Limb length is a useful parameter in the assessment of common musculoskeletal disorders such as limb length discrepancy. The measurement variation among rates adversely affects the quantitative aspect of assessments and introduces a greater subjectivity in the course of treatment. Common practise for measuring limb length is based on radiographic imaging techniques which are inconvenient, costly and require clinical knowledge. Direct instruments are difficult to use with patients due to susceptibility to human error in determining the position of the rotational joint. In this study, the determination of limb length is automated using a contemporary algorithm which applies curvature to the measurements from a low-cost and miniaturised inertial sensor, primarily used in the bio-kinematic research. The motion artefacts contribute to the ultimate estimations and, in this approach, a least noise threshold model is employed to address the robustness. The proposed estimation technique was validated with real-data observed from 14 healthy subjects comparing with radiographic and direct measurements. The experimental results indicate greater accuracy compared with manual measurements with low root mean squared error percentages with values ranging from 5.34 to 5.84%. Additionally, the mean limb length difference between our estimator and both radiographic measurements and direct measurement was <1.6 cm.