Choosing the right accelerometer for your application can be a complex task, as data sheets from different manufacturers often vary significantly, making it challenging to identify the most critical technical specifications. The first three parts of this series, "Three Key Dimensions + Key Indicators: Select the MEMS Accelerometer That Works Best for You," explored the essential parameters and characteristics of accelerometers and their relevance to tilt and stability applications.
In this second part of the series, we focus on wearable devices, status monitoring, and networking applications, discussing the key technical specifications and features that are most relevant in these areas.
Wearable Devices
Key Metrics: Low power, small size, integrated features, and energy efficiency.
For battery-powered wearable applications, the primary metric is ultra-low power consumption (typically in the range of microamps) to maximize battery life. Other important metrics include compact size and integrated features such as alternate ADC channels and deep FIFO buffers, which enhance power management and system functionality. This is why MEMS accelerometers are widely used in wearable technology.
Table 1 highlights some vital signs monitoring (VSM) applications and their corresponding settings. Accelerometers in wearables are typically used to classify motion, detect free fall, determine if the system is active or dormant, and assist in data fusion for ECG and other VSM measurements. These same sensors are also suitable for wireless sensor networks and IoT applications due to their low power consumption.
To select an accelerometer for ultra-low power applications, it's crucial to review the sensor’s performance at nominal power levels as specified in the datasheet. A key parameter to watch is whether the bandwidth and sampling rate drop to a level where the acceleration data cannot be accurately measured. Some competing products achieve low power by waking up every second, but this approach may miss critical data due to reduced sampling rates. For real-time human motion tracking, higher power consumption is necessary.
The ADXL362 and ADXL363 do not alias input signals through undersampling; they sample the entire sensor bandwidth at full data rate. Power consumption varies dynamically with the sampling rate, as shown in Figure 1. These devices can operate at up to 400 Hz with a power consumption of only 3 μA, enabling features like click/double-click detection. When operating at 6 Hz, the average power consumption drops to 270 nA, making them ideal for implantable applications where battery replacement is impractical.
Figure 1. ADXL362 supply current vs. output data rate
For applications requiring occasional polling, the ADXL362 and ADXL363 offer an awake mode that consumes just 270 nA. The ADXL363 integrates a three-axis MEMS accelerometer, a temperature sensor, and an onboard ADC in a compact package (3 mm x 3.25 mm x 1.06 mm). It includes a 512-sample FIFO buffer, allowing data storage for up to 13 seconds.
ADI has developed a demo-only VSM watch (shown in Figure 2) to demonstrate the potential of ultra-low-power devices like the ADXL362 in space-constrained, battery-powered applications.
Figure 2. VSM watch (integrated with various ADI devices highlighting ultra-low power, small size, and lightweight design)
The ADXL362 is used to track motion and record it, helping eliminate interference artifacts from other measurements.
Condition Monitoring (CBM)
Key Metrics: Low noise, wide bandwidth, signal processing, g-range, and low power.
CBM systems monitor parameters like machine vibration to detect and indicate potential faults. This is a critical part of preventive maintenance, commonly used in turbines, fans, pumps, and motors. The key metrics for CBM accelerometers are low noise and wide bandwidth. Currently, few competitors offer MEMS accelerometers with bandwidths above 3.3 kHz, while some manufacturers provide up to 7 kHz.
With the rise of the Industrial Internet of Things, there is increasing emphasis on reducing wiring and using wireless and ultra-low-power technologies. This makes MEMS accelerometers more favorable than piezoelectric ones in terms of size, weight, and power consumption. While piezoelectric accelerometers are widely used for their linearity, SNR, and high-temperature performance, they perform poorly in the DC range, leading to errors in low-frequency to DC fault detection, especially in wind turbines.
MEMS capacitive accelerometers offer greater integration, including self-test, peak acceleration, spectrum alarms, FFT, and data storage, along with DC response and smaller size. The ADXL354/ADXL355 and ADXL356/ADXL357 are ideal for condition monitoring due to their ultra-low noise and temperature stability, though their limited bandwidth restricts deeper diagnostics. However, they still provide valuable measurements, such as in wind turbine monitoring where DC response is required.
The ADXL100x family of single-axis accelerometers offers measurement bandwidths up to 50 kHz, g-ranges up to ±100 g, and ultra-low noise performance, making them comparable to piezoelectric accelerometers.
For more details on ADI MEMS capacitive accelerometers versus piezoelectric ones, see the article: "MEMS Accelerometer Performance Is Well Established."
The frequency response of the ADXL1001/ADXL1002 is shown in Figure 4. Major failures in rotating machinery, such as bearing damage, misalignment, imbalance, and cavitation, fall within the measurement range of the ADXL100x Series Condition Monitoring Accelerometer.
Figure 4. Frequency response of the ADXL1001/ADXL1002; laser vibrometer controller based on the ADXL1002 package to improve accuracy.
Piezoelectric accelerometers typically lack intelligent features, whereas MEMS capacitive accelerometers like the ADXL100x series include overrange detection circuits that alert when a serious overrange event occurs. This feature is essential for intelligent measurement and monitoring systems. The ADXL100x uses internal clock mechanisms to protect sensor components during continuous overrange events, reducing the burden on the host processor and increasing the intelligence of the sensor node—key indicators for stateful monitoring and industrial IoT solutions.
MEMS capacitive accelerometers have made significant strides in performance, allowing the new ADXL100x series to compete strongly against traditional piezoelectric sensors. The ADXL35x family offers industry-leading ultra-low noise performance and can replace sensors in CBM applications. New CBM solutions are now merging with IoT architectures to create better detection, connectivity, and analysis systems. ADI’s latest accelerometers enable smarter edge-node monitoring, allowing plant managers to implement fully integrated vibration monitoring and analysis systems.
Further complementing these MEMS accelerometers is the first-generation CBM subsystem, the ADIS16227 and ADIS16228 semi-autonomous, fully integrated wide-bandwidth vibration analysis systems (shown in Figure 5). These products include features like six-band programmable alarms, level 2 alarm settings, adjustable response delays, internal self-tests, and status flags.
Figure 5. Digital triaxial vibration sensor with integrated FFT analysis and storage system
Frequency domain processing includes 512-point real-valued FFT and FFT averaging per axis, which reduces background noise variation and improves resolution. The ADIS16227 and ADIS16228 fully integrated vibration analysis systems reduce design time, cost, and space constraints, making them ideal for CBM applications.
Internet of Things / Wireless Sensor Network
Key Metrics: Power consumption, integrated features for smart energy savings, small size, deep FIFO, and appropriate bandwidth.
The future of the Internet of Things (IoT) is well understood, and millions of sensors will need to be deployed in the coming years. Most of these sensors will be installed in hard-to-reach or space-constrained locations, such as roofs, street lights, towers, bridges, and heavy machinery, to support smart cities, agriculture, and buildings. Due to these limitations, many sensors will require wireless communication and battery power, with some needing energy harvesting.
The trend in IoT applications is to minimize the amount of data transmitted wirelessly to the cloud or local servers, as existing methods are costly and require high bandwidth. By performing intelligent processing at the sensor node, useless data can be filtered out, reducing the need for large data transfers and lowering bandwidth and cost requirements. This demands sensors with intelligent features while maintaining ultra-low power levels. The standard IoT signal chain is shown in Figure 6.
Figure 6. ADI’s edge sensor node solution
Outside the gateway, Analog Devices offers solutions for each module. Not all solutions require a wireless connection—many applications still rely on wired solutions like RS-485, 4 mA to 20 mA, or Industrial Ethernet.
Once a node has some intelligence, it can transmit only useful data through the signal chain, saving power and bandwidth. In CBM, the amount of processing done locally depends on factors like machine cost, complexity, and condition monitoring system cost. Data transmission ranges from simple over-range alarms to data streams. Standards like ISO10816 specify alarm conditions. When a machine runs at a specific RPM, it triggers an alarm if vibration speed exceeds a threshold. ISO 10816 aims to optimize the effective life of the system under test and its rolling bearings, so reducing data transmission supports deployment in WSN architecture.
For accelerometers used in ISO10816 applications, g-values up to 50 g are required, with low noise maintained at low frequencies because the system periodically integrates acceleration data to form mm/sec rms. When integrating low-frequency noise, speed output error may increase linearly. ISO 10816 specifies a measurement range of 1 Hz to 1 kHz, but users want data integration down to 0.1 Hz. Traditionally, charge-coupled piezoelectric accelerometers are limited by low-frequency, high-noise levels, but ADI’s next-gen accelerometers keep the noise floor at DC and are affected only by 1/f noise. The corner frequency limit can be reduced to 0.01 Hz with careful design. MEMS accelerometers are ideal for cost-effective CBM applications or embedded solutions due to their smaller size and lower cost compared to piezoelectric sensors.
ADI’s wide range of accelerometers are perfect for intelligent sensor nodes requiring ultra-low power, with multiple features that extend battery life, reduce bandwidth usage, and lower costs. Key metrics for IoT sensor nodes include low power (ADXL362, ADXL363) and rich energy management and data detection features like threshold activity, line profile alarms, peak acceleration values, and long activity or inactive periods (ADXL372, ADXL375).
All these accelerometers enter a low-power state when storing acceleration data in the FIFO and checking for activity. When an impact event occurs, the data before the event is frozen in the FIFO. Without a FIFO, capturing samples before the event would require continuous sampling, significantly reducing battery life. The ADXL362 and ADXL363 FIFOs can store over 13 seconds of data, clearly showing events before the trigger. Instead of using a power duty cycle, a full bandwidth architecture is used at all data rates, preventing signal aliasing and maintaining ultra-low power consumption.
Asset Condition Monitoring
Key Metrics: Power consumption, integrated features for smart energy savings, small size, deep FIFO, and proper bandwidth.
Asset Condition Monitoring (AHM) involves monitoring high-value assets over time, whether at rest or in transit. These assets could include cargo, remote pipelines, civilians, warriors, or high-density batteries. They are vulnerable to impacts or shocks. The IoT provides an ideal infrastructure for reporting such events that might affect asset functionality or security. For AHM sensors, the key metric is the ability to measure high-g shock and impact events while maintaining ultra-low power. When embedded in battery-powered or portable applications, other key sensor metrics include size, oversampling, anti-aliasing properties, and intelligent features that increase the host processor’s sleep time and allow interrupt-driven algorithms to detect and capture shock characteristics, extending battery life.
The ADXL372 micropower ±200 g MEMS accelerometer meets the demand for smart IoT edge nodes in the emerging asset condition monitoring market. It includes unique features tailored for AHM, simplifying system design and enabling energy savings at the system level. High-g events, such as shocks or impacts, are often associated with acceleration components across wider frequencies. To accurately capture these events, a wide bandwidth is needed, as insufficient bandwidth can significantly reduce the recorded event magnitude, causing errors. This is a key parameter to pay attention to in the datasheet. Some devices do not meet the Nyquist sampling rate. The ADXL375 and ADXL372 offer the option to capture the entire shock characteristics for further analysis without host processor intervention. This can be achieved using the Shock Interrupt Register in conjunction with the accelerometer’s internal FIFO. As shown in Figure 7, having sufficient FIFO is crucial to determine impact characteristics before the trigger event. If the FIFO is insufficient, the impact event cannot be recorded and analyzed.
Figure 7. Accurate capture of shock characteristics
The ADXL372’s operating bandwidth can reach 3200 Hz at ultra-low power levels. Its steep filter roll-off helps suppress out-of-band components effectively. The ADXL372 integrates a quadrupole low-pass anti-aliasing filter. Without anti-aliasing filtering, any input signal whose frequency exceeds half the output data rate will alias into the target measurement bandwidth, resulting in measurement errors. The quadrupole low-pass filter provides user-selectable filter bandwidth for maximum flexibility in user applications.
With the Instantaneous Impact Detection feature, the ADXL372 can be configured to capture shock events above a certain threshold in ultra-low power mode. As shown in Figure 8, after an impact event occurs, the accelerometer enters a full measurement mode to accurately capture the impact characteristics.
Figure 8. Instant conduction mode with default threshold
Some applications require only peak acceleration samples from shock events, as such samples themselves provide sufficient information. The ADXL372 FIFO can store peak acceleration samples for each axis. The maximum length of time that can be stored in the FIFO is 1.28 seconds (512 single-axis samples at 400 Hz ODR). The 170 3-axis samples at 3200 Hz ODR correspond to a 50 ms time window, enough to capture a typical shock waveform.
For applications that don’t require full event characteristics, storing only peak acceleration information allows longer intervals between FIFO read operations, resulting in further energy savings. 512 FIFO samples can be allocated in various ways, including:
- 170 sets of parallel 3-axis data
- 256 sets of parallel 2-axis data (user selectable)
- 512 sets of uniaxial data
- 170 peaks of impact events (x, y, z)
Proper use of the FIFO allows the host processor to remain dormant for extended periods while the accelerometer collects data autonomously, reducing system-level power consumption. Alternatively, using FIFOs to collect data can reduce the load on the host processor, allowing it to handle other tasks.
There are several other high-g accelerometers on the market, but they are not suitable for AHM/SHM IoT edge node applications due to narrow bandwidth and high power consumption. In low power modes, they generally have low bandwidth that cannot be accurately measured. The ADXL372 truly implements a ready-to-use AHM/SHM implementation model, enabling end customers to rethink potential asset classes wherever practicable.
Figure 9. ADXL372 in action
In conclusion, Analog Devices offers a wide range of accelerometer products for various applications, including dead reckoning, AHRS, inertial measurement, automotive stability, medical alignment, and more. Our next-generation MEMS capacitive accelerometers are ideal for applications requiring low noise, low power, high stability, and temperature stability, along with low compensation and integrated intelligence to improve overall system performance and design complexity. Analog Devices provides all relevant datasheet information to help you choose the best device for your application. All devices mentioned in this article, as well as others, are available for evaluation and prototyping.
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