One, Two Step

Wrist pedometers are used by many to count their steps, and notify users when they “reach their 10,000”. These wearable devices quantify step activity and give indiviudals an idea of exactly how much they are moving throughout the day.

Accelerometers are often used within these wearable devices to detect the force acting on the device. The force acting on the accelerometer is correlated to an analog voltage output, which must be processed through a series of op amps to turn a users movement into an electrical output that can be analyzed through signal processing, but what signal processing circuitry is needed following the accelerometer within a wrist pedometer to correlate force acting on the pedometer to steps taken by the user?

In this post we will solve at the following engineering problem associated with pedometers: what signal processing circuitry is needed to convert the analog voltage input from an accelerometer to a binary digital signal that can be correlated to steps taken by a user?

Background 

Figure 1. Force acting on wrist pedometer during gait cycle[1].

The average person takes between 0 and 120 steps per a minute. Throughout each gait cycle, a wrist pedometer experiences forces relative to its position, as shown in Figure 1. While standing the force detected by the pedometer is 1G (one times the force of gravity). When a user is pushing against the ground to step forward the force detected by the pedometer can rise above 1G, and while the user is between steps the force detected by the pedometer can go below 1G. The pedometer can detect when a user takes a step by monitoring forces and determining when the 1G threshold is crossed. Many wrist pedometers use a threshold of at least +/- 0.2G to prevent noise and standing movements from being accounted for in step count. So a step count will be equal to crossing the 1.2G and -1.2G thresholds[1].

 

Figure 2. Voltage Output as a function of force for Analog Devices+/- 2g accelerometer [2]

Accelerometers are often used to relate the force acting on an object to an electrical signal. Analog Devices, a circuitry component manufacturer, produces an +/- 2g accelerometer that relates forces between -2g and +2g to a voltage output, as shown in Figure 2. A linear region exists between +/- 2g, which can be defined by the following simplified function V(g)=(.875*g)+2.5 [2].

Approach 

In designing the signal processing circuitry necessary to convert an analog signal from an accelerometer to a binary digital signal, we will do the following:

1.Define the input signal in terms of force acting on a wrist pedometer, and the voltage output of an accelerometer

2.Determine the signal processing necessary to convert the analog signal into binary digital output

3.Select circuit components to complete desired signal processing, and appropriate values for integrated components

4.Use LTSpice to model desired circuitry, and confirm that designed circuit solves the defined engineering problem

Signal Input

It is known that the force acting on a wrist pedometer can be defined by a sine wave function fluctuating +/ 0.5g around 1g, with a frequency of 0-2Hz. Therefore we will define the force acting on the pedometer as F(t)= 0.5sin(t) + 1g. Given the voltage output of analog devices accelerometer is V(g)=(.875*g)+2.5, the voltage output of the accelerometer can be defined as V(t)=0.4105sin(t)+3.375.

Figure 3. Force acting on pedometer throughout gait cycle

Figure 4. Voltage signal generated by accelerometer from force input signal

 

 

 

 

 

 

 

 

Signal Processing

To generate a digital binary signal from an analog voltage input signal processing through circuitry is required.

Figure 5. Flow chart of signal processing of input analog signal from accelerometer to binary output signal

First, the input signal should be passed through a low pass filter, with a cutoff frequency of 2Hz to remove high frequency noise from the signal. The force acting on the pedometer, and voltage output of an accelerometer can be defined by sine wave functions. The baseline of accelerometer voltage output exists above zero volts, therefore a subtractor should be used to bring the baseline of this signal to 0V. A full wave rectifier will be used as an AC to DC converter, converting both polarities of the signal to a pulsating DC signal. A compactor will be used to produce a binary DC output that indicates whether the signal is above a given threshold, voltage relative to passing +/- 0.2g force threshold. This binary signal is the system output and can be used to count total steps taken by a user.

Circuit Components 

Circuit components were selected to complete necessary signal processing, and assumed to be ideal for simplification of solving this problem.

Low pass filter 

Figure 6. Low pass filter

 

With a cutoff frequency of 2Hz, a low pass filter with a Capacitor of 4.7 nF and resistor of 0.0169 Ohms can be used to filter out high frequency noise

 

 

 

 

Follower

Figure 7. Op amp acting as follower

 

Follower used to preserve signal and prevent current flow back to the user

 

 

Subtractor 

Figure 8. Op amp acting as a subtractor

 

A subtractor can be used to reduce the baseline signal to zero. Given our voltage input is V(t)=0.4105sin(t)+3.375 V, and the Voltage output of this component is Vout = (R3/R1)*(Vin-Vs), we will set Vs=3.375V DC and R1=R2=R3=100 Ohm to bring the baseline signal down to 0V.

 

 

 

 

Full Wave Rectifier

Figure 9. Op amps acting as a full wave rectifier

 

A full wave rectifier will be used to convert all polarities of the input signal to the same polarities. If R1=R2=R3=R4=R5, then if Vin>0 Vout=Vin and Vin<0 Vout=-Vin. Therefore, we will set all the resistors equal to each other to achieve a rectified DC signal.

 

 

 

Comparator 

Figure 10. Op Amp acting as a comparator

A comparator will be used to convert the pulsating DC signal to a binary digital output. Op amps functioning as comparators follow the rule that if V+>V- Vout = Vc+ and V->V+ Vout=Vc-. In our ideal circuit, we aim our binary signal to be either 0 or 1.

V+ terminal will be our signal, and we look to determine if this signal represents crossing the 1.2g threshold. To find what the V- terminal should be we need to determine the voltage at this point in the circuit if it has crossed the threshold. Given, V(g)=(.875*g)+2.5, V(g=1.2)=3.52. The signal is brought through a subtractor where it is reduced by 3.375 and afterword is no longer amplified or modified, so the threshold voltage at this point is 0.1534 V. The negative input terminal will be set to be a DC voltage of 0.1534V.

To generate a binary output where 0V= not crossing the threshold and 1V= crossing threshold, the op amp terminals will be set to be Vc+ = 1V and Vc-=0V.

LTSpice Modeling and Verification 

Figure 11. LTSpice signal processing circuit following accelerometer to convert analog accelerometer signal input to binary output


LTSpice was used to model the designed circuit, as shown in figure 11. This circuit was simulated using LTSpice software and it’s ability to produce a binary digital output from an analog signal was verified as depicted in figure 12.

Figure 12. Voltage input, green, and output, blue, of signal processing circuit in figure 11

Solution

Our goal was to design signal processing circuitry is needed to convert the analog voltage input from an accelerometer to a binary digital signal that can be correlated to steps taken by a user.

Figure 13. Accelerometer analog output signaling processing circuit to produce binary digital output

A series of signal processing components were integrated within a circuit, depicted in figure 13, to convert an analog voltage signal from an accelerometer into a binary digital signal. This circuit removes high frequency signal noise, reduces the signal baseline, generates a pulsating DC signal, and generates a binary signal output, as shown in figure 14.

Figure 14. Binary digital output of accelerometer signal processing circuit

This binary digital output can be correlated to steps taken, as two square waves is equal to one step taken. These square waves can be counted by an integrated software and used to count user steps. Thus, turning the analog accelerometer voltage output into a binary digital signal.

This binary signal can be used to count steps and step frequency, and when integrated with GPS and other technologies can be used to determine step distance and user speed.

With one two sqaure waves equaling a step, the designed integrated circuit turns user movement into step count, enabling the signal processing necessary to count those 10,000 steps everyone is so desperately trying to reach!

References

[1] Modi, Yash Rohit. (2014). United States Patent No. US20140074431A1. Retrieved from https://patents.google.com/patent/US20140074431A1/en

[2] “Accelerometer Specifications – Quick Definitions.” Accelerometer Specifications – Quick Definitions | Analog Devices, www.analog.com/en/products/landing-pages/001/accelerometer-specifications-definitions.html.

Air Displacement Plethysmography: How It Works Patent Post

Body Fat is an important health statistic. Whether you are a person who dreams of obtaining “rock-hard” abs on the beach, a person aiming to shed a couple of pounds for the new year, a doctor assessing a patient’s risk of cardiac arrest, or just a general fitness enthusiast, body fat is the rave of today’s exercise culture. Although there is a negative connotation associated with body fat, it is an essential nutrient. Fats are needed to boost energy levels and numerous metabolic processes. Generally, a healthy individual is considered to have a body fat value in the range of 18-25%. However, excessive fat levels have shown a positive correlation with mortality.

Historically, body mass index (BMI) has been used more often by doctors to evaluate a person’s overall fitness. But a health study in the American Journal of Clinical Nutrition determined that an individual’s body fat is more effective in assessing his/her risk of developing chronic disease than BMI due to the failure of the latter in differentiating between fat-free mass (bone, water, lean tissue) and the weight of fat mass in the body. An individual may be on the lower end of the obesity spectrum in terms of total weight, but still possess an enormous risk of cardiovascular diseases due to having too much body fat.

Based on these facts, one could argue that healthcare professionals should deviate from the practice of collecting patient’s BMIs and focus their attention solely on calculating patients’ body fat percentage. However, measuring an individual’s  body fat is an arduous process due to the amount of time need to procure data and make calculations, which require a good understanding of topics such as calculus. and conversation of mass (nasty math/physics). For that reason, BMI  is more commonly used despite the lower confidence in this data. Thus, there is a high demand for technology that can assess an individual’s body fat percentage in an accurate and timely manner.

Air Displacement Plethysmography is an emerging technology that utilizes air perturbations that occur when a subject enters a confined space in order to determine their body fat levels. Please click here to view figures collected from a US patent filed for the BodPod: an air plethysmographic apparatus manufactured by Life Measurements Instruments, a medical device company based in Concord, California.

The Bod Pod consists of an air circulation system (represented by item 60 on figure 2) linked to a plethysmographic measurement chamber (pointed out by item  50 on figure 2). The air circulation system (embodied in greater detail by  Fig 3 of the patent), comprised of one or more pumps, acts as both a source of circulation and filtration within the chamber by using ambient air (air that is derived from a temperature-enclosed environment). Clean air is pumped into the chamber via an inlet tube (represented by item  86) while contaminated air is moved out of the chamber through an outlet tube (represented by item 88), where it is later filtered and recycled. The result is a clean and controlled air environment that is maintained throughout the duration of the BodPod’s operation. Inside of the Bod Pod are plethysmographic measurement components(represented by item 56 and 58 on Figure 2) that record perturbations in the volume of air inside the chamber before and after a subject enters in order to calculate the subject’s body volume by subtraction. For those who aren’t familiar, a plethysmograph is an instrument that measures displacements in a fluid within an enclosed environment (in this case, the BodPod chamber). In order to gather accurate data, it is imperative that the volume of air in the chamber is recorded before a subject enters the chamber. Once all data has been collected, it is wirelessly  transmitted to a computer for further analysis using software provided by Life Instruments. Once the subject’s body volume has been determined, it is immediately inserted into Siri’s Equation to calculate the subject’s body fat percentage.

 

References

Dempster Phillip, Michael Homer, and Mark Lowe (2004). United States Patent 20040193074 A1. Retrieved from https://patentimages.storage.googleapis.com/93/cf/ea/6d2d1346ea1129/US20040193074A1.pdf

 

Tired of Blood Tests? Don’t Sweat it! (Or do, I Suppose)

I think I stand with the majority of the population when I say that I do not like needles. Blood work, as I’m sure you can imagine, is not really my cup of tea. As I sit there in that dreadfully uninviting chair with a rubber tourniquet tied way-too-tightly-for-comfort around my upper arm, I find myself wishing there was a less invasive alternative, ideally one that doesn’t leave me with a puncture wound. Well, luckily for me and any other sane individual who shares my distaste for needles, the future looks bright. Sweat has historically been overlooked as a biosensing platform despite carrying many of the same biomarkers, chemicals, and solutes that are carried in blood. This stems from a variety of different complications associated with sweat sensing that simply don’t exist for blood tests, and these complications have been enough to prevent any significant progress in the field for a long time. It was not until a group of scientists from the University of Cincinnati were issued an intriguing patent on January 22nd of this year that the field of sweat sensing technology began to see hope.

Now all of that is a bit dramatic, but let’s be real. How many of you would have kept reading if I started off with “Devices for integrated, repeated, prolonged, and/or reliable sweat stimulation and biosensing” (the official name for the patent in question)? I’m sure sweat stimulation isn’t something you planned on spending a great deal of time thinking about today, but you’re here now and boy let me tell you, this is exciting stuff. US patent number 10182795 could have some major implications in the near future across multiple facets of life. Inventors Jason Heikenfeld and Zachary Cole, working out of the assignee of the patent, the University of Cincinnati, have put forth some novel approaches to addressing the complications associated with sweat biosensing, specifically the inability to consistently gather enough sweat from one area for testing, skin irritation, and the contamination of sweat samples from chemicals used to stimulate sweating, like pilocarpine. Their patent, filed on October 17, 2014 and classified under CPC A61B 5/1491 (using means for promoting sweat production), among other classifications, describes a medical technology that has potential for a wide-ranging impact, and the many claims they make (13 to be exact) sound promising.

So let’s talk about the invention. At its core, it’s a method of sweat stimulation and sampling through device-skin interface that could be deployed through a variety of different mediums, including patches, bands, straps, clothing, wearables, etc. Utilizing multiple sweat stimulation pads controlled by a timing circuit capable of selectively activating/deactivating individual pads, this technology is able to rotate through the pads over time. This prevents skin irritation caused by the single-pad, continuous sweat stimulation that has been used in this design’s predecessors, and also allows more consistent sweat collection due to the rotation between fresh sweat stores. One commonality between this design and previous sweat sensors is the method of sweat stimulation, but even within that apparent commonality there are improvements that have been made. While this design still plans to use a molecular method of drawing sweat out of the skin, like pilocarpine, it also includes a filtration component, or membrane, to ensure purity of the sweat sample collected. Once collected, the sample is pumped to a sensor by a microfluidic component to analyze the concentration of the analyte of interest for that sensor. The design can be seen in better detail below.

The inventors claim that this device will collect and analyze sweat over extended periods of time through the components and methods described above. If that is in fact true, this technology could soon be in use everywhere. From athletes seeking to maximize their body’s performance to nurses caring for neonates to patients undergoing pharmacological monitoring, this technology could be a real breakthrough in systemic biomarker detection. Why bother with a needle when you can get the same information by slapping a patch on your arm? This patent has the potential to render most blood tests for analytes present in sweat obsolete. There is likely still a long road ahead before it reaches the market, and it may very well end up like the vast majority of US patents that never make it there. But I know I’ll be on the lookout for clinical trials over the next 5-10 years. I hope you will too.

Reference

Heikenfeld, Jason C., Sonner, Zachary Cole. (2019). United States Patent No. 10182795B2. Retrieved from http://pdfpiw.uspto.gov/.piw?PageNum=0&docid=10182795&IDKey=263AB8D65766%0D%0A&HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2Fnph-Parser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526p%3D1%2526u%3D%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r%3D3%2526f%3DG%2526l%3D50%2526co1%3DAND%2526d%3DPTXT%2526s1%3Deccrine%2526s2%3Dsensor%2526OS%3Deccrine%252BAND%252Bsensor%2526RS%3Deccrine%252BAND%252Bsensor

Just Trying To Reach 10,000 Or Competing To Step Above The Rest – How Do Wrist Pedometers Count Our Steps?

People everywhere are getting their steps in. Whether they’re attempting to reach 10,000 steps a day or participating in competitions with friends, family, or coworkers to see who can step the most, people are moving – and they want to know exactly how much. Wrist fitness trackers with built in pedometers have become a popular mode for individuals to track their daily activity, but how do these devices work?

Let’s look at Apple Incorporated’s Wrist Pedometer Step Detection technology. This technology uses motion data to determine a force comparison threshold that can be used to accurately count steps while a user is running and walking.

An illustration of a person using a wrist pedometer for step detection, included in United States Patent No. US20140074431A1

 

Patent title: Wrist Pedometer Step Detection

Patent number: US20140074431A1

Patent filing date: 2012-09-10

Patent issue date: 2014-03-13

Inventor: Yash Rohit Modi

Assignee: Apple Inc

U.S. classification: G01C22/006 Pedometers

How many claims: 18

Forces acting on a wrist pedometer can be associated with user movement, specifically when they’re walking or running. The force of gravity as well as the forces exerted by the user against the force of gravity are measured by the pedometer; changes in forces acting on the device can be used to determine step count as well as type of exercise. While standing the force detected by the pedometer is 1G (one times the force of gravity). When a user is pushing against the ground to step forward the force detected by the pedometer can rise above 1G, and while the user is between steps the force detected by the pedometer can go below 1G. The pedometer can detect when a user takes a step by monitoring forces and determining when the 1G threshold is crossed.

Forces are compared based off magnitude and frequency to accurately count user steps. Other pedometer technologies worn at the trunk have used a 0.2G comparison threshold to account for steps, meaning when the pedometer experiences a for change of at least 0.2G one step will be added onto the step count. This threshold has been set to prevent noise and standing movements from being accounted for in step count.  However, the force differential experienced by wrist pedometers change with alternating steps. With the step on the side opposing the pedometer, the force acting on the pedometer is often less than 0.2G and may not be detected by the device with this threshold in place. To overcome this issue, this devices step algorithm has included frequency of threshold crossing to account for opposing steps. If the comparison threshold has been crossed twice over a set step time, then the technology will account for two steps rather than one. This prevents the technology from missing steps – thus, increasing device accuracy.

Motion data is also utilized in this technology to account for user activity and adjust parameters appropriately count steps . Fast Fourier Transform (FFT) is used to determine dominant frequency of motion and determine user activity. If the dominant frequency is below run threshold, then steps are counted for within walking parameters, described above. If the dominant frequency is above run threshold, then steps are counted for within running parameters. While running, there is a reduction in change of force acting on the pedometer; the change of parameters takes this into account and utilizes this information to properly account for steps.

Unlike other step counting technologies on the market, this product has improved accuracy in step count. The step counting algorithm has parameters that better define noise and non-walking movement as well as a mode to account for the imbalance in force acting on the wrist pedometer during walking. Less steps are unaccounted for and less random movements are counted – making for more accurate step counts.

There are a number of pedometer technologies that exist on the market today. Regardless of brand and step counting algorithm – these technologies are giving indiviudals the ability to count their steps and measure their fitness levels, promoting an active lifestyle for those who utilize them.

Reference

Modi, Yash Rohit. (2014). United States Patent No. US20140074431A1. Retrieved from https://patents.google.com/patent/US20140074431A1/en

How it Works: Air Displacement Plethysmography

How it Works: Pedometers

How It Works: Heart Rate Monitors

By: Margot Farnham, Juliana Gullotta & Nicholas Ruggiero

Suggested Reading:

https://www.topendsports.com/fitness/equip-monitors.htm

https://www.cnet.com/news/how-accurate-are-wristband-heart-rate-monitors/

https://www.news-medical.net/health/What-is-Heart-Rate.aspx

https://www.mayoclinic.org/healthy-lifestyle/fitness/expert-answers/heart-rate/faq-20057979

https://www.brianmac.co.uk/hrm4.htm

https://www.getqardio.com/healthy-heart-blog/how-does-ecgekg-work-and-what-is-an-ecgekg-monitor/

https://blog.ouraring.com/blog/heart-rate-variability-basics/

How it Works: BIA and EIM