Last Updated on April 2, 2023 by pg@petergamma.org
We suggest for instance for Python coders who want to attach all the sensors introduced in the journal of the MRIS to a software platform running on a mobile device the following procedure:
- Choose Neurokit 2 or Python & InfluxD and attach all the sensors, and eventually also Home Assistant. There are examples for Home Assistant for instance for the Apple watch (can be found in this journal).
- For code which requires a x86 processor:
- LSL Python does require a x86 processor
- or if you want to choose a Home Assistant image which allows an easy editing of .yaml file, which is essential for developers who want to work with Home Assistant
For these examples we have to choose a device with x86 processor, for instance a LattePanda V1 or a more powerful x86 device.
- For code which does not require a x86 processor
- if your code does not contain LSL Python it does not require an x86 processor, for instance Home Assistant images can also built together for the Raspberry Pi to have a version which allows edition of .yaml files. If you build Home Assistant images according to instructions which can be found on YouTube for instance for the Raspberry Pi 4 by our own, which allows edition of .yaml files, it does not require a x86 processor.
In these cases we can choose an ARM based platform, and choose the device which fits best for your needs. Avoid as much as possible code which requires an x86 processor. These devices are less suitable for mobile applications than ARM based platfroms, since the are more power hungry.
We suppose sooner or later a software image with for instance InfluxDB connected to the sensors in our journal will be freely available and can be downloaded from a GITHUB page.
- We have not plans to do this by ourselves, since we are non-coders, but we are happy to contribute as reviewers and debuggers.
- Developers welcome to contribute to this project in any form you can.