![]() By observing the foot movement waveforms, it can be seen that the local minimum (point ‘a’ of Figure 2) of the thigh angle waveform next to the maximum refers to the Initial Contact point of the stride cycle. ![]() Figure 2 shows a time synchronized plot of the thigh angle with the vertical and forward movements of the foot. To enable identifying key points of stride cycle in the thigh angle waveform, the foot movement was also recorded. This thigh angle was obtained from an experiment conducted in a Motion Analysis Lab (MAL) containing a Vicon optical motion capture system, which is discussed later. Although GIOF was only implemented and tested for estimating pitch of the IMU, it can be easily extended into 2D to estimate both pitch and roll.Ī sample of the flexion–extension movement of the thigh (thigh angle) when the subject is walking on a level surface is shown in Figure 1a. The computation time on an 8-bit microcontroller running at 8 MHz for GIOF is about a half of that of Complementary Filter implementation. The Root Mean Square Error (RMSE) of the thigh angle estimated by GIOF compared with Vicon measurement was 1.8477°. The thigh angle estimated by GIOF was compared against the Vicon Optical Motion Capture System and reported a mean correlation of 99.58% for 374 walking trials with a standard deviation of 0.34%. This strategy was used to reduce the drift caused by the gyro integration. ![]() The algorithm fuses the accelerometer and gyroscope readings to derive the single dimension orientation in such a way that the orientation is corrected using the accelerometer reading when it reads gravity only or otherwise integrate the gyro reading to estimate the orientation. This paper discusses a computationally inexpensive orientation estimation algorithm (Gyro Integration-Based Orientation Filter-GIOF) that is used to estimate the forward and backward swing angle of the thigh (thigh angle) for a vision impaired navigation aid. Most of the algorithms used for orientation estimation are computationally expensive and it is difficult to implement them in real-time embedded systems with restricted capabilities. Inertial measurement units are commonly used to estimate the orientation of sections of sections of human body in inertial navigation systems.
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