When artificial intelligence (AI) becomes increasingly central to scientific and technological advancements, the demand for large volumes of high-quality data intensifies. This challenge is particularly felt in developing countries, where the high cost of standard data acquisition equipment and their traditional, expensive calibration pose significant barriers to scientific research. This work addresses this issue by demonstrating how to validate the accuracy and reliability of a low-cost sensor (SIGA) designed for collecting triaxial acceleration data from healthy individuals during activities of daily living (ADLs). Data from SIGA were compared with data from a commercially available and validated sensor, widely used by the scientific community (i.e., Physilog). Both devices were attached together on a validation pendulum, and through Bayesian optimization, it was possible to demonstrate a low-cost sensor validation alternative such that a single reference sensor can be used to validate multiple low-cost sensors. This study also shows that low-cost sensors can offer precision compatible with scientific research, contributing to the democratization of access to this type of technology.