibility of cutting off accurate readings that are interpreted by the sensor as noise. Athletes who do a lot of vigorous exercises may find that their heart rates are inaccurate under this solution if their heart rates spike hard enough during exercise.

[1]: Shmerling R. How’s your heart rate and why it matters? – Harvard Health. August. https://www.health.harvard.edu/heart-health/hows-your-heart-rate-and-why-it-matters. Published 2017.

[2]: LeBeouf S. Five Challenges of Optical Heart Rate Monitoring. Sensors online. https://www.sensorsmag.com/components/five-challenges-optical-heart-rate-monitoring. Published 2016.

[3]: Cheriyedath S. Photoplethysmography (PPG). News Medical Life Sciences. https://www.news-medical.net/health/Photoplethysmography-(PPG).aspx. Published 2016.

[4]: Oniani, Salome & Woolley, Sandra & Pires, Ivan & Garcia, Nuno & Collins, Tim & Ledger, Sean & Pandyan, Anand. (2018). Reliability Assessment of New and Updated Consumer-Grade Activity and Heart Rate Monitors. 10.13140/RG.2.2.35628.72328.

[5]: Wearables | OSRAM. https://www.osram.com/os/applications/mobile-competence/mobile_competence_wearables.jsp.

[6]: Maity S, He M, Nath M, Das D, Chatterjee B, Sen S. BioPhysical Modeling, Characterization and Optimization of Electro-Quasistatic Human Body Communication. IEEE Transactions on Biomedical Engineering. http://arxiv.org/abs/1805.05200. Published May 14, 2018.

7 Minutes of Sweating: Taking the HIIT

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One of the most commonly stated New Year’s Resolutions is losing some extra pounds, starting a jogging routine, or otherwise getting physically fit. It is a very noble goal with good intentions, but many people who make this resolution for the new year end up breaking this promise to themselves. Chief among the reasons for quitting is the lack of time during the day. Between work, school, family, friends, and other obligations, it can be hard to set aside one or two hours to do a full traditional workout routine. But what if there were a way to get some exercise in regardless? What if you could work out for as little as 7 minutes, and get the same results as if you worked the full hour? As long as you follow a specific type of workout, this dream could be a reality. Enter: High-intensity interval training.

Figure 1: A Warrior Training instructor, leading her class.

By Any Other Name:

High-intensity interval training (HIIT) takes many different forms and names: Tabata Training, Sprint Interval Training, 7 Minute Workouts, and Warrior Training are all different forms of HIIT. The core idea behind HIIT is that athletes who take part of these programs work at maximum exertion for a short period of time before taking a short break. According to the guidelines put forth in the ACSM’s Health & Fitness Journal [2], working at a maximal output for short bursts of time causes you to generate close to 90% of your VO2 Max (also known as a VO2 Peak) over the course of the exercise. These guidelines, which have formed the basis of the 7 Minute Workout, pen the program as a time-efficient way to get similar efforts to other workouts that last longer, by trading time for increased effort. But how true is this claim? Can you really get the same workout you would in an hour in the span of 7 minutes, simply by working at maximal output? This is the question, one that I hope to answer.

Insulin Activity:

One study [3] focused on the potential for Sprint Interval Training (SIT) to be used for promoting insulin sensitivity, as well as other indicators for increased cardiometabolic health. In this study, they took 27 sedentary men with similar age, weight, and VO2 Peak. These men were divided into three groups and were given different workout routines: one was SIT, one was a traditional moderate-intensity continuous training (MICT), and the last did not train at all as a control. The SIT group would do high intensity, 10-minute sessions, while the MICT group would do more moderate bouts over 50 minutes sessions. Over the span of 12 weeks, these men worked out and got measurements of their results. They concluded that SIT was comparable to MICT, with regards to improving VO2 Peak, insulin sensitivity, and skeletal muscle adaptations. The study does not, however, make any mention of weight loss, though that is due to it being outside of their scope.

Figure 2: VO2 Peaks of the different groups in the study before, in the middle of, and after the full study.

New Year’s Resolution Buster:

In regards to body fat, one review [4], though dismayed at the effort required to adhere to the HIIT program, found it to be a preferable alternative to MICT. In that review, not only did they find many of the same adaptations from the study above, they found that many studies point out increased levels of skeletal muscle fat oxidation using HIIT. Another study [5] found that HIIT, while better than sedentary activity, was not significantly better than MICT, and may even be slightly worse. Still another study review [6] suggests that HIIT and MICT have similar benefits, with the only difference between the two being time.

One term that gets thrown out a lot during discussions is EPOC or Excess Post-Exercise Oxygen Consumption. Long name aside, it is a process that your body undergoes after exercising in order to bring the body back to a normal state. In this recovery state, the body uses more energy and calories compared to your resting rate as it tries to help heal and build your muscles. This gets thrown around especially in regards to HIIT, as some review articles [6] report that HIIT can lead to increased levels of EPOC, compared to MICT. This sounds like a decisive point in HIIT’s favor, as calorie burn is often the biggest signifier of hard work for starting athletes. But the actual amount of calories that are burned as a result of EPOC, according to this review [7] might not be very substantial in the first place.

The Bottom Line:

Armed with all of this information, what can we say about HIIT? As a form of exercise, it seems to be a perfectly valid way of working out. Whether it is better or worse than traditional duration exercises is up for debate, but HIIT is at least around as good as MICT. Both MICT and HIIT cause similar increases in VO2 Max and other adaptations such as increased insulin sensitivity. Neither method has been shown to be significantly better at burning calories, either.

That being said, one common theme appears across several studies is how harsh the workout is. In almost every single review study involving HIIT, the discussion often concedes that HIIT is very intense and that not everyone will be able to maintain the level of exertion requested by HIIT. With this, I can say that HIIT will not be replacing MICT. Ultimately, the question of whether to do MICT or HIIT comes down to personal preference. If you don’t have time during your day and are willing to really sweat it out for a short amount of time, then HIIT is a good alternative choice. If you do have time during the day and don’t want to work out to near your maximal output, then stick with a more traditional workout may be the right thing for you.

Questions To Consider:
  • Given a choice between working out using the HIIT method or the traditional MICT method, which would you choose?
  • Would you recommend HIIT to a beginner athlete?
  • What about someone more grounded in their routine? Would you ask them to give it a shot?
  • Looking through a calories-down lens, would you focus on HIIT?
  • If you are working out using the traditional MICT method, would you integrate some HIIT workouts in there as well?
  • Given the short time investment, would you work out using HIIT 7 days a week?

[1]: Figure 1: Warrior Trained Fitness offers service members, families’ group workout. https://www.nellis.af.mil/News/Article/665186/warrior-trained-fitness-offers-service-members-families-group-workout/.

[2]: Klika B, Jordan C. HIGH-INTENSITY CIRCUIT TRAINING USING BODY WEIGHT. ACSMs Health Fit J. 2015;17(3):8-13. doi:10.1249/fit.0b013e31828cb1e8

[3]: Skelly LE, Martin BJ, Gibala MJ, Gillen JB, MacInnis MJ, Tarnopolsky MA. Twelve Weeks of Sprint Interval Training Improves Indices of Cardiometabolic Health Similar to Traditional Endurance Training despite a Five-Fold Lower Exercise Volume and Time Commitment. Sandbakk Ø, ed. PLoS One. 2016;11(4):e0154075. doi:10.1371/journal.pone.0154075

[4]: Boutcher SH. High-intensity intermittent exercise and fat loss. J Obes. 2011;2011:868305. doi:10.1155/2011/868305

[5]: Keating SE, Johnson NA, Mielke GI, Coombes JS. A systematic review and meta-analysis of interval training versus moderate-intensity continuous training on body adiposity. Obes Rev. 2017;18(8):943-964. doi:10.1111/obr.12536

[6]: Børsheim E, Bahr R. Effect of Exercise Intensity, Duration and Mode on Post-Exercise Oxygen Consumption. Sport Med. 2003;33(14):1037-1060. doi:10.2165/00007256-200333140-00002

[7]: Laforgia J, Withers RT, Gore CJ. Effects of exercise intensity and duration on the excess post-exercise oxygen consumption. J Sports Sci. 2006;24(12):1247-1264. doi:10.1080/02640410600552064

Patent Blog Post: Fitbit’s Wearable Heart Rate Monitor

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Perhaps you’ve been barraged by emails from Fitbit that try and get you to buy one of their products during one of their many sales. Perhaps you’re a trendy techie and have a wearable in the form of a Galaxy or Apple Watch. Or perhaps you’re simply the owner of a smartphone made within the past few years. All these technologies have heart rate monitoring built into them from the get-go, and it is increasingly hard to get away from gadgets that don’t have some form of heart monitoring. With how ubiquitous the technology has gotten, I would like to look today at one of the patents put forward by Fitbit, one of the more popular brands when it comes to wearable fitness trackers. For this post, I’ll be using the information put forward by Google Patents, seen here.

One of the many figures in the patent, detailing the backside of the wearable.

The patent is simply titled as, “Wearable heart rate monitor,” and has a patent number of US8945017B2. It was originally filed on June 3rd, 2014, and was then approved on February 3rd, 2015. This makes the time to issue a little under a year, which is quite fast for an electronics product. The two inventors credited in the patent are Subramaniam Venkatraman and Shelten Gee Jao Yuen. Looking at the other patents associated with them, Venkatraman seems to have worked on more navigational devices, while Jao Yuen has worked on several other gyroscope-related projects. The assignee is, of course, Fitbit Inc. themselves. Officially, one of the classifications of the patent is, “signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal.” This one patent has 30 different claims to its name.

Of the 30 different claims in the patent, many of them tie into 2 main claims. The first is that the wearable heart monitor has a way to efficiently, accurately, and quickly determine the heart rate of the user. The second is to ensure that the wearable is capable of compiling the heart rate monitor’s data, including the heart rate data. This patent is aimed at both casual and advanced fitness enthusiasts, as the data gleaned from the wearable is handy to track. Runners, in particular, would find this tempting as it also mentions step tracking and other forms of movement.

The heart rate monitor works by using a waveform sensor, which reads signals at the surface of the skin. These signals are sent to the rest of the device, where the data is processed. The raw data from the sensor is rough and has a lot of noise from several factors, including movement and moisture. To remove the noise, the data has to be passed through several filters. From that data, a heart rate can be determined, and then presented to the user. Unlike the monitors of prior ages, this heart rate monitor would not rely upon disposable components, instead simply being able to be used multiple times by wearing it. In addition, the heart rate tracker would track more than just heart rate, including details about steps.

References:

Venkatraman, S., & Yuen, S. G. J. (2014). Wearable heart rate monitor. Retrieved from https://patents.google.com/patent/US8945017B2/en