Description updates

accelerometer-and-activity-projects/arduin-row-tinyml-rowing-machine-coach-arduino-nicla-sense.md

@@ -1,7 +1,7 @@
 ---
 description: >-
   Use an Arduino Nicla Sense ME and Edge Impulse model to determine your rowing
-  cadence and provide feedback via the IoT Remote app
+  cadence and provide feedback via the IoT Remote app.
 ---
 
 # Arduin-Row, a TinyML Rowing Machine Coach

accelerometer-and-activity-projects/bluetooth-fall-detection-arduino-nano-33.md

@@ -1,7 +1,7 @@
 ---
 description: >-
-  A client/server device to detect and analyze worker falls with Machine
-  Learning
+  A client/server device to detect and analyze worker falls with machine
+  learning and an Arduino Nano 33 BLE Sense.
 ---
 
 # Bluetooth Fall Detection
@@ -26,7 +26,7 @@ Each worker has a small TinyML device in charge of detecting falls via the onboa
 
 ## Client Devices
 
-The electronics part of the client build is easy: just a battery, a TP4056 and the Arduino Nano BLE 33 Sense. The board has an onboard accelerometer, onboard RGB led, and enough processing power to run an Edge Impulse library for inferencing locally.
+The electronics part of the client build is easy: just a battery, a TP4056 and the Arduino Nano 33 BLE Sense. The board has an onboard accelerometer, onboard RGB led, and enough processing power to run an Edge Impulse library for inferencing locally.
 
 ![](../.gitbook/assets/bt-fall-detection/diagram.jpg)
 

air-quality-and-environmental-projects/harmful-gases-detection-arduino-nano-33.md

@@ -1,5 +1,5 @@
 ---
-description: Detect harmful gases with Machine Learning and an Arduino Nano 33 BLE
+description: Detect harmful gases with machine learning and an Arduino Nano 33 BLE plus gas sensor.
 ---
 
 # Bhopal 84, Detect Harmful Gases

audio-projects/ai-patient-assistance-arduino-nano-33.md

@@ -1,7 +1,7 @@
 ---
 description: >-
-  Keyword Recognition and Notification for AI Patient Assistance With Edge
-  Impulse and the Arduino Nano 33 BLE Sense
+  Keyword recognition and notification for AI patient assistance With Edge
+  Impulse and the Arduino Nano 33 BLE Sense.
 ---
 
 # AI-Powered Patient Assistance

audio-projects/collect-data-keyword-spotting-raspberry-pi-pico.md

@@ -1,5 +1,5 @@
 ---
-description: Collect audio data for your machine learning model on Raspberry Pi Pico.
+description: Collect audio data for your machine learning model on a Raspberry Pi Pico.
 ---
 
 # Collect Data for Keyword Spotting with Raspberry Pi Pico and Edge Impulse

audio-projects/gunshot-audio-classification-arduino.md

@@ -1,7 +1,7 @@
 ---
 description: >-
   A proof-of-concept machine learning project for first responders, to detect
-  the sound of a gunshot
+  the sound of a gunshot.
 ---
 
 # Gunshot Audio Classification

audio-projects/voice-activated-led-controller-raspberry-pi-pico.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Build a voice-activated LED light strip controller on the cheap with Raspberry
+  Build a voice-activated LED light strip controller on the cheap with a Raspberry
   Pi Pico.
 ---
 

image-projects/bean-leaf-disease-classification-sony-spresense.md

@@ -1,5 +1,5 @@
 ---
-description: A TinyML Approach for Bean Leaf Disease Classification using a Sony Spresense.
+description: A TinyML approach for bean leaf disease classification using a Sony Spresense.
 ---
 
 # Bean Leaf Disease Classification - Sony Spresense

image-projects/esd-protection-using-computer-vision-seeed-recomputer.md

@@ -1,10 +1,10 @@
 ---
 description: >-
   Use a Seeed Studio ReComputer and FOMO to detect if an operator has ESD risk
-  or protection when handling electronic components
+  or protection when handling electronic components.
 ---
 
-# ESD Protection using Computer Vision
+# ESD Protection Using Computer Vision
 
 Created By: Justin Lutz
 

image-projects/label-defect-detection-raspberry-pi.md

@@ -1,8 +1,8 @@
 ---
-description: Automatic FOMO-based objects counting using computer vision and a web app.
+description: Inspect printed labels for defects using computer vision and a Raspberry Pi.
 ---
 
-# Automated Label Inspection With FOMO
+# Automated Label Defect Detection With FOMO
 
 Created By: Shebin Jose Jacob
 

image-projects/object-counting-for-smart-industries-raspberry-pi.md

@@ -1,5 +1,5 @@
 ---
-description: Automatic FOMO-based objects counting using computer vision and a web app.
+description: Automatic FOMO-based object counting using computer vision and a web app.
 ---
 
 # Adaptable Vision Counters for Smart Industries

novel-sensor-projects/food-irradiation-dose-detection-dfrobot-beetle-esp32c3.md

@@ -1,7 +1,7 @@
 ---
 description: >-
-  Building a Food Irradiation detection device using a DFRobot ESP32, Geiger
-  Counter, and Visible Light sensor.
+  Building a food irradiation detection device using a DFRobot ESP32, Geiger
+  Counter, and visible light sensor.
 ---
 
 # Food Irradiation Dose Detection

predictive-maintenance-and-fault-classification/refrigerator-predictive-maintenance-arduino-nano-33.md

@@ -1,5 +1,5 @@
 ---
-description: A machine learning model to detect a fault in a refrigerator immediately.
+description: A machine learning model to detect a fault in a refrigerator by monitoring temperature and humidity.
 ---
 
 # Refrigerator Predictive Maintenance
@@ -8,9 +8,7 @@ Created By: Swapnil Verma
 
 Public Project Link: [https://studio.edgeimpulse.com/public/115503/latest](https://studio.edgeimpulse.com/public/115503/latest)
 
-## GitHub Repo
-
-[https://github.com/sw4p/Refrigerator\_Predictive\_Maintenance](https://github.com/sw4p/Refrigerator\_Predictive\_Maintenance)
+GitHub Repo: [https://github.com/sw4p/Refrigerator\_Predictive\_Maintenance](https://github.com/sw4p/Refrigerator\_Predictive\_Maintenance)
 
 ![](../.gitbook/assets/refrigerator-predictive-maintenance/intro.jpg)