We translated the body position coordinates to egocentric coordinates by subtracting the predicted location of the intersection between the thorax and abdomen from all other body-position predictions for each frame. We then calculated the instantaneous velocity along the rostrocaudal axis of each leg tip within these truly egocentric reference coordinates. The speed of each body part was smoothed using a Gaussian filter with a five-frame moving window. For each leg tip, instances in which the smoothed velocity was greater than zero were defined as swing, while those with velocity less than zero were defined as stance. Information from this egocentric axis was combined with allocentric tracking data to incorporate speed and orientation information. The centroids and orientations of the flies were smoothed using a moving mean filter with a five-frame window to find the instantaneous speed and forward velocity. To remove idle bouts and instances of backward walking, all gait analyses were limited to times when the fly was moving in the forward direction at a velocity greater than 2 mm s−1 (approximately one body length per second) unless otherwise noted. The analyses relating stance and swing duration to body velocity were limited to forward velocities greater than 7.2 mm s−1, to remain in line with previous work25 .
To measure gait modes, we trained an HMM to model gait as described previously41 (link). The training data consisted of a vector denoting the number of legs in stance for bouts in which the fly was moving forward at a velocity greater than 2 mm s−1 lasting longer than 0.5 s. Training data were sampled such that up to 3,000 frames were taken from each video, resulting in a total of 159,270 frames. We trained a three-state HMM using the Baum–Welch algorithm and randomly initialized transition and emission probabilities51 . We designated each hidden state as tripod, tetrapod or noncanonical in accordance with the estimated emission probabilities. We then used the Viterbi algorithm along with our estimated transition and emission matrices to predict the most probable sequence of hidden states from which the observed stance vectors for the entire dataset would emerge52 .
To measure gait modes, we trained an HMM to model gait as described previously41 (link). The training data consisted of a vector denoting the number of legs in stance for bouts in which the fly was moving forward at a velocity greater than 2 mm s−1 lasting longer than 0.5 s. Training data were sampled such that up to 3,000 frames were taken from each video, resulting in a total of 159,270 frames. We trained a three-state HMM using the Baum–Welch algorithm and randomly initialized transition and emission probabilities51 . We designated each hidden state as tripod, tetrapod or noncanonical in accordance with the estimated emission probabilities. We then used the Viterbi algorithm along with our estimated transition and emission matrices to predict the most probable sequence of hidden states from which the observed stance vectors for the entire dataset would emerge52 .
