remove hand raise

treadmill_gait_analysis/function/gait_analysis.py

@@ -74,7 +74,7 @@ def __init__(self, session_dir, trial_name, leg='auto',
                 self.markerDict['markers'][marker] = self.markerDict['markers'][marker][:self.idx_trim_end,:]
             self.coordinateValues = self.coordinateValues.iloc[:self.idx_trim_end]
 
-        # Rotate marker data so x is forward
+        # Rotate marker data so x is forward (not using for now, but could be useful for some analyses).
         self.rotation_about_y, self.markerDictRotated = self.rotate_x_forward()
 
         # Segment gait cycles.
@@ -812,7 +812,7 @@ def get_coordinates_normalized_time(self):
 
         return coordinateValuesTimeNormalized
 
-    def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False):
+    def segment_walking(self, n_gait_cycles=-1, leg='auto', delete_hand_sync_stride=True, visualize=False):
 
         # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it 
         # finds that many gait cycles, working backwards from end of trial.
@@ -989,6 +989,10 @@ def detect_correct_order(rHS, rTO, lHS, lTO):
         gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=int)
         if n_gait_cycles <1:
             raise Exception('Not enough gait cycles found.')
+
+        if delete_hand_sync_stride:
+            r_wrist_rel_y = self.markerDict['markers']['r_mwrist_study'][:,1] - self.markerDict['markers']['r_shoulder_study'][:,1]
+            l_wrist_rel_y = self.markerDict['markers']['L_mwrist_study'][:,1] - self.markerDict['markers']['L_shoulder_study'][:,1]
 
         for i in range(n_gait_cycles):
             # Ipsilateral HS, TO, HS.
@@ -1026,6 +1030,12 @@ def detect_correct_order(rHS, rTO, lHS, lTO):
                                ' steps until the end. Skipping this step.')
                 gaitEvents_cont[i,:] = -1
                 gaitEvents_ips[i,:] = -1
+
+            elif delete_hand_sync_stride:
+                if (np.any(r_wrist_rel_y[gaitEvents_ips[i,0]:gaitEvents_ips[i,2]] > 0.1) or
+                   np.any(l_wrist_rel_y[gaitEvents_ips[i,0]:gaitEvents_ips[i,2]] > 0.1)):
+                    gaitEvents_cont[i,:] = -1
+                    gaitEvents_ips[i,:] = -1
 
         # Remove any nan rows
         mask_ips = (gaitEvents_ips == -1).any(axis=1)