Lora to Lora gateway asset tracking with dashboard and maps
Lora to Lora gateway asset tracking with dashboard and maps
Setup hardware and its application for asset tracking using Adafruit Lorawan (Lora). This communication design is done from Lora to Lora.
Internet of Things
Working and training machine learning is currently highly complex and computationally intensive. A typical user of machine learning is needs an overall understanding of the underlying hardware and software infrastructure, including configuring Spark, installing machine learning libraries within a framework hosting these libraries, and managing the jobs during execution to handle failures and recovery.
This is where the opportunity of machine learning as a service comes in along with IBM Studios to provide hand-in-hand an easy configuration. In this code pattern we are going to show how to deploy health app, which is web based, uses gyroscope for pulse metrics and Watson Machine Learning on IBM Cloud and on IBM Studios. MyPulse, the app reads the live data generated for about less than a minute and transmits it in real-time to perform predictions on heart-rate and seconds coordinates, to return back the beats per minute (bpm) as scoring values. The app provides other gyroscopic metrics and stores all these data in Cloudant database and displays them on IoT platform too, all in real-time.
- Setup hardware starting with Lora node(s) that will act as a transmitter, there will be a Lora that will act as a receiver and pass data to the Raspberry Pi (gateway).
- Watson IoT Platform instance needs to be created and binded to the Node.js app in order to create devices and controls the data events
- IBM Cloud
- IBM Cloud Documentation
- IBM Cloud Developers Community
- IBM Watson Internet of Things
- IBM Watson IoT Platform
- IBM Watson IoT Platform Developers Community
- Savitzky–Golay filter for smoothing the accelerometer data
- [Machine Learning]
- [Spark]
- [Node.js Application]
- [Data in Database]
- [Visualization]
- [Gyroscope]
Almost multiple in a day, most of the people uses devices to check and track their health performances. MyPulse pattern targets the most used device in the world and that is the mobile phone. Unlike other designs, MyPulse applies a design based on accelerometer metrics and studies that filter down the noise to keep out errors.
MyPulse takes these metrics and makes a prediction on what the pulse rate is, translated as beat per minute, through Watson Machine Learning.
A generated number will associated to the mobile device and it will become registered with Watson IoT Platform and the gyroscope metrics will be displayed in real-time on an ongoing graph.
Not to forget to mention that this pattern shows that all the work done above are stored in a Cloudant database.
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