Wearable inertial sensors have become a cornerstone technology for detecting fall-related events, such as tripping while walking, in older adults. However, low-cost devices must undergo rigorous validation to ensure fidelity and stability in measurement. This study aimed to evaluate the concurrent validity of a newly developed, low-cost triaxial accelerometer by comparing its linear acceleration measurements with those obtained from a widely used commercial reference sensor (RS). A methodological validation study was conducted to assess the accuracy and precision of the proposed accelerometer. Triaxial linear acceleration data were simultaneously collected from the new device (sensor under test, SUT) and a commercial RS (Physilog®5, GaitUp, Lausanne, Switzerland). Measurements were obtained across three independent axes from 49 SUT units, with three repeated trials performed for each axis. The RS exhibited a more negative mean bias on the mediolateral (x) axis and a slightly larger mean bias on the vertical (y) axis. The SUT showed a greater mean bias (offset) on the anteroposterior (z) axis. Root mean squared error values were highly similar between the x and y axes (<3.2% difference), while the z-axis presented moderately larger errors (∼9%). Mean absolute error was similar in the x and y axes, whereas the z-axis was slightly higher. Visual waveform comparisons demonstrated strong overlap in mean time-series profiles, and near-unity correlations indicated high correspondence in signal variation. Bland–Altman analysis confirmed minimal bias and narrow limits of agreement for the x and y axes, with greater variability observed along the z axis, but no evidence of substantial systematic deviation. Overall, the low-cost accelerometer (∼60 USD) showed strong agreement with the commercial reference device (>1 000 USD), supporting its validity for three-dimensional linear acceleration measurement under controlled laboratory conditions. This low-cost device represents a promising solution for scalable motion monitoring and fall-related event detection.