Measuring multiple parameters, accurate readings, and having a long battery lifetime: these are the most critical parameters for a wearable device that monitors cardiovascular health. Wearable devices that were initially meant for sports and wellness purposes are now being designed for medical purposes, measuring critical health parameters such as heart rate and heart rate variability (HRV). With this transition, the accuracy of the measurements and device's battery lifetime become more important design considerations. This article examines this new generation of wearable health devices, including how to make a system more reliable and more power efficient.
PPG for Heart Rate Measurement
When it comes to health, one of the most important organs in the body is the heart. Without a well-performing heart and heartbeat, people can face serious health issues. For that reason, monitoring the heart function is a key priority. There are many good reasons for checking heart rate that go beyond the number of beats per minute. In addition, a significant amount of additional information can be gained from the behavior of the heart in terms of the frequency as function of activity. When more activity is asked from the body, the heart rate should go up to bring more nutritious- and oxygenated-blood to the cells. A continuous high heart rate or a fast-changing heart rate can be indicators of a cardiac disease such as atrial fibrillation.
Besides monitoring heart frequency, another parameter, monitoring HRV, is also important. When a person is relaxed, the heart won't beat with a fixed number of beats per minute, but it should experience a slight variation around the heart frequency, something in the range of 3 beats per minute. This variation is an indicator for being relaxed. At the moment that people get stressed or get a startled response, the adrenalin level in the body goes up and the heart starts pumping with a very monotonic frequency. For this reason, the HRV parameter is important to monitor.
The classical way for retrieving cardiac signals is by biopotential measurement with an electrocardiogram (ECG); however, this is not easy to integrate into wearable devices. A trend for measuring heart rate other than biopotential is by making use of an optical principle. This technology has existed for quite some time and is called photoplethysmogram (PPG). PPG technology mainly has been used in systems for measuring oxygen saturation in the blood (SPO2). To measure SPO2, two wavelengths of light are sent through a particular part of the body (usually the finger or earlobe), measuring the percentage of oxygenated hemoglobin versus the total amount of hemoglobin. This technology is also commonly used in wearable systems such as small wrist-worn devices, where unlike a biopotential measurement, it is possible to pick up the heart rate using a single measurement point. The ADPD174 from Analog Devices is an optical subsystem, which has been designed to support these applications (see Figure 1).
Reflective Versus Transmission
The SPO2 measurement is usually performed with a clip on the finger or earlobe. Light is sent through a part of the body and at the opposite site, the received signals are being measured by a photodiode. This transmission technique measures the amount of received light or light that is not absorbed. This principle is best in class in terms of signal performance versus the amount of power spent.
Integration of transmissive measurement, however, isn't an easy task in a wearable system where comfort is key. Therefore, reflective measurement is more commonly used in wearable systems. In a reflective optical system, light is sent into the surface of the tissue, whereupon a part is absorbed by the red blood cells, and the remaining light is reflected back to the tissue surface and measured by a photosensor. In a reflective system, the receive signals are up to 60 dB weaker, so it is important to pay attention to electrical and optical aspects of the transmit and receive signal chain.
Electronic and Mechanical Challenges
During a heartbeat, the flow and volume of blood is changing, resulting in scattering of the amount of reflected light received. The wavelength of the light that is used for measuring the PPG signals can vary depending on a number of factors, the first being the type of measurement. The discussion in this article is limited to the measurement of just heart rate and variation. For this measurement, the required wavelength depends not only on the location on the body where the measurement is being taken, but also on the relative perfusion level, temperature of the tissue, and color tone of the tissue. Arteries are not located on top of the wrist, so for wrist-worn devices, pulsatile components are picked up from veins and capillaries just under the skin surface. The wavelength of a green light in these applications is most effective. Where there is sufficient blood flow, like the upper arm, temple, or ear canal, red or infrared wavelengths will be more effective as they penetrate deeper into the tissue. Because red or infrared LEDs require a lower forward voltage, they are also suitable for wearable applications where battery power and size is always an issue. For applications where coin cell batteries are used, these LEDs can be driven directly from the battery voltage.
Unfortunately, green LEDs need a higher forward voltage that requires an additional boost converter, and so these have a negative impact on a system's overall current consumption. Figure 2 shows the required forward voltage for different LED colors as function of the current. If Green LEDs are still required, the ADP2503 buck/boost converter could be of help to support a higher LED forward voltage up to max 5.5 V, operated from an input voltage, which can go as low as 2.3 V.
When trade-offs such as sensor-position and LED color are being made, the next step is to select the most appropriate optical solution. Many types of analog front ends (AFEs) — both discrete built or fully integrated — are available, but there are also many photosensors and LEDs. To minimize design efforts and to shorten time to market, ADI built ADPD174, a fully integrated optical subsystem for reflective optical measurement (see Figure 3). The module is 6.5 × 2.8 mm, which makes it suitable for wearable systems.
The module is built around a big photodiode, two green LEDs, and an IR LED. The onboard mixed signal application-specific integrated circuit (ASIC) includes an analog signal processing block, a SAR-type ADC, a digital signal processing block, an I2C communication interface, and three free programmable LED current sources. The system drives the LEDs and measures the corresponding optical return signal with its 1.2 mm2 photodiode. The biggest challenge for measuring PPG with a wearable device is overcoming interferers like ambient light and artifacts generated by motion.
Ambient light can greatly influence the measurement results. Sunlight is not too difficult to reject, but light from fluorescent and energy-saving lamps, which include AC components, are difficult to cancel. The ADPD174 optical module has a two-stage ambient light rejection function. After the photosensor and input amplifier stage, a band-pass filter is integrated, followed by a synchronous demodulator, to offer best-in-class rejection for ambient light and interference from DC up to 100 kHz. The ADC has a resolution of 14 bits and up to 255 pulse values can be summed to get a 20-bit measurement. Additional resolution up to 27 bits can be achieved by accumulating multiple samples.
The ADPD174 operates in two independent time slots, which enables it to measure two separate wavelengths and carry out the results sequentially. During each time slot, the complete signal path is executed, starting with LED stimulation followed by photo signal capturing and data processing. Each current source can drive the connected LED with currents up to 250 mA. Innovative control over the pulsing of the LED keeps the average power dissipation low and contributes significantly to power savings and thus the battery life of the system.
The advantage of this LED driving circuit is that it is dynamic and scalable on the fly. Many factors can affect the signal-to-noise ratio (SNR) of the received optical signal, including skin tone or hair between the sensor and skin, which affects the sensitivity at the receiving side. For this reason, the excitation of the LEDs can be easily configured to build an auto-adaptive system. All timing and synchronization is handled by the AFE, so no overhead is required from the microprocessor in the system.
With the ADPD174, a wearable device can run a reliable heart rate monitor in normal circumstances at a power level of around a milliwatt. To find this operating point, the gain of the transimpedance amplifier (TIA) can be tuned in combination with setting the maximum LED peak current. After optimizing the LED current and TIA gain the number of LED pulses can be increased to get more signal. It is important to note that increasing the LED peak current increases the SNR proportionally, whereas increasing the number of pulses by a factor of n results in an SNR improvement of the root of n (√(n)) only.
Finding the optimum settings for a particular heart rate device also depends heavily on the user. In addition to the user's skin tone, device positioning, temperature, and blood flow also affect the signal strength. For calculating the power consumption, the optical front-end can be treated as two separate power contributors, IADPD and ILED. IADPD is the current consumed by the input amplifier stage, the ADC, and the digital state machine. These power numbers very much depend on the sampling rate of the ADC.
The LED current ILED changes with the person's skin tone and the position of the sensor on the body. For darker skin tones, more LED current is needed. This is also the case for sensor positions on the body where there is very little blood flow. The average LED current changes with the LED drive pulse width, the number of pulses, and the ADC sampling time. The average LED current is the max LED current multiplied by the pulse width and the number of pulses. This average LED current uses a single time slot and repeats every time a new sample is taken. The pulse width can be as narrow as 1 μs.
To achieve a good heart rate measurement on the wrist, an LED peak current is required of around 125 mA, when using 2 pulses with 1 μs width. Considering a 100 Hz sample frequency, the average LED drive takes 25 μA. When 250 μA average AFE current is added, the optical front end is consuming 275 μA (@ 3 V = 825 μW).
Additional Mechanical Challenges
Interference from ambient light is one of the challenges designers face when designing an optical system. Another major challenge to overcome in a reflective-mode optical system is internal light pollution. In a perfectly designed system, all light from the LEDs is sent into the tissue, and only reflected light is seen and measured by the photosensor. In reality, however, LED light can be reflected by the transparent window of the housing and sent back directly to the photosensor without penetrating the tissue (see the light path marked green in Figure 4.)
This ILP effect results in a DC offset and limits the AC component of the signal, also called modulation index (MI). The MI, in fact, is the only signal of interest. ILP can be resolved by separation of the window; however, this is very difficult and costly to implement in volume production. The ADPD174 has a specially designed housing to reduce the ILP behavior without the requirement for separation of the transparent window in the housing. In Figure 5, ILP as a function of the LED current is reduced on the ADPD174 compared with its predecessor.
Total System Power
In an optical system, the interference of motion also needs to be canceled. Motion affects the overall performance of a wearable system. Motion can cause the mechanical connection or contact to the tissue to change, which leads to errors in the optical reading. Therefore, it is important to measure the motion of the device and compensate for the interferers. ADI's ultra-low power three-axis ADxL362 MEMS measures all three axes. It has an integrated 12-bit SAR ADC, resulting in a least significant bit (LSB) size of 1 mg and communicates over a digital SPI interface. The power dissipation scales with the ADC sampling rate. At a data output rate of 100 Hz per axis, the sensor dissipates only 1.8 μA.
Connecting It All Together
This article has presented various sensors needed to build a wearable health device for monitoring heart rate and HRV. These sensors must be connected, running the required software algorithms, and either storing, visualizing, or transmitting the results. An ultra-low power, mixed signal microcontroller is needed to bring it all together. For example, the ADuCM3027/29 CortexTM-M3 processor consumes less that 38 μA per MHz of processing power. It has a max clock frequency of 26 MHz and can be operated in four different power modes:
- Active: <38 μA/MHz (all analog and digital working)
- Flexi: <11.5 μA/MHz (analog active, core clock gated, MCU down)
- Hibernate: <900 nA (RTC running, wake-up interrupts active, SRAM retained)
- Shutdown: <60 nA (analog/digital in deep sleep; only wake-up interrupts active).
The mixed signal front end includes a 12-bit SAR type ADC, a reference buffer, and a temperature sensor. It includes either 128 k or 256 kB of onboard flash memory, 4 kB of cache memory, and 64 kB of SRAM on board. Device content is protected from being read through an external interface by an unauthorized user to protect code and algorithms. It can be operated from a single operating voltage between 1.8 and 3.6 V, where internally the core voltage of 1.2 V can be generated by either the onboard LDO or its more efficient switch capacitor step-down converter.
For wireless uploading of the measurement results to a host processor, a fair amount of the overall system power is required. Preprocessing the measurement results helps to reduce the amount of data that needs to get transmitted, which means additional power savings.
This article was written by Jan-Hein Broeders, Healthcare Business Development Manager Europe for Analog Devices Inc., Norwood, MA. For more information, Click Here .