Studying people in their daily life is important for understanding conditions with multi-faceted aetiology such as chronic low back pain. Inertial measurement units can be used to reconstruct the posture and motion of the body outside of laboratories to enable this research. The battery life of these sensors strongly affects the usability of the system, since recharging them frequently is inconvenient and can lead to additional errors. A major determinant of the battery life for these sensors is sampling rate, but the relationship between sampling rate and accuracy in motion reconstruction is not well documented. We measured the spine of 12 participants using inertial measurement units across a variety of tasks such as sitting, standing, walking, and jogging. The orientation of the spine was reconstructed using several filters, including a novel filter developed specifically for high performance at low sampling frequencies. Benchmarking against optical motion capture, we developed a model showing that the error of all tested filters depends exponentially on the sampling frequency, with the optimal filter gains showing a similar exponential relationship. Using this model of error, we developed a criterion for recommending minimum sampling frequencies for accurate motion estimates for each task, finding frequencies ranging from about 13 to 35 Hz sufficient depending on the task. Although we only studied the spine, these models should provide insight into optimizing sampling rate and filter parameters for inertial measurements in general use.