Estimation of spinal shape profiles in motion using accelerometers

To achieve real-time monitoring of spinal shape profile in daily life we have developed a wireless wearable sensor, small in size, that has a tri-axial accelerometer. By using ten sensors on the surface of the back of the body, a spinal shape profile is represented as a set of angles calculated from the gravitational accelerations measured. In this paper, we propose a new method which removes the effect of motional component by the angle calculation according to a constrained optimization formulation. In our formulation, angles and movable ranges of 19 virtual vertebrae are expressed in constraint conditions. Experiments comparing the spinal shape profiles in standing, lifting, and jumping forward motions demonstrate that our method is comparable to those by optoelectronic motion systems and gives better estimations than those by low-pass filter (LPF). In a jumping forward motion, although the maximum error of LPF is more than 32 cm, our method is less than 9 cm. These results show that our method obtained small errors, even when the LPF shows unrealistic profiles due to the large motional acceleration.