The Near-Electromagnetic Spectrum Is Just a Name

Humans require oxygen for everyday life. It is a key component in many body functions so it logically follows that measuring oxygen and oxygen usage in the body can be extremely beneficial. Healthcare and sports are two main fields that come to mind. On the market today, there are hundreds of devices that measure various  body metrics and oxygen levels and saturation are no exception. One device that measures the the oxygenation saturation in body tissues is a portable near-infrared spectroscopy (NIRS) apparatus created by Gutwein et al.[1]. Gutwein et al. applied for a patent titled “Method and Apparatus for Assessing Tissue Oxygenation Saturation” on March 22, 2017 and the patent  was approved and filed roughly 5 months later on September 28, 2017. Gutwein et al. also had a previous patent “Portable Near-Infrared Spectroscopy Apparatus” filed in October 2016. The basic patent information is outline below[1]:

  1. Patent title: Method and Apparatus for Assessing Tissue Oxygenation Saturation
  2. Patent number: US 2017/0273609 A1
  3. Patent filing date: March 22, 2017
  4. Patent issue date: September 28, 2017
  5. Inventors: Luke G. Gutwein, Clinton D. Bahler, Anthony S. Kaleth
  6. Assignee: Indiana University Research and Technology Corporation
  7. U.S. Classification: CPC – A61B: 5/14552, 5/6807, 5/02055
  8. Claims: 20

Invention & Claims

This invention is an apparatus and method developed for assessing tissue oxygenation saturation during physical activity. The portable near-infrared spectroscopy (NIRS) apparatus comprises of a wearable article of clothing, namely a shirt, pair of shorts or a calf sleeve. It also contains a spectroscopy probe, a near-infrared light source and a photodetector coupled to the article of clothing used. The probe is configured to measure oxygenation saturation in skin dermis, adipose tissue (fat) or muscle fascial layer[1].

Figure 1. Image depicting user wearing device. The wireless probe can be incorporated into different wearable articles.

Why Is This Important?

There is a need for such a device in a multiple fields, namely healthcare and sports. This NIRS apparatus has applications in clinical settings, sports industry and in exercise physiology research. Clinicians, doctors and researchers can benefit from a portable NIRS device in monitoring and diagnosing disease states, including:

  • septic shock
  • real-time tissue perfusion analysis during surgery
  • peripheral arterial disease

The NIRS device is highly sensitive to changes in Muscle tissue oxygenation (StO2) and during exercise, the NIRS signal is considered to reflect  the balance between oxygen delivery and utilization. This can have multiple applications in sports including looking at how efficient oxygen flow is and to show what muscles are specifically being activated and which aren’t; this can indicate whether or not proper form is being used[1].

Tech Talk: How It Works

NIRS is an optical technique that allows for continuous non-invasive monitoring. This technique is founded on the Beer-Lambert Law. The Beer- Lambert Law is a linear relationship between absorbance and concentration of an absorbing species[2].

Where A is the measured absorbance, a(𝜆) is the wavelength-dependent absorptivity coefficient, b is the path length, and c is the analyte concentration.

The NIRS device measures hemoglobin oxygen saturation in microvessels (arterioles, capillaries, venules) by applying this Beer-Lambert Law and the using the differences in light absorption coefficients of oxyhemoglobin and deoxyhemoglobin. The device uses these absorption characteristics to calculate changes in oxygenated & deoxygenated hemoglobin in skin, fat, or muscle tissues[1].

What Sets It Apart?

Early devices that measured oxygenation saturation were mostly limited to research usage. Since then there have been advances in the field leading to the creation of smaller probes and utilizing wireless probes instead. The PortaMon created by Artinis Medical Systems (B.V. Netherlands) is currently the most common portable NIRS device on the market. There are still problems with these devices however. Namely issues with:

  • Device size
  • Motion signal artifacts
  • Adipose (fat) tissue thickness

The size of the probe is based off the required penetration depth. In order to get the 2-6 cm of depth required, the probes tend to be bulkier. Another issue is motion signal artifacts that raise questions concerning the reliability of some devices. These artifacts are created by instabilities (for example, lose of contact) at the skin-probe interface. Finally, the thickness of the adipose tissue can limit the depth of penetration achieved[1].

The main advantage of this device is that it can measure levels in skin, fat or muscle tissue over any sport or activity including running, bicycling, swimming and weightlifting. Some additional features are that this NIRS device and method can measure the user’s heart rate, respiratory rate and body temperature[1].

Citations:

[1] Gutwein et al. (2017). Method and Apparatus for Assessing Tissue Oxygenation Saturation. US 2017/0273609 A1. U.S. Patent and Trademark Office

[2] Beer-Lambert Law. (n.d.). Retrieved March 10, 2020, from http://life.nthu.edu.tw/~labcjw/BioPhyChem/Spectroscopy/beerslaw.htm

It’s Bioelectric! Boogie, Woogie, Woogie!

The Patent 

  • Patent title: Bioelectrical impedance measuring apparatus constructed by one-chip integrated circuit
  • Patent #: 6472888
  • Patent filing date: Jan. 29, 2001
  • Patent issue date: Oct. 29, 2002
  • How long it took for this patent to issue: 1yr and 9mo
  • Inventor(s): Oguma, Koji, Miyoshi, Tsutomu
  • Assignee: Tanita Corporation
  • US classification: 324/691; 324/692; 600/547
  • How many claims: 9

The Invention

Bioelectrical impedance apparatuses (BIA) estimate the body composition of the individual by sending a small electrical impulse through its tissues. The speed of this electrical current varies due to the water, muscle, and fat content in the tissues [1]. Tissues containing higher water and muscle content tend to have faster electrical currents, and thus, a lower impedance; whereas, tissues containing higher fat content tend to have slower electrical currents, and thus, a higher impedance [1]. Other factors, such as sex, height, and weight, are also considered when using this device. [2]

Having been available to the public since the 1980’s [1], this recent 2001 patent improves upon the original BIA by scaling down all the necessary processes onto a one-chip microcomputer [2]. The essential components of a BIA include: inputting patient information, applying electrical current, integrating bioelectrical impedance, and outputting one’s body composition, all of which remain withstanding [2]. The one-chip microcomputer is useful for 1) selectively generating and relaying an alternating-current (AC), 2) containing multiple switches that measure bioelectrical impedance, send the AC signal, and quantify the output voltage, and 3) containing devices that produce, supply, and detect voltage [2]. Overall, the inventors sought to maintain the effectiveness of the current solution whilst minimizing the amount of compartments needed to output one’s body composition.

Figure 1. Bioelectrical Impedance Analysis Schematic. The body is composed of fat or fat-free mass and water or water-free tissues (A) [8]. These different components result in different resistances, and thus, different impedances. Moving through water encounters less resistance than moving through fat (C) [8]. To measure impedance, the circuit connects to 2 electrodes placed at the wrist and 2 placed at the ankle (B) [8]. 

The Population

BIA can prove to be an effective tool for quantifying one’s body composition in clinical studies, more specifically studies whose population consists of individuals from developing countries [3] due to its low cost, portability, ease of use, and reliability [4]. Additionally, bioelectrical impedance analyzers are available for commercial use and offered from online retailers, such as Amazon [5]. Like an Apple Watch or Fitbit, one may resort to purchasing a BIA in means of receiving information regarding their body constitution in a timely fashion. Using an age limit similar to a Fitbit, individuals below the age of 13 should not make use of a BIA [6]. Moreover, the curiosity over one’s weight, body mass index, and body composition has been trending over the last decade, and is directly related to the booming health and fitness industry [7]. Aside from its current uses, BIA deems to be a probable technique for detecting the presence of abnormalities in the body (i.e. lesions and/or tumors) [2].

The Engineering

As previously stated, BIA relies on an electrical current that passes through various regions of high to low water, muscle, or fat content in order to decipher one’s body composition. This technique assumes the body exists as conductive cylinders, uniform in material and density, and fixed in cross-sectional area [3].In addition, BIA assumes that the body’s conductive volume is reflective of its water composition [3]. With these assumptions in mind, the formula that is used to estimate the contribution of a body part’s weight to the whole body resistance is as seen in equation 1.

V=p x S^2/R                (1)

The variable V represents the conductive volume; p represents the receptivity of the conductor; S represents the length of the conductor; and R represents the resistance of the cross-section area [3].

Two electrode configurations are used to measure four impedance values in the body – one from each electrode – and to decipher whether an anomaly has occurred [2]. The impedance analyzer makes use of a signal generator, sensor, switch, and drive and measurement electrodes in order to produce and measure a signal [2]. The principle behind Ohm’s Law (equation 2) is used to calculate the voltage difference between the two electrode configurations as current flows through the body [9]. For BIA measurements, electrodes are often placed at the wrist and ankle [9].

V=I x R                (2)

Impedance is the ratio between voltage and current, V/I, and is often represented as the variable, Z [9]. Moreover, Z is dependent upon resistance, R, and reactance, X, and can be expressed using the formula in equation 3.

Z=(R^2+X^2)^1/2            (3)

Engineers often use assumptions to simplify biological processes while also attempting to retain the maximum amount of information in said processes. Based on the assumptions that make up equations 1 and 3, researchers often encounter accuracy issues when analyzing BIA measurements [9]. To combat this, these equations can be manipulated so that they are only effective when the sample is reflective of its reference population’s impedance formula [9].

The Improvement

The one-chip microcomputer expands on prior solutions as it integrates all circuits that measure impedance onto a single platform. The inventors specifically compare their design to that of a body fat meter. In summary, the body fat meter makes use of a microcomputer, yet, continues to employ other instruments to calculate impedance, which results in 1) a large apparatus, 2) increased labor to create the circuit board, and 3) heightened likelihood of encountering noise [2]. In synopsis, the one-chip microcomputer allows for downsizing, offers a cheaper alternative, and reduces the margin for error. Additional patents use BIA to monitor abnormalities in the body [10], analyze organ output [11], or improve instrumentation of the device [12].

Figure 2. Patent Drawing. The above image displays the compartments needed to measure impedance on a one-chip microcomputer [2]. 

References

  1. Bioelectrical Impedance Analysis (BIA). Science for Sport. Website. https://www.scienceforsport.com/bioelectrical-impedance-analysis-bia/. Published May 20, 2018. Accessed February 27, 2020.
  2. Oguma, Koji, Miyoshi, Tsutomu. Bioelectrical impedance measuring apparatus constructed by one-chip integrated circuit. 2002.
  3. Dehghan, M., Merchant A.T. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr J. 2008; 7: 36. doi: 10.1186/1475-2891-7-26
  4. Essa’a, V.J., Dimodi, H.T., Ntsama, P.M. et al. Validation of anthropometric and bioelectrical impedance analysis (BIA) equations to predict total body water in a group of Cameroonian preschool children using deuterium dilution method. Nutrire. 2017; 42: 20. https://doi.org/10.1186/s41110-017-0045-y
  5. Bioelectrical Impedance Analysis. Amazon. Website. https://www.amazon.com/bioelectrical-impedance-analysis/s?k=bioelectrical+impedance+analysis. c1996-2020.  Accessed March 8, 2020.
  6. Terms of Service. Fitbit. Website. https://www.fitbit.com/us/legal/terms-of-service. Updated September 18, 2018. Accessed March 8, 2020.
  7. The Six Reasons The Fitness Industry Is Booming. Forbes. Website. https://www.forbes.com/sites/benmidgley/2018/09/26/the-six-reasons-the-fitness-industry-is-booming/#6c6e04e3506d. Published September 26, 2018. Accessed March 8, 2020.
  8. Grossi, M., Ricco, B. Electrical impedance spectroscopy (EIS) for biological analysis and food characterization: a review. J Sens Sens Syst. 2017; 6: 303-325. https://doi.org/10.5194/jsss-6-303-2017
  9. Bioelectrical Impedance Analysis in Body Composition Measurement. U.S. Department of Health & Human Services. https://consensus.nih.gov/1994/1994BioelectricImpedanceBodyta015html.htm. Published December 12, 1994. Accessed March 8, 2020.
  10. Chetham, Scott, M. Apparatus for connecting impedance measurement apparatus to an electrode. 2014.
  11. Kaiser, Willi, Fideis, Martin. Apparatus and method for obtaining cardiac data. 2007.
  12. Chetham, Scott, Daly, Newton, C., Bruinsma, John, I. Measurement apparatus. 2016.

 

A Look into Air Displacement Plethysmography

All information about this Air Displacement Plethysmography Chamber was retrieved from this patent: Air Circulation Apparatus and Methods for Plethysmographic Measurement Chambers 

Air Displacement Plethysmography

This air displacement plethysmography chamber is used to assess the body composition of patients. The measurements of fat and fat-free mass allow physicians to record important physical information about patients. Excess body fat and low levels of free-fat mass are indicators of various different diseases and developmental problems.  The major claim of the device is that air displacement plethysmography determines the volume of a patient by measuring the amount of air displaced when the patient sits in an enclosed chamber. This invention specifically includes an apparatus and plethysmographic measurements chamber that use air that has circulated through the chamber and replaced with air from outside the chamber in order to record its measurements. [1]

Who uses it?

Physicians primarily use air displacement plethysmography within the populations of infants and obese individuals. For low birth weight infants, variations in body composition can dictate infant energy needs and can indicate the health progression and future physical development of the infant. Air displacement measurements for infants must be more accurate than other body composition determining techniques because of an infant’s metabolic rate and longer measurement periods required due to their larger breathing artifacts. Excess body fat within obese individuals can be indicators of diseases such as cardiovascular disease, diabetes, hyper tension, hyperlipidemia, kidney disease, and musculoskeletal disorders. Athletes can also use this technology to determine their body composition to ensure that they are at peak physical shape for their required sport. [1]

How it works: A little bit of engineering for you

In air displacement plethysmography, the volume of air in the chamber is calculated through Boyle’s Law and/or Poisson’s Law. In most technologies, volume perturbations of a fixed frequency of oscillation are induced with the chamber and the perturbations lead to pressure fluctuations. The amplitude of the pressure fluctuations is determined and is used to determine the amount of air in the chamber through Boyle’s Law (isothermal conditions) or Poisson’s Law (using adiabatic conditions). [1]

Boyle’s Law: For gases at room temperature, there is an inversely proportional relationship between pressure and volume of that gas. [2]

P1V1 = P2V2

Where,

  • P1 is the initial pressure of the gas
  • V1 is the initial volume of the gas 
  • P2 is the final pressure of the gas 
  • V2 is the final volume of the gas

Poisson’s Law: In an adiabatic process, no heat transfer takes place between the surroundings and the system, or within the system. [3]

(P1V1)^Y= (P2V2)^Y

Where,

  • P1 is the initial pressure exerted by the gas
  • V1 is the initial volume occupied by the gas
  • P2 is the final pressure exerted by the gas
  • V2 is the final volume occupied by the gas
  • Υ is the ratio of specific heats, CP/ CV

By subtracting the volume of air remaining in the chamber (when the subject is in the container) from the volume of air in an empty chamber, body volume can be calculated indirectly.

Once the volume of the subject is known, body composition can be found with the volume, the weight, and the surface area of the subject. Body composition can be found by using the relationship between density and percent fat mass. The following two equations can be used to determine percent fat mass: 

Siri’s Equation: Percent Fat Mass=(4.95/Density)-4.5)*100) 

Brozek’s Equation: Percent Fat mass=((4.57/Density)-4.142)* 100)

Where,

Density= subject mass/subject volume

[1]

Better Than the Rest

There are other methods out there used to determine body composition, but they contain flaws compared to air displacement plethysmography. One method is skin folding, which uses calipers that compress the skin at certain points on the body. This technique is inaccurate in accounting for variations in fat patterning and requires perfect application of the calipers by a technician. Biometric impedance analysis (BIA) is also used to determine body composition. This technique requires the passing an electric current through a patient’s body, measuring its impedance value and comparing it to the known impedance value of muscle tissue thus to determine body composition. This method is not effective because impedance can be affected by the patient’s state of hydration, internal and external temperature, and BIA has not been used on infants. Lastly, the most common technique used to measure body composition is hydrostatic weighing. This process includes weighing the patient on land and repeatedly underwater to estimate the amount of air present in their lungs. This technique is incredibly invasive and unpleasant, especially for the populations of infants, the elderly, and individuals with disabilities. Air plethysmography is used because it is a less invasive technique for the populations of interest and it provides more accurate readings of body composition. [1]

There are a few components of the invention in the patent that differentiate it from other air displacement plethysmography devices. This plethysmographic measurement chamber prevents the accumulation of water vapor and carbon dioxide in the chamber, it addresses variations in chamber temperature due to body heat produced by the subject, and it maintains a safe and comfortable air composition for infants. All of these measures are due to internal systems and methods of circulating and renewing air within the chamber, while also maintaining the acoustic properties of the chamber at the perturbation frequency used to conduct the volume measurements. [1] 

Patent Information

The information from this post was retrieved from the following patent:

Patent Title: Air circulation apparatus and methods for plethysmography measurement chambers

Patent Number: US 2004/0193074 A1

Patent Filing Date: March 26, 2003

Patent Issue Date: September 30, 2004

How long it took for this patent to be issued: About 1.5 years 

Inventors: Philip T. Dempster, Michael V. Homer, Mark Lowe 

Assignee: Fish & Neave 

U.S. Classification: 600/587; 73/149

Amount of Claims: 57

[1]

Detailed Drawing

Figure 1: Labeled drawing of an air plethysmography displacement system with the following labeled components: 50. Entire plethysmographic system,  52. Plethysmographic measurement chamber, 54. Chamber door, 56. Plethysmographic measurement components, 58. Volume perturbation element, 60. Air circulation chamber, 62. Plethysmographic measurement components, 64. Computer, 66. Software for controlling operation of measurement components, 68. Inlet tube, 70. Exhaust tube [1]

References

  1. Dempster et al. (2004). Air Circulation Apparatus and Methods for Plethysmographic Measurement Chambers.  US 2004/0193074 A1. U.S. Patent and Trademark Office 
  2. (2019) Boyle’s Law – Statement, Detailed Explanation, and Examples. Retrieved from https://byjus.com/chemistry/boyles-law/
  3. (n.d.) Adiabatic Process. Retrieved from http://hyperphysics.phy-astr.gsu.edu/hbase/thermo/adiab.html

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.

Engineering Concerns for a Portable NIRS Device

When designing a portable Near-Infrared Spectroscopy (NIRS) device for the measurement of muscle oxygenation, design engineers have plenty of factors to consider. They must think about battery life, portability, affordability, safety, and many other design criteria. Before considering many of these criteria, however, an engineer must design a working technology that is capable of actually measuring muscle oxygenation. Without this basic attribute, the device would be a complete failure. The basics for measurement of relative oxygenated and deoxygenated hemoglobin concentrations was introduced previously in the patent blog post, but the engineering design problem was mostly glossed over. This post will dive a little deeper into the quantitative nature of measurement of muscle oxygenation and what functions the design engineer must consider when designing a device that will operate properly and accurately. The main question to be answered is: how does an engineer use light to measure concentration of a particle in muscle?

Fig 1: Molecular Absorption Coefficient Profiles for Oxygenated and Deoxygenated Hemoglobin

As mentioned before, NIRS works by measuring the absorbance or attenuation of light as it passes through a sample to make a measurement of concentration of the absorbing analyte or particle. Also previously introduced were the benefits of using near-infrared light since it can pass through biological tissue and is primarily absorbed by hemoglobin. In an ideal world the absorbance is defined by the Beer-Lambert Law. According to this law, the absorbance of a particle is equal to the natural log of incident light over the detected light and this is further equal to the product of the molar absorbance coefficient, the concentration of the particle, and the mean path length of detected photons. In an ideal case this law works because it describes when light is shown through a glass cuvette with a solution with only one absorbance particle, but this is not helpful for a NIRS device for muscle oxygenation. Thus, for a NIRS device, the modified Beer-Lambert Law must be used, which is the same as the original equation but with an extra scattering term to account for photon scatter when passing through tissue like skin and muscle (Eqn. 1).

Here A is absorbance, I0 is incident (transmitted) light, I is detected light, ɛ is molar absorbance coefficient, c is concentration, L is mean path length, and G is the scattering term. This is great in theory because it appears that concentration can be calculated relatively easily, but there are further problems to solve. Start by considering the knowns and unknowns. The absorbance coefficient is a known value for any analyte given the wavelength of the laser used (Fig. 1), and the path length can easily be found from the distance between the light emitter and detector with some regards to the path shape which is known to be roughly banana shaped. This leaves two unknown terms: the unknown that to be measured, i.e. concentration, and the scatter term. The scatter term is unfortunately a problem. It varies by tissue and considering the device should be designed for consumers to use on different locations, different muscles, and different amounts of say fat that may lie in the way of the muscle, this G term will forever be changing. Thus, there needs to be a way to get rid of it. The easiest way to do this is to find change in absorbance so that G will be subtracted away. This uses the assumption that G is constant for a given location. The resulting equation will then give change in concentration as it is the only factor that changed between measurements 1 and 2 (usually an initial measurement and a second measure at a later time) (Eqn. 2). Notice that absorbance is now equal to the natural log of the first intensity detected divided by the second intensity measured based on the identity (log(x/y) = log(x)-log(y). Note that the need to get rid of G, because it cannot be calculated on every single consumer, leads to the fact that NIRS devices almost always measure change in concentration or relative concentration when measuring muscle oxygenation.

This equation looks great. So change in concentration as opposed to exact concentration is found, but so what, this is still a very helpful measure for oxygenation during exercise. BUT, this equation is not the whole story. NIRS works by measuring both oxygenated and deoxygenated hemoglobin (Hb). Both species of Hb contribute to absorbance in the near-infrared range. Thus the equation actually looks like this (Eqn. 3)

In this equation, subscript O is used for oxygenated Hb, and subscript Hb is used for deoxygenated Hb. Now there are two unknowns and only one equation. So what does a smart engineer do? They add more lights. By measuring multiple wavelengths, two changes in absorbance can be measured allowing both concentrations to be calculated by solving the system of equation (Eqn. 4-5).

In these equations, superscripts refer to the wavelengths of light 1 and 2. It must be remembered that absorbance coefficient, absorbance change, and path length will all vary based on wavelength. This clearly allows for the output of relative concentrations or total blood oxygen saturation percentage (oxyHb / [oxyHb + deoxyHb]). Here the assumption is that total Hb is equal to oxyHb plus deoxyHb. The last piece of the puzzle for an engineer is to decide on what wavelengths should be used for the lights. This is a very impactful decision in building the algorithm to calculate the outcome measures of the device since ɛ, A, and L all depend on wavelength. It should be noted based on Figure 1 that certain wavelengths will be better than others. For example, if 805 nm light is used, then the absorbance coefficients for both species of Hb will be the same. This leads to irrational answers for Equations 4 and 5, so this wavelength should be avoided. The best case is to pick a wavelength above and below this so that one is more sensitive to oxyHb and the other is more sensitive to deoxyHb. Thus, using 750 and 850nm could be viable options, and these are used in several current devices.

These results allow an engineer to design a device that will properly measure muscle oxygenation through the relative concentrations of oxygenated and deoxygenated Hb. A reminder that some of the assumptions that needed to be made were that the tissue was homogenous, that oxy and deoxy Hb are the only particles contributing to absorbance, that absorbance is constant in time when Hb concentrations do not change, that the scattering term remained constant, and that oxy + deoxy Hb is the total Hb. Realistically, tissue is not homogeneous, but this assumption causes smaller errors in the volumes being considered close to the skin surface. Unfortunately, Hb is not the only chromophore contributing to absorbance. Fat is a major problem because it shares a similar range of wavelengths for absorbance. Some devices take fat correction into account, but other do not, and papers have pointed this out. It is reasonable to assume that absorbance is constant in time when concentration is constant, but pulsatile flow can cause error here. The scattering term should remain constant if the position of the device is not changed, and it is also reasonable to assume that there are not Hb species besides oxy and deoxy in the muscle. Some of these do cause limitations to the design described here, and as already mentioned it will only measure change in concentration not the absolute value. In conclusion, two wavelengths of light are needed measure muscle oxygenation with NIRS.

 

References

[1]. Shimadzu Commercial Website https://www.ssi.shimadzu.com/products/imaging/labnirs-principle-of-operation.html

[2]. Kocsis, L., Herman, P., & Eke, A. (2006). The modified Beer-Lambert law revisited. Physics in Medicine and Biology, 51(5). http://doi.org/10.1088/0031-9155/51/5/N02

[3]. Len-Carrin, J., & Len-Domnguez, U. (2012). Functional Near-Infrared Spectroscopy (fNIRS): Principles and Neuroscientific Applications. Neuroimaging – Methods. http://doi.org/10.5772/23146

[4]. McManus, C. J., Collison, J., & Cooper, C. E. (2018). Performance comparison of the MOXY and PortaMon near-infrared spectroscopy muscle oximeters at rest and during exercise. Journal of Biomedical Optics, 23(01), 1. http://doi.org/10.1117/1.jbo.23.1.015007

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