diff --git a/demo/node-multiprocess-worker.js b/demo/node-multiprocess-worker.js
index eed50346..1ee127be 100644
--- a/demo/node-multiprocess-worker.js
+++ b/demo/node-multiprocess-worker.js
@@ -1,11 +1,10 @@
 // @ts-nocheck
 
 const fs = require('fs');
-// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
 const log = require('@vladmandic/pilogger');
 
 // workers actual import tfjs and faceapi modules
-// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
+// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node');
 const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
 
@@ -38,7 +37,7 @@ const myConfig = {
 // you can add any pre-proocessing here such as resizing, etc.
 async function image(img) {
   const buffer = fs.readFileSync(img);
-  const tensor = tf.tidy(() => tf.node.decodeImage(buffer).toFloat().expandDims());
+  const tensor = human.tf.tidy(() => human.tf.node.decodeImage(buffer).toFloat().expandDims());
   return tensor;
 }
 
@@ -65,10 +64,10 @@ async function main() {
     log.data('Worker received message:', process.pid, msg); // generic log
   });
 
-  // wait until tf is ready
-  await tf.ready();
   // create instance of human
   human = new Human(myConfig);
+  // wait until tf is ready
+  await human.tf.ready();
   // pre-load models
   log.state('Worker: PID:', process.pid, `TensorFlow/JS ${human.tf.version_core} Human ${human.version} Backend: ${human.tf.getBackend()}`);
   await human.load();
diff --git a/demo/node-webcam.js b/demo/node-webcam.js
new file mode 100644
index 00000000..14bbf718
--- /dev/null
+++ b/demo/node-webcam.js
@@ -0,0 +1,60 @@
+const util = require('util');
+const log = require('@vladmandic/pilogger');
+const nodeWebCam = require('node-webcam');
+// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
+const tf = require('@tensorflow/tfjs-node');
+// load specific version of Human library that matches TensorFlow mode
+const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
+
+// options for node-webcam
+const optionsCamera = {
+  callbackReturn: 'buffer', // this means whatever `fswebcam` writes to disk, no additional processing so it's fastest
+  saveShots: false, // don't save processed frame to disk, note that temp file is still created by fswebcam thus recommendation for tmpfs
+};
+
+// options for human
+const optionsHuman = {
+  backend: 'tensorflow',
+  modelBasePath: 'file://node_modules/@vladmandic/human/models/',
+};
+
+const camera = nodeWebCam.create(optionsCamera);
+const capture = util.promisify(camera.capture);
+const human = new Human(optionsHuman);
+const results = [];
+
+const buffer2tensor = human.tf.tidy((buffer) => {
+  const decode = human.tf.node.decodeImage(buffer, 3);
+  let expand;
+  if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
+    const channels = human.tf.split(decode, 4, 2); // tf.split(tensor, 4, 2); // split rgba to channels
+    const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb and ignore alpha
+    expand = human.tf.reshape(rgb, [1, decode.shape[0], decode.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
+  } else {
+    expand = human.tf.expandDims(decode, 0); // inpur ia rgb so use as-is
+  }
+  const cast = human.tf.cast(expand, 'float32');
+  return cast;
+});
+
+async function process() {
+  // trigger next frame every 5 sec
+  // triggered here before actual capture and detection since we assume it will complete in less than 5sec
+  // so it's as close as possible to real 5sec and not 5sec + detection time
+  // if there is a chance of race scenario where detection takes longer than loop trigger, then trigger should be at the end of the function instead
+  setTimeout(() => process(), 5000);
+
+  const buffer = await capture(); // gets the (default) jpeg data from from webcam
+  const tensor = buffer2tensor(buffer); // create tensor from image buffer
+  const res = await human.detect(tensor); // run detection
+
+  // do whatever here with the res
+  // or just append it to results array that will contain all processed results over time
+  results.push(res);
+
+  // alternatively to triggering every 5sec sec, simply trigger next frame as fast as possible
+  // setImmediate(() => process());
+}
+
+log.header();
+process();
diff --git a/demo/node.js b/demo/node.js
index 406fa19e..5c9c01de 100644
--- a/demo/node.js
+++ b/demo/node.js
@@ -4,6 +4,7 @@ const process = require('process');
 const fetch = require('node-fetch').default;
 
 // for NodeJS, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
+// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu');
 
 // load specific version of Human library that matches TensorFlow mode
@@ -38,10 +39,10 @@ const myConfig = {
 };
 
 async function init() {
-  // wait until tf is ready
-  await tf.ready();
   // create instance of human
   human = new Human(myConfig);
+  // wait until tf is ready
+  await human.tf.ready();
   // pre-load models
   log.info('Human:', human.version);
   log.info('Active Configuration', human.config);
@@ -66,7 +67,7 @@ async function detect(input) {
   // decode image using tfjs-node so we don't need external depenencies
   // can also be done using canvas.js or some other 3rd party image library
   if (!buffer) return {};
-  const tensor = tf.tidy(() => {
+  const tensor = human.tf.tidy(() => {
     const decode = human.tf.node.decodeImage(buffer, 3);
     let expand;
     if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
@@ -92,7 +93,7 @@ async function detect(input) {
   }
 
   // dispose image tensor as we no longer need it
-  tf.dispose(tensor);
+  human.tf.dispose(tensor);
 
   // print data to console
   log.data('Results:');