High Performance Accelerometer Data Logger X2-5

USB Accelerometer X2-5


The X2-5 is our newest high sensitivity data logger incorporating the Analog Devices ADXL355 digital sensor. The ADXL355 sensor is a significant improvement over the Kionix KXRB5-2050 sensor used in the previous X2-2 logger. The X2-5 offers 20-bit resolution, 2g/8g range, selectable sample rate up to 2000 Hz, and digital filtering options. The logger operates from an internal 500mAh lithium-polymer rechargeable battery that recharges when connected to a USB port. The X2-5 acts as a USB Mass storage device (like a USB flash drive) when connected to a computer, which provide quick and convenient access to data files. Data is stored as plain text Comma Separated Values format (.csv), ready to be imported into any spreadsheet, word processor, or end user application. Read more about our data logger product-line features.

ADXL355 noise performance

The plot illustrates the signal response comparison between the X16 series, X2-2, and X2-5 data loggers. The data represents the z-axis orientation only, which is typically the worst-case axis in MEMS type accelerometer sensors. The X2-5 noise floor is 1/3 that of the X2-2 (between 0-100 Hz).

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For more details, specifications and how to use the X2-5 please refer to the user manual(.pdf).


Need more logging time or a waterproof enclosure? Check this list of instructions for modifying the data logger.

Example Applications

The high sensitivity X2-5 logger can detect very low amplitude signals, which is applicable to vibration analysis.

Analysis Tools

No special software tools are required to configure the logger or retrieve the data. We recommend using a spreadsheet or math program such as Matlab, Octave, or R to analyze and plot data. Visit our data analysis page to learn more about R.


A simple text file is used to configure the device gain, dead band, and size of each data file.

Data Format

Data is contained in easily readable plain text files. Meta data starts with a ";" and the comma delimited data (.csv) can be analyzed with a spreadsheet or text processor as shown here.