Inertial wearable sensors constitute a booming industry. They are self contained, low powered and highly miniaturized. They allow for remote or self monitoring of health-related parameters. When used to obtain 3-D position, velocity and orientation information, research has shown that it is possible to draw conclusion about issues such as fall risk, Parkinson disease and gait assessment. A key issues in extracting information from accelerometers and gyroscopes is the fusion of their noisy data to allow accurate assessment of the disease. This, so far, is an unsolved problem. Typically, a Kalman filter or its nonlinear, non-Gaussian version are implemented for estimating attitude â?? which in turn is critical for position estimation. However, sampling rates and large state vectors required make them unacceptable for the limited-capacity batteries of low-cost wearable sensors. The low-computation cost complementary filter has recently been re-emerging as the algorithm for attitude estimation. We employ it with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate. Inertial sensor data is obtained from the 10-axis SP-10C sensor, attached to a wearable insole that is inserted in the shoe. Data is obtained from walking in a structured indoor environment in Votey Hall.