LogoLogo
HomeDocsAPI & SDKsProjectsForumStudio
  • Welcome
    • Featured Machine Learning Projects
      • Getting Started with the Edge Impulse Nvidia TAO Pipeline - Renesas EK-RA8D1
      • Smart City Traffic Analysis - NVIDIA TAO + Jetson Orin Nano
      • ROS 2 Pick and Place System - Arduino Braccio++ Robotic Arm and Luxonis OAK-D
      • Optimize a cloud-based Visual Anomaly Detection Model for Edge Deployments
      • Rooftop Ice Detection with Things Network Visualization - Nvidia Omniverse Replicator
      • Surgery Inventory Object Detection - Synthetic Data - Nvidia Omniverse Replicator
      • NVIDIA Omniverse - Synthetic Data Generation For Edge Impulse Projects
      • Community Guide – Using Edge Impulse with Nvidia DeepStream
      • Computer Vision Object Counting - Avnet RZBoard V2L
      • Gesture Appliances Control with Pose Detection - BrainChip AKD1000
      • Counting for Inspection and Quality Control - Nvidia Jetson Nano (TensorRT)
      • High-resolution, High-speed Object Counting - Nvidia Jetson Nano (TensorRT)
    • Prototype and Concept Projects
      • Renesas CK-RA6M5 Cloud Kit - Getting Started with Machine Learning
      • TI CC1352P Launchpad - Getting Started with Machine Learning
      • OpenMV Cam RT1062 - Getting Started with Machine Learning
      • Getting Started with Edge Impulse Experiments
  • Computer Vision Projects
    • Workplace Organizer - Nvidia Jetson Nano
    • Recyclable Materials Sorter - Nvidia Jetson Nano
    • Analog Meter Reading - Arduino Nicla Vision
    • Creating Synthetic Data with Nvidia Omniverse Replicator
    • SonicSight AR - Sound Classification with Feedback on an Augmented Reality Display
    • Traffic Monitoring - Brainchip Akida
    • Multi-camera Video Stream Inference - Brainchip Akida
    • Industrial Inspection Line - Brainchip Akida
    • X-Ray Classification and Analysis - Brainchip Akida
    • Inventory Stock Tracker - FOMO - BrainChip Akida
    • Container Counting - Arduino Nicla Vision
    • Smart Smoke Alarm - Arduino Nano 33
    • Shield Bot Autonomous Security Robot
    • Cyclist Blind Spot Detection - Himax WE-I Plus
    • IV Drip Fluid-Level Monitoring - Arduino Portenta H7
    • Worker PPE Safety Monitoring - Nvidia Jetson Nano
    • Delivered Package Detection - ESP-EYE
    • Bean Leaf Disease Classification - Sony Spresense
    • Oil Tank Measurement Using Computer Vision - Sony Spresense
    • Object Counting for Smart Industries - Raspberry Pi
    • Smart Cashier with FOMO - Raspberry Pi
    • PCB Defect Detection with Computer Vision - Raspberry Pi
    • Bicycle Counting - Sony Spresense
    • Counting Eggs with Computer Vision - OpenMV Cam H7
    • Elevator Passenger Counting - Arduino Nicla Vision
    • ESD Protection using Computer Vision - Seeed ReComputer
    • Solar Panel Defect Detection - Arduino Portenta H7
    • Label Defect Detection - Raspberry Pi
    • Dials and Knob Monitoring with Computer Vision - Raspberry Pi
    • Digital Character Recognition on Electric Meter System - OpenMV Cam H7
    • Corrosion Detection with Computer Vision - Seeed reTerminal
    • Inventory Management with Computer Vision - Raspberry Pi
    • Monitoring Retail Checkout Lines with Computer Vision - Renesas RZ/V2L
    • Counting Retail Inventory with Computer Vision - Renesas RZ/V2L
    • Pose Detection - Renesas RZ/V2L
    • Product Quality Inspection - Renesas RZ/V2L
    • Smart Grocery Cart Using Computer Vision - OpenMV Cam H7
    • Driver Drowsiness Detection With FOMO - Arduino Nicla Vision
    • Gastroscopic Image Processing - OpenMV Cam H7
    • Pharmaceutical Pill Quality Control and Defect Detection
    • Deter Shoplifting with Computer Vision - Texas Instruments TDA4VM
    • Smart Factory Prototype - Texas Instruments TDA4VM
    • Correct Posture Detection and Enforcement - Texas Instruments TDA4VM
    • Visual Anomaly Detection with FOMO-AD - Texas Instruments TDA4VM
    • Surface Crack Detection and Localization - Texas Instruments TDA4VM
    • Surface Crack Detection - Seeed reTerminal
    • Retail Image Classification - Nvidia Jetson Nano
    • SiLabs xG24 Plus Arducam - Sorting Objects with Computer Vision and Robotics - Part 1
    • SiLabs xG24 Plus Arducam - Sorting Objects with Computer Vision and Robotics - Part 2
    • Object Detection and Visualization - Seeed Grove Vision AI Module
    • Bike Rearview Radar - Raspberry Pi
    • Build a Self-Driving RC Vehicle - Arduino Portenta H7 and Computer Vision
    • "Bring Your Own Model" Image Classifier for Wound Identification
    • Acute Lymphoblastic Leukemia Classifier - Nvidia Jetson Nano
    • Hardhat Detection in Industrial Settings - Alif Ensemble E7
    • Motorcycle Helmet Identification and Traffic Light Control - Texas Instruments AM62A
    • Import a Pretrained Model with "Bring Your Own Model" - Texas Instruments AM62A
    • Product Inspection with Visual Anomaly Detection - FOMO-AD - Sony Spresense
    • Visual Anomaly Detection in Fabric using FOMO-AD - Raspberry Pi 5
    • Car Detection and Tracking System for Toll Plazas - Raspberry Pi AI Kit
    • Visual Anomaly Detection - Seeed Grove Vision AI Module V2
    • Object Counting with FOMO - OpenMV Cam RT1062
    • Visitor Heatmap with FOMO Object Detection - Jetson Orin Nano
    • Vehicle Security Camera - Arduino Portenta H7
  • Audio Projects
    • Occupancy Sensing - SiLabs xG24
    • Smart Appliance Control Using Voice Commands - Nordic Thingy:53
    • Glass Window Break Detection - Nordic Thingy:53
    • Illegal Logging Detection - Nordic Thingy:53
    • Illegal Logging Detection - Syntiant TinyML
    • Wearable Cough Sensor and Monitoring - Arduino Nano 33 BLE Sense
    • Collect Data for Keyword Spotting - Raspberry Pi Pico
    • Voice-Activated LED Strip - Raspberry Pi Pico
    • Snoring Detection on a Smart Phone
    • Gunshot Audio Classification - Arduino Nano 33 + Portenta H7
    • AI-Powered Patient Assistance - Arduino Nano 33 BLE Sense
    • Acoustic Pipe Leakage Detection - Arduino Portenta H7
    • Location Identification using Sound - Syntiant TinyML
    • Environmental Noise Classification - Nordic Thingy:53
    • Running Faucet Detection - Seeed XIAO Sense + Blues Cellular
    • Vandalism Detection via Audio Classification - Arduino Nano 33 BLE Sense
    • Predictive Maintenance Using Audio Classification - Arduino Nano 33 BLE Sense
    • Porting an Audio Project from the SiLabs Thunderboard Sense 2 to xG24
    • Environmental Audio Monitoring Wearable - Syntiant TinyML - Part 1
    • Environmental Audio Monitoring Wearable - Syntiant TinyML - Part 2
    • Keyword Spotting - Nordic Thingy:53
    • Detecting Worker Accidents with Audio Classification - Syntiant TinyML
    • Snoring Detection with Syntiant NDP120 Neural Decision Processor - Arduino Nicla Voice
    • Recognize Voice Commands with the Particle Photon 2
    • Voice Controlled Power Plug with Syntiant NDP120 (Nicla Voice)
    • Determining Compressor State with Audio Classification - Avnet RaSynBoard
    • Developing a Voice-Activated Product with Edge Impulse's Synthetic Data Pipeline
    • Enhancing Worker Safety using Synthetic Audio to Create a Dog Bark Classifier
  • Predictive Maintenance and Defect Detection Projects
    • Predictive Maintenance - Nordic Thingy:91
    • Brushless DC Motor Anomaly Detection
    • Industrial Compressor Predictive Maintenance - Nordic Thingy:53
    • Anticipate Power Outages with Machine Learning - Arduino Nano 33 BLE Sense
    • Faulty Lithium-Ion Cell Identification in Battery Packs - Seeed Wio Terminal
    • Weight Scale Predictive Maintenance - Arduino Nano 33 BLE Sense
    • Fluid Leak Detection With a Flowmeter and AI - Seeed Wio Terminal
    • Pipeline Clog Detection with a Flowmeter and AI - Seeed Wio Terminal
    • Refrigerator Predictive Maintenance - Arduino Nano 33 BLE Sense
    • Motor Pump Predictive Maintenance - Infineon PSoC 6 WiFi-BT Pioneer Kit + CN0549
    • BrickML Demo Project - 3D Printer Anomaly Detection
    • Condition Monitoring - Syntiant TinyML Board
    • Predictive Maintenance - Commercial Printer - Sony Spresense + CommonSense
    • Vibration Classification with BrainChip's Akida
    • AI-driven Audio and Thermal HVAC Monitoring - SeeedStudio XIAO ESP32
  • Accelerometer and Activity Projects
    • Arduino x K-Way - Outdoor Activity Tracker
    • Arduino x K-Way - Gesture Recognition for Hiking
    • Arduino x K-Way - TinyML Fall Detection
    • Posture Detection for Worker Safety - SiLabs Thunderboard Sense 2
    • Hand Gesture Recognition - OpenMV Cam H7
    • Arduin-Row, a TinyML Rowing Machine Coach - Arduino Nicla Sense ME
    • Fall Detection using a Transformer Model – Arduino Giga R1 WiFi
    • Bluetooth Fall Detection - Arduino Nano 33 BLE Sense
    • Monitor Packages During Transit with AI - Arduino Nano 33 BLE Sense
    • Smart Baby Swing - Arduino Portenta H7
    • Warehouse Shipment Monitoring - SiLabs Thunderboard Sense 2
    • Gesture Recognition - Bangle.js Smartwatch
    • Gesture Recognition for Patient Communication - SiLabs Thunderboard Sense 2
    • Hospital Bed Occupancy Detection - Arduino Nano 33 BLE Sense
    • Porting a Posture Detection Project from the SiLabs Thunderboard Sense 2 to xG24
    • Porting a Gesture Recognition Project from the SiLabs Thunderboard Sense 2 to xG24
    • Continuous Gait Monitor (Anomaly Detection) - Nordic Thingy:53
    • Classifying Exercise Activities on a BangleJS Smartwatch
  • Air Quality and Environmental Projects
    • Arduino x K-Way - Environmental Asthma Risk Assessment
    • Gas Detection in the Oil and Gas Industry - Nordic Thingy:91
    • Smart HVAC System with a Sony Spresense
    • Smart HVAC System with an Arduino Nicla Vision
    • Indoor CO2 Level Estimation - Arduino Portenta H7
    • Harmful Gases Detection - Arduino Nano 33 BLE Sense
    • Fire Detection Using Sensor Fusion and TinyML - Arduino Nano 33 BLE Sense
    • AI-Assisted Monitoring of Dairy Manufacturing Conditions - Seeed XIAO ESP32C3
    • AI-Assisted Air Quality Monitoring - DFRobot Firebeetle ESP32
    • Air Quality Monitoring with Sipeed Longan Nano - RISC-V Gigadevice
    • Methane Monitoring in Mines - Silabs xG24 Dev Kit
    • Smart Building Ventilation with Environmental Sensor Fusion
    • Sensor Data Fusion with Spresense and CommonSense
    • Water Pollution Detection - Arduino Nano ESP32 + Ultrasonic Scan
    • Fire Detection Using Sensor Fusion - Arduino Nano 33 BLE Sense
  • Novel Sensor Projects
    • 8x8 ToF Gesture Classification - Arduino RP2040 Connect
    • Food Irradiation Dose Detection - DFRobot Beetle ESP32C3
    • Applying EEG Data to Machine Learning, Part 1
    • Applying EEG Data to Machine Learning, Part 2
    • Applying EEG Data to Machine Learning, Part 3
    • Liquid Classification with TinyML - Seeed Wio Terminal + TDS Sensor
    • AI-Assisted Pipeline Diagnostics and Inspection with mmWave Radar
    • Soil Quality Detection Using AI and LoRaWAN - Seeed Sensecap A1101
    • Smart Diaper Prototype - Arduino Nicla Sense ME
    • DIY Smart Glove with Flex Sensors
    • EdgeML Energy Monitoring - Particle Photon 2
    • Wearable for Monitoring Worker Stress using HR/HRV DSP Block - Arduino Portenta
  • Software Integration Demos
    • Azure Machine Learning with Kubernetes Compute and Edge Impulse
    • ROS2 + Edge Impulse, Part 1: Pub/Sub Node in Python
    • ROS2 + Edge Impulse, Part 2: MicroROS
    • Using Hugging Face Datasets in Edge Impulse
    • Using Hugging Face Image Classification Datasets with Edge Impulse
    • Edge Impulse API Usage Sample Application - Jetson Nano Trainer
    • MLOps with Edge Impulse and Azure IoT Edge
    • A Federated Approach to Train and Deploy Machine Learning Models
    • DIY Model Weight Update for Continuous AI Deployments
    • Automate the CI/CD Pipeline of your Models with Edge Impulse and GitHub Actions
    • Deploying Edge Impulse Models on ZEDEDA Cloud Devices
Powered by GitBook
On this page
  • Project Demo
  • Intro
  • Hardware requirements
  • Software requirements
  • Hardware Setup
  • Software Setup
  • Training the weather prediction model
  • Training the activity tracking model
  • Data gathering for ML
  • Conclusion

Was this helpful?

Edit on GitHub
Export as PDF
  1. Accelerometer and Activity Projects

Arduino x K-Way - Outdoor Activity Tracker

A wearable Nicla Sense ME that can measure both the environment, and your outdoor activities using machine learning.

PreviousAccelerometer and Activity ProjectsNextArduino x K-Way - Gesture Recognition for Hiking

Last updated 1 year ago

Was this helpful?

Created By:

Public Project Links:

GitHub Repository:

Project Demo

Intro

Hiking is a great way to get outdoors and enjoy some fresh air. However, keeping track of your progress can be challenging, and that's where an outdoor activity tracker comes in handy. A hiking wearable device provides some valuable functions that can make your hike more enjoyable and safe. It can track things like how many steps you've taken, your walking speed, and even the weather conditions.

We’ll present the following use cases for the Arduino Nicla Sense ME board:

  • Weather prediction - The wearable will be able to predict weather changes using the onboard pressure sensor and AI. By monitoring the atmospheric pressure, the tracker can notify you when a storm is approaching or when conditions are ripe for favorable weather. This information can be helpful in deciding whether to push on with your hike or turn back.

  • Activity tracking - The wearable will be able to track your steps and identify walking, climbing, or breaks taken during the hike.

  • Data gathering for ML - The Arduino Nicla Sense ME will send motion and environmental data to another device over a Bluetooth connection and the data will be stored in the Arduino IoT Cloud for future processing.

Hardware requirements

  • Arduino Nicla Sense ME

  • LiPo battery (3.7V, 200mA)

  • Micro USB cable

  • Enclosure

  • K-Way jacket

Software requirements

  • Edge Impulse account

  • Arduino IDE

  • Arduino IoT Cloud account

Hardware Setup

The Arduino Nicla Sense ME is a tiny and robust development board that is specifically designed for wearable applications. It has several Bosch Sensortec's cutting-edge sensors on board, including an accelerometer, gyroscope, magnetometer, and environmental monitoring sensors. In addition, the board has an RGB LED that can be used for visual feedback and it can be powered by a LiPo battery. Furthermore, its compact form factor, high computing power, and low power consumption make it an ideal choice for edge Machine Learning applications.

Barometric pressure is used to forecast short-term weather changes so, for training the weather prediction model, we will use the digital onboard BMP390 low-power and low-noise 24-bit absolute barometric pressure sensor. This high-performance sensor is able to detect barometric pressure between 300 and 1250 hPa and can even be used for accurate altitude tracking applications.

For training the climbing detection model, we will use the onboard BHI260AP self-learning AI smart sensor with integrated 6-axis IMU (3-Axis Accelerometer + 3-Axis Gyroscope) together with the BMM150 3-axis digital geomagnetic sensor.

Housing your wearables in an enclosure is necessary because it protects the electronics from liquids or dust, as well as allows you to attach them securely onto clothing. In this project, we will be using a plastic enclosure for our Arduino Nicla Sense ME which features a hole for the USB port so that we can easily program the board.

Software Setup

In order to use the Edge Impulse platform, you will need to create an account. Once you have done so, log in and click on the "New Project" button. Enter a name for it, then select "Create Project". You should now be redirected to the project main page. Here, you will be able to configure the settings for your project, as well as add and train machine learning models.

To get started, you'll need to connect the Nicla Sense ME to your computer using a micro USB cable. Once it's connected, open up the Arduino IDE and go to the Board Manager (under Tools > Board) to install the board support package (Arduino Mbed OS Nicla Boards).

Next, go to Tools > Board > Arduino Mbed OS Nicla Boards and select the Nicla Sense ME board.

Training the weather prediction model

Data collection

We will collect data for three classes:

  • Normal - This class will be used to detect stable weather conditions.

In the Arduino sketch you’ll find the ei_printf function which sends data through a serial connection to your computer, which then forwards it to Edge Impulse. Depending on which class you want to collect data for, you’ll have to uncomment the corresponding line of code from the code snippet below. Since collecting enough real weather data for training the model would take a lot of time and is weather-dependent, for the purpose of this tutorial we will simulate the Rise and Drop classes using the barometerValueHigh() and the barometerValueLow() functions which generate arbitrary data based on an initial reading of the real measured pressure. To collect data for the Normal class, uncomment the barometer.value() function.

/* uncomment these functions depending on the class you want to collect data for */
ei_printf("%.2f,"
          //, barometer.value()
          //, barometerValueLow()
          , barometerValueHigh()
         );

From a terminal, run:

edge-impulse-data-forwarder

This will launch a wizard that will prompt you to log in and select an Edge Impulse project. You will also have to name the device and the axes of your sensor (in this case our only axis is barometer). You should now see Nicla Sense in the Devices menu on Edge Impulse.

With the data forwarder configured, we can now start collecting training data. Go to Edge Impulse > Data acquisition > Record new data, write the name of the class in the Label prompt, and click on Start Sampling. Each sample is 10s long and you should collect at least 2 minutes of data for each class. For the Rise and Drop classes, each time you collect a new sample you’ll have to press the reset button on the Nicla Sense board to reset the readings.

Your collected samples should look something like this:

Designing the Impulse

Now that you have enough training data, you can design the impulse. Go to Impulse design > Create impulse on Edge Impulse and add a Spectral Analysis processing block and a Classification (Keras) learning block.

An impulse consists of a signal processing block used to extract features from the raw input data, and a learning block which uses these features to classify new data. The Spectral Analysis signal processing block applies a filter to remove noise, performs spectral analysis on the input signal, and extracts frequency and spectral power data. The Classification (Keras) learning block is trained on these spectral features and learns to identify patterns in the data that indicate which class a new data point should belong to.

Click on Save Impulse, then go to Spectral features in the left menu. You’ll se the raw signal, the filtered signal, and the spectral power of the signal.

Click on Save parameters and you will be prompted to the feature generation menu. Glick on Generate features and when the process is done you will be able to visualize the Feature explorer. If your classes are well-separated in clusters, it means the model will easily learn how to distinguish between them.

Training the model

Now go to NN Classifier and start training the model. At the end of the training you’ll see the accuracy and the loss of the model. A good performing model will have a high accuracy and a low loss. In the beginning, you can use the default training settings and adjust them later if you are not satisfied with the performance results.

Testing the model

Go to Model testing and click on Classify All to see how your model performs on new data.

Deploying the model

Finally, go to Deployment and export the trained model as an Arduino library.

Unzip the downloaded library and move it into your libraries folder in your Arduino workspace. At Files > Examples > Examples for custom libraries > your_library_name > nicla_sense > nicla_sense_fusion you’ll find a sketch for running inference on your board. We’ll use the onboard RGB LED for visual feedback as follows:

  • Red - pressure drop;

  • Green - pressure rise;

  • Blue - normal pressure.

You can turn on the LED by adding the following lines of code to the sketch:

// setup
nicla::begin();
nicla::leds.begin();

// loop
nicla::leds.setColor(red);

Training the activity tracking model

Data collection

Create a separate project on Edge Impulse and give it a name.

Again, we will use the Data Forwarder to collect data, so run the following command from a terminal:

edge-impulse-data-forwarder --clean

This will launch a wizard that will prompt you to log in and select the Edge Impulse project. The --clean tag is used when you want to switch to a new project in case you’ve previously connected a project to the Data Forwarder. You will also have to name the device and the axes of your sensor (in this case the axes are in the following order: accel.x, accel.y, accel.z, gyro.x, gyro.y, gyro.z, ori.heading, ori.pitch, ori.roll, rotation.x, rotation.y, rotation.z, rotation.w). You should now see Nicla Sense in the Devices menu on Edge Impulse.

We will collect data for three classes, as described in the previous section:

  • Walking

  • Climbing

  • Staying

Designing the Impulse

The Spectral Analysis signal processing block can identify periodicities in data, which is helpful in this case since the motion and orientation data will have a predictable pattern when the user is sitting, walking, or climbing.

Training the model

Navigate to NN Classifier and begin training the model. Adjust the default training parameters if needed, in order to obtain a better training performance.

Testing the model

Finally, go to Model testing and click on Classify All to check how your model performs on new data.

Deploying the model

Now you can deploy your model as an Arduino library by going to Deployment > Create library > Arduino library. You can also enable the EON Compiler to optimize the model.

Data gathering for ML

We will be using the Arduino IoT Cloud to store the data from the Nicla Sense ME board and visualize the metrics. The platform provides an easy-to-use interface for managing devices, sending data to the cloud, and creating dashboards. In order to use the Arduino IoT Cloud, you will need to create an Entry, Maker, or Maker plus account that allows you to create an API key for sending data online.

To generate your API credentials, follow the steps below:

  1. Access your Arduino account.

  2. In the bottom left corner, click API keys, and then CREATE API KEY. Give it a name and save it somewhere safe. After this, you will no longer be able to see the client secret.

Click on the newly created thing and add variables for the metrics you want to monitor. We’ve used the following ones:

'AccelerationX',
'AccelerationY',
'AccelerationZ',
'GyroscopeX',
'GyroscopeY',
'GyroscopeZ',
'RotationW',
'RotationX',
'RotationY',
'RotationZ',
'OrientationHeading',
'OrientationPitch',
'OrientationRoll',
'StepCount',
'Temperature',
'Pressure',
'Humidity',
'Gas'

Now go to nicla-sense-me-fw-main/bhy-controller/src/ and run:

go run bhy.go webserver

A webpage will pop up and you’ll have to select Sensors. Turn on Bluetooth on your computer, then click Connect and select your Nicla board. After the devices are paired, enable the sensors you want to monitor and the webpage will start making requests to post data to Arduino IoT Cloud.

You can also configure a Dashboard to visualize your sensor data:

Conclusion

The Arduino Nicla Sense ME is a great board for building an outdoor activity tracker that has the ability to monitor your progress on hikes, predict weather changes before they happen and log data for training Machine Learning models. With the Edge Impulse platform, you can effortlessly train Machine Learning models to run on edge devices, and with the Arduino IoT Cloud, you can easily store data for future machine learning processing.

Paired with the weather resistant K-Way jacket, you'll be able take this device along any time you head outdoors making sure you'll be ready for any adventure ahead of you!

In this tutorial, we'll show you how to build a smart hiking wearable using the board paired with the weather-resistant .

We'll use the platform to train Machine Learning models using the data from the sensors, the to program the Nicla Sense ME board, and the to store data and visualize the metrics. By the end of this tutorial, you'll have a working prototype that you can take with you on your next hike!

The first step when designing a Machine Learning model is data collection, and Edge Impulse provides a straightforward method of doing this through their Data Forwarder, which can collect data from the device over a serial connection and send it to the Edge Impulse platform through their ingestion service. To use the Data Forwarder, install the Edge Impulse CLI following the steps from .

Download the Edge Impulse ingestion sketch from and upload it to your board.

Drop - This class will be used to detect bad weather conditions. A quick drop in air pressure indicates the arrival of a low-pressure system, in which there is an insufficient force to push clouds or storms away. Cloudy, wet, or windy weather is connected with low-pressure systems, as explained .

Rise - This class will be used to detect good weather conditions. A sharp rise in atmospheric pressure drives the rainy weather away, clearing the sky and bringing in cold, dry air, as explained .

You can find the full code and the trained model .

Download the Edge Impulse ingestion sketch from and upload it to your board.

Go to the main page.

Now go to and in the Things menu create a new Thing called Wearable.

With the Arduino IoT Cloud configured, we can now start sending data from our device. To do this, download (which is an adaptation based on ) and add the Arduino_BHY2 folder to your Arduino libraries. Go to Examples > Arduino_BHY2 > App and upload this sketch to your device.

Arduino Nicla Sense ME
K-Way jacket
Edge Impulse
Arduino IDE
Arduino IoT Cloud
Edge Impulse CLI
here
here
here
here
here
here
Arduino Cloud
Arduino IoT Cloud
this project
this
Zalmotek
Weather Prediction Model
Activity Tracking Model
https://github.com/Zalmotek/edge-impulse-arduino-k-way-outdoor-activity-tracker