Merge pull request #445 from dtischler/main

hr-hrv block fixes

novel-sensor-projects/ecg-hrv-block-arduino-portenta.md

@@ -62,9 +62,9 @@ The ECG signal can also be combined with data from an accelerometer for enhanced
 
 The pads should be connected as follow:
 
-Yellow pad to the left
-Red pad to the right
-Green pad below the red pad
+- Yellow pad to the left.
+- Red pad to the right.
+- Green pad below the red pad.
 
 > Note: Remember to disconnect the AC from the laptop before sampling. 
 
@@ -80,7 +80,7 @@ Close the Serial Monitor and run `edge-impulse-data-forwarder`.
 
 Select the Edge Impulse project and check that the frequency shows `[SER] Detected data frequency: 50Hz`.
 
-Go to https://studio.edgeimpulse.com/studio/<Your-Project-ID>/acquisition/training
+Go to [https://studio.edgeimpulse.com/studio/Your-Project-ID/acquisition/training](https://studio.edgeimpulse.com/studio/Your-Project-ID/acquisition/training)
 
 Select **Length 120.000 ms** and take around 10 to 20 samples for each category to classify. For example, regular working versus stressed. Set aside 10% of the samples for testing. 
 
@@ -102,8 +102,6 @@ Frequency-domain features are: Raw VLF Energy, Raw LF Energy, Raw HF Energy, Raw
 
 I have used ECG, filter preset 1, window size 40 and no HRV features.
 
-![](../.gitbook/assets/ecg-hrv-block-arduino-portenta/wearable-1.jpg)
-
 ## Model Training
 
 The training could require some parameters to be modified from the defaults. I have found the following parameters to work well for my dataset, with a 89.3% accuracy. Training cycles **40**, learning rate **0.005**, bacth size **30** and no auto weight. 
@@ -130,6 +128,8 @@ Now you will be able to use the model library with your own code. A sample ECG m
 
 > Note: If Arduino Portenta shows `Exit status 74`, double click "Reset", and select the correct port.
 
+![](../.gitbook/assets/ecg-hrv-block-arduino-portenta/wearable-1.jpg)
+
 ## Final Notes
 
 Thanks to machine learning, monitoring ECG signals no longer requires transmitting data to a remote computer for expert analysis. Instead, subtle health conditions can be detected by small, offline, wearable devices equipped with machine learning capabilities. These devices can identify over-stressed workers who may be unable to perform their tasks effectively, thus preventing serious harm or consequences.