diff --git a/Usage.md b/Usage.md
index 663a062..bae8601 100644
--- a/Usage.md
+++ b/Usage.md
@@ -8,6 +8,8 @@ All configuration is done in a single JSON object and all model weights are dyna
 
 <br>
 
+## Detect
+
 There is only *ONE* method you need:
 
 ```js
@@ -25,12 +27,32 @@ or if you want to use promises
   })
 ```
 
+## Results Smoothing
+
+If you're processing video input, you may want to interpolate results for smoother output  
+After calling `detect` method, simply call `next` method when you need a new interpolated frame
+
+`Result` Parameter to `next` method is optional and if not provided, it will use last known result
+
+Example that performs single detection and then draws new interpolated result at 50 frames per second:
+
+```js
+  const result = await human.detect(image, config?)
+  setInterval(() => {
+    const interpolated = human.next(result);
+    human.draw.all(canvas, result);
+  }, 1000 / 50);
+```
+
+## Extra Properties and Methods
+
 `Human` library exposes several additional objects and methods:
 
 ```js
   human.version       // string containing version of human library
-  human.tf            // instance of tfjs used by human
   human.config        // access to configuration object, normally set as parameter to detect()
+  human.result        // access to last known result object, normally returned via call to detect()
+  human.performance   // access to last known performance counters
   human.state         // <string> describing current operation in progress
                       // progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
   human.sysinfo       // object containing current client platform and agent
@@ -39,8 +61,11 @@ or if you want to use promises
   human.image(image, config?) // runs image processing without detection and returns canvas
   human.warmup(config, image? // warms up human library for faster initial execution after loading
                               // if image is not provided, it will generate internal sample
+  human.tf            // instance of tfjs used by human
 ```
 
+## Face Recognition
+
 Additional functions used for face recognition:  
 For details, see [embedding documentation](https://github.com/vladmandic/human/wiki/Embedding)
 
@@ -59,6 +84,8 @@ Internal list of modules and objects used by current instance of `Human`:
   human.classes       // dynamically maintained list of classes that perform detection on each model
 ```
 
+## Draw Functions
+
 Additional helper functions inside `human.draw`:
 
 ```js
@@ -89,14 +116,16 @@ Style of drawing is configurable via `human.draw.options` object:
   drawBoxes: true,                      // draw boxes around detected faces
   roundRect: 8,                         // should boxes have round corners and rounding value
   drawPolygons: true,                   // draw polygons such as body and face mesh
-  fillPolygons: true,                   // fill polygons in face mesh
+  fillPolygons: false,                  // fill polygons in face mesh
   useDepth: true,                       // use z-axis value when available to determine color shade
-  useCurves: true,                      // draw polygons and boxes using smooth curves instead of lines
-  bufferedOutput: false,                // experimental: buffer and interpolate results between frames
+  useCurves: false,                     // draw polygons and boxes using smooth curves instead of lines
+  bufferedOutput: true,                 // experimental: buffer and interpolate results between frames
 ```
 
 <br>
 
+## Example Video Processing
+
 Example simple app that uses Human to process video input and  
 draw output on screen using internal draw helper functions
 
@@ -132,6 +161,8 @@ detectVideo();
 
 <br>
 
+## Example for NodeJS
+
 Note that when using `Human` library in `NodeJS`, you must load and parse the image *before* you pass it for detection and dispose it afterwards  
 Input format is `Tensor4D[1, width, height, 3]` of type `float32`