diff --git a/.github/workflows/gait_analysis.yml b/.github/workflows/gait_analysis.yml
new file mode 100644
index 0000000..5f19586
--- /dev/null
+++ b/.github/workflows/gait_analysis.yml
@@ -0,0 +1,74 @@
+# This workflow will build and push a new container image to Amazon ECR,
+# and then will deploy a new task definition to Amazon ECS, on every push
+# to the master branch.
+#
+# To use this workflow, you will need to complete the following set-up steps:
+#
+# 1. Create an ECR repository to store your images.
+#    For example: `aws ecr create-repository --repository-name my-ecr-repo --region us-east-2`.
+#    Replace the value of `ECR_REPOSITORY` in the workflow below with your repository's name.
+#    Replace the value of `aws-region` in the workflow below with your repository's region.
+#
+# 2. Create an ECS task definition, an ECS cluster, and an ECS service.
+#    For example, follow the Getting Started guide on the ECS console:
+#      https://us-east-2.console.aws.amazon.com/ecs/home?region=us-east-2#/firstRun
+#    Replace the values for `service` and `cluster` in the workflow below with your service and cluster names.
+#
+# 3. Store your ECS task definition as a JSON file in your repository.
+#    The format should follow the output of `aws ecs register-task-definition --generate-cli-skeleton`.
+#    Replace the value of `task-definition` in the workflow below with your JSON file's name.
+#    Replace the value of `container-name` in the workflow below with the name of the container
+#    in the `containerDefinitions` section of the task definition.
+#
+# 4. Store an IAM user access key in GitHub Actions secrets named `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`.
+#    See the documentation for each action used below for the recommended IAM policies for this IAM user,
+#    and best practices on handling the access key credentials.
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'gait_analysis/**'
+  workflow_dispatch:
+
+name: PROD Analysis "gait analysis" build & deployment
+
+jobs:
+  deploy:
+    name: Deploy
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v1
+
+    - name: Configure AWS credentials
+      uses: aws-actions/configure-aws-credentials@v1
+      with:
+        aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+        aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+        aws-region: us-west-2
+
+    - name: Login to Amazon ECR
+      id: login-ecr
+      uses: aws-actions/amazon-ecr-login@v1
+
+    - name: Build, tag, and push image to Amazon ECR
+      id: build-image
+      env:
+        IMAGE_TAG: latest # ${{ github.sha }}
+      run: |
+        # Build a docker container and
+        # push it to ECR so that it can
+        # be deployed to ECS.
+        cd gait_analysis
+        docker build -f Dockerfile -t 660440363484.dkr.ecr.us-west-2.amazonaws.com/opencap-analysis/gait_analysis:$IMAGE_TAG .
+        docker push 660440363484.dkr.ecr.us-west-2.amazonaws.com/opencap-analysis/gait_analysis:$IMAGE_TAG
+        echo "::set-output name=image::660440363484.dkr.ecr.us-west-2.amazonaws.com/opencap-analysis/gait_analysis:$IMAGE_TAG"
+
+    - name: Force deployment
+      env:
+        IMAGE_TAG: latest # ${{ github.sha }}
+      run: |
+        aws lambda update-function-code --function-name gait-analysis --image-uri 660440363484.dkr.ecr.us-west-2.amazonaws.com/opencap-analysis/gait_analysis:$IMAGE_TAG | jq 'if .Environment.Variables.API_TOKEN? then .Environment.Variables.API_TOKEN = "REDACTED" else . end'
diff --git a/.github/workflows/max_centerofmass_vpos.yml b/.github/workflows/max_centerofmass_vpos.yml
index 4454b14..2eb7773 100644
--- a/.github/workflows/max_centerofmass_vpos.yml
+++ b/.github/workflows/max_centerofmass_vpos.yml
@@ -27,7 +27,7 @@
 on:
   push:
     branches:
-      - dev
+      - main
     paths:
       - 'max_centerofmass_vpos/**'
   workflow_dispatch:
diff --git a/gait_analysis/function/handler.py b/gait_analysis/function/handler.py
index 8fcd9a5..f2cf37e 100644
--- a/gait_analysis/function/handler.py
+++ b/gait_analysis/function/handler.py
@@ -77,7 +77,7 @@ def handler(event, context):
                  'step_width': 'Step width (m)',
                  'cadence': 'Cadence (steps/min)',
                 #  'single_support_time': 'Single support time (% gait cycle)', 
-                 'double_support_time': 'Double support time (% gait cycle)',
+                 'double_support_time': 'Double support (% gait cycle)',
                  'step_length_symmetry': 'Step length symmetry (%, R/L)'}
 
     # %% Process data.
@@ -98,25 +98,32 @@ 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'] = 2
+    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
+
     # %% Thresholds.
     metadataPath = os.path.join(sessionDir, 'sessionMetadata.yaml')
     metadata = import_metadata(metadataPath)
     subject_height = metadata['height_m']
     gait_speed_threshold = 67/60
     step_width_threshold = 0.14
-    stride_length_threshold = subject_height * .57
+    stride_length_threshold = subject_height * .45
     cadence_threshold = 100
     # single_support_time_threshold = 65
     double_support_time_threshold = 35
     step_length_symmetry_threshold = [90,110]
     thresholds = {
-              'gait_speed': gait_speed_threshold,
-              'step_width': step_width_threshold,
-              'stride_length': stride_length_threshold,
-              'cadence': cadence_threshold,
+              'gait_speed': {'value': gait_speed_threshold, 'decimal': 2},
+              'step_width': {'value': step_width_threshold, 'decimal': 2},
+              'stride_length': {'value': stride_length_threshold, 'decimal': 2},
+              'cadence': {'value': cadence_threshold, 'decimal': 1},
             #   'single_support_time': single_support_time_threshold,
-              'double_support_time': double_support_time_threshold,
-              'step_length_symmetry': step_length_symmetry_threshold}
+              'double_support_time': {'value': double_support_time_threshold, 'decimal': 1},
+              'step_length_symmetry': {'value': step_length_symmetry_threshold, 'decimal': 1}}
     # Whether below-threshold values should be colored in red (default) or green (reverse).
     scalar_reverse_colors = ['step_width', 'double_support_time']
     # Whether should be red-green-red plot
@@ -136,24 +143,24 @@ def handler(event, context):
     metrics_out = {}
     for scalar_name in scalar_names:
         metrics_out[scalar_name] = {}
-        vertical_values = np.round(gait_scalars[scalar_name]['value'], 2)
+        vertical_values = np.round(gait_scalars[scalar_name]['value'], gait_scalars[scalar_name]['decimal'])
         metrics_out[scalar_name]['label'] = scalar_labels[scalar_name]
         metrics_out[scalar_name]['value'] = vertical_values
         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],2))
-            metrics_out[scalar_name]['max_limit'] = float(np.round(1.10*thresholds[scalar_name],2))
+            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']))
         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][0],2))        
-            metrics_out[scalar_name]['max_limit'] = float(np.round(thresholds[scalar_name][1],2)) 
+            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'])) 
         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],2))
-            metrics_out[scalar_name]['max_limit'] = float(np.round(thresholds[scalar_name],2))
+            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']))
             
     # Datasets
     colNames = gait[last_leg].coordinateValues.columns
@@ -181,4 +188,4 @@ def handler(event, context):
         'statusCode': 200,
         'headers': {'Content-Type': 'application/json'},
         'body': results
-    }
\ No newline at end of file
+    }