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Merge pull request #22 from stanfordnmbl/order-analysis
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ordering scalars
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@antoinefalisse
antoinefalisse authored May 7, 2024
2 parents 1070e71 + 29ed0df commit 2634302
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Showing 2 changed files with 123 additions and 97 deletions.
109 changes: 61 additions & 48 deletions gait_analysis/function/handler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,19 +66,16 @@ def handler(event, context):
filter_frequency = 6

# Select scalar names to compute.
scalar_names = {
'gait_speed','stride_length','step_width','cadence',
'double_support_time','step_length_symmetry'}
# 'single_support_time',

scalar_labels = {
'gait_speed': "Gait speed (m/s)",
'stride_length':'Stride length (m)',
'step_width': 'Step width (cm)',
'cadence': 'Cadence (steps/min)',
# 'single_support_time': 'Single support time (% gait cycle)',
'double_support_time': 'Double support (% gait cycle)',
'step_length_symmetry': 'Step length symmetry (%, R/L)'}
scalars = {
'gait_speed': {'label': 'Gait speed (m/s)', 'order': 0},
'stride_length': {'label': 'Stride length (m)', 'order': 1},
'step_width': {'label': 'Step width (cm)', 'order': 2},
'cadence': {'label': 'Cadence (steps/min)', 'order': 3},
'double_support_time': {'label': 'Double support (% gait cycle)', 'order': 4},
'step_length_symmetry': {'label': 'Step length symmetry (%, R/L)', 'order': 5},
# 'single_support_time': {'label': 'Single support time (% gait cycle)', 'order': 6},
}
scalar_names = list(scalars.keys())

# %% Process data.
# Init gait analysis and get gait events.
Expand All @@ -98,27 +95,26 @@ def handler(event, context):
# Compute scalars.
gait_scalars = gait[last_leg].compute_scalars(scalar_names)

gait_scalars['gait_speed']['decimal'] = 2
gait_scalars['step_width']['decimal'] = 1
gait_scalars['stride_length']['decimal'] = 2
gait_scalars['cadence']['decimal'] = 1
gait_scalars['double_support_time']['decimal'] = 1
gait_scalars['step_length_symmetry']['decimal'] = 1
scalars['gait_speed']['decimal'] = 2
scalars['step_width']['decimal'] = 1
scalars['stride_length']['decimal'] = 2
scalars['cadence']['decimal'] = 1
scalars['double_support_time']['decimal'] = 1
scalars['step_length_symmetry']['decimal'] = 1

# Change units
# Default = 1
for key in gait_scalars:
gait_scalars[key]['multiplier'] = 1

gait_scalars['step_width']['multiplier'] = 100 # cm
for scalar_name in scalar_names:
scalars[scalar_name]['multiplier'] = 1
scalars['step_width']['multiplier'] = 100 # cm

# %% Info about metrics.
gait_scalars['gait_speed']['info'] = "Gait speed is computed by dividing the displacement of the center of mass by the time it takes to move that distance. A speed larger than 1.12 m/s is considered good."
gait_scalars['step_width']['info'] = "Step width is computed as the average distance between the ankle joint centers in the mediolateral direction during 40-60% of the stance phase. A step width between 4.3 and 7.4 times the subject's height is considered good."
gait_scalars['stride_length']['info'] = "Stride length is computed as the distance between the calcaneus positions at the beginning and end of the gait cycle. A stride length larger than 0.45 times the subject's height is considered good."
gait_scalars['cadence']['info'] = "Cadence is computed as the number of gait cycles (left and right) per minute. A cadence larger than 100 is considered good."
gait_scalars['double_support_time']['info'] = "Double support time is computed as the duration when both feet are in contact with the ground. A double support time smaller than 35% of the gait cycle is considered good."
gait_scalars['step_length_symmetry']['info'] = "Step length symmetry is computed as the ratio between the right and left step lengths. A step length symmetry between 90 and 110 is considered good."
scalars['gait_speed']['info'] = "Gait speed is computed by dividing the displacement of the center of mass by the time it takes to move that distance. A speed larger than 1.12 m/s is considered good."
scalars['step_width']['info'] = "Step width is computed as the average distance between the ankle joint centers in the mediolateral direction during 40-60% of the stance phase. A step width between 4.3 and 7.4 times the subject's height is considered good."
scalars['stride_length']['info'] = "Stride length is computed as the distance between the calcaneus positions at the beginning and end of the gait cycle. A stride length larger than 0.45 times the subject's height is considered good."
scalars['cadence']['info'] = "Cadence is computed as the number of gait cycles (left and right) per minute. A cadence larger than 100 is considered good."
scalars['double_support_time']['info'] = "Double support time is computed as the duration when both feet are in contact with the ground. A double support time smaller than 35% of the gait cycle is considered good."
scalars['step_length_symmetry']['info'] = "Step length symmetry is computed as the ratio between the right and left step lengths. A step length symmetry between 90 and 110 is considered good."

# %% Thresholds.
metadataPath = os.path.join(sessionDir, 'sessionMetadata.yaml')
Expand All @@ -131,18 +127,21 @@ def handler(event, context):
# single_support_time_threshold = 65
double_support_time_threshold = 35
step_length_symmetry_threshold = [90,110]
thresholds = {
'gait_speed': {'value': gait_speed_threshold, 'decimal': 2},
'step_width': {'value': step_width_threshold, 'decimal': 1},
'stride_length': {'value': stride_length_threshold, 'decimal': 2},
'cadence': {'value': cadence_threshold, 'decimal': 1},
# 'single_support_time': single_support_time_threshold,
'double_support_time': {'value': double_support_time_threshold, 'decimal': 1},
'step_length_symmetry': {'value': step_length_symmetry_threshold, 'decimal': 1}}

scalars['gait_speed']['threshold'] = gait_speed_threshold
scalars['step_width']['threshold'] = step_width_threshold
scalars['stride_length']['threshold'] = stride_length_threshold
scalars['cadence']['threshold'] = cadence_threshold
scalars['double_support_time']['threshold'] = double_support_time_threshold
scalars['step_length_symmetry']['threshold'] = step_length_symmetry_threshold
# scalars['single_support_time']['threshold'] = single_support_time_threshold

# Whether below-threshold values should be colored in red (default) or green (reverse).
scalar_reverse_colors = ['double_support_time']
# Whether should be red-green-red plot
scalar_centered = ['step_length_symmetry','step_width']

scalars_to_exclude = []

# %% Return indices for visualizer and line curve plot.
# %% Create json for deployement.
Expand All @@ -157,29 +156,43 @@ def handler(event, context):
# Metrics
metrics_out = {}
for scalar_name in scalar_names:
if scalar_name in scalars_to_exclude:
continue
metrics_out[scalar_name] = {}
vertical_values = np.round(gait_scalars[scalar_name]['value'] *
gait_scalars[scalar_name]['multiplier'],
gait_scalars[scalar_name]['decimal'])
metrics_out[scalar_name]['label'] = scalar_labels[scalar_name]
scalars[scalar_name]['multiplier'],
scalars[scalar_name]['decimal'])
metrics_out[scalar_name]['label'] = scalars[scalar_name]['label']
metrics_out[scalar_name]['value'] = vertical_values
metrics_out[scalar_name]['info'] = gait_scalars[scalar_name]['info']
metrics_out[scalar_name]['info'] = scalars[scalar_name]['info']
if scalar_name in scalar_reverse_colors:
# Margin zone (orange) is 10% above threshold.
metrics_out[scalar_name]['colors'] = ["green", "yellow", "red"]
metrics_out[scalar_name]['min_limit'] = float(np.round(thresholds[scalar_name]['value'],thresholds[scalar_name]['decimal']))
metrics_out[scalar_name]['max_limit'] = float(np.round(1.10*thresholds[scalar_name]['value'],thresholds[scalar_name]['decimal']))
metrics_out[scalar_name]['min_limit'] = float(np.round(scalars[scalar_name]['threshold'],scalars[scalar_name]['decimal']))
metrics_out[scalar_name]['max_limit'] = float(np.round(1.10*scalars[scalar_name]['threshold'],scalars[scalar_name]['decimal']))
elif scalar_name in scalar_centered:
# Red, green, red
metrics_out[scalar_name]['colors'] = ["red", "green", "red"]
metrics_out[scalar_name]['min_limit'] = float(np.round(thresholds[scalar_name]['value'][0],thresholds[scalar_name]['decimal']))
metrics_out[scalar_name]['max_limit'] = float(np.round(thresholds[scalar_name]['value'][1],thresholds[scalar_name]['decimal']))
metrics_out[scalar_name]['min_limit'] = float(np.round(scalars[scalar_name]['threshold'][0],scalars[scalar_name]['decimal']))
metrics_out[scalar_name]['max_limit'] = float(np.round(scalars[scalar_name]['threshold'][1],scalars[scalar_name]['decimal']))
else:
# Margin zone (orange) is 10% below threshold.
metrics_out[scalar_name]['colors'] = ["red", "yellow", "green"]
metrics_out[scalar_name]['min_limit'] = float(np.round(0.90*thresholds[scalar_name]['value'],thresholds[scalar_name]['decimal']))
metrics_out[scalar_name]['max_limit'] = float(np.round(thresholds[scalar_name]['value'],thresholds[scalar_name]['decimal']))
metrics_out[scalar_name]['min_limit'] = float(np.round(0.90*scalars[scalar_name]['threshold'],scalars[scalar_name]['decimal']))
metrics_out[scalar_name]['max_limit'] = float(np.round(scalars[scalar_name]['threshold'],scalars[scalar_name]['decimal']))

metrics_out_ordered = metrics_out.copy()
for scalar_name in scalar_names:
if scalar_name in metrics_out_ordered:
# change the name of the key to str(scalars['order]) + scalar_name
# the name should be a two-character string, if the order is only one digit, add a 0 in front
order = scalars[scalar_name]['order']
if order < 10:
order = '0' + str(order)
else:
order = str(order)
metrics_out_ordered[order + '_' + scalar_name] = metrics_out_ordered.pop(scalar_name)

# Datasets
colNames = gait[last_leg].coordinateValues.columns
data = gait[last_leg].coordinateValues.to_numpy()
Expand All @@ -204,7 +217,7 @@ def handler(event, context):
# Create results dictionnary.
results = {
'indices': times,
'metrics': metrics_out,
'metrics': metrics_out_ordered,
'datasets': datasets,
'x_axis': 'time',
'y_axis': y_axes}
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