The future of low-cost high quality physiological sensor platforms: Home Assistant & OpenBCI GUI & GoldenCheetah & Neurokit2 & MRIS sensor plugins & integrations?

Last Updated on June 2, 2023 by pg@petergamma.org

And many more physiological sensor platforms with Home Assistant plugins and integrations. We promote here the idea of a Home Assistant version for physiological sensors.

We started our evaluation for a low-cost high data acquisition system for meditation research with Matlab and the Garmin watch. But our evaluation is also suitable for other physiological research projects which have not research grant, such as for instance student projects, or research projects at the university level with low budget, as well as for other persons interested in this topic.

We can buy the Matlab Home edition license for about 200 USD. But if we want a little bit more than looking at our data at home, the Matlab standard edition is required for around 1000 USD. This standard edition does not even include additional toolboxes, which are expensive as well, and need to be bought separately.

We now found out, that the the Home Assistant Operating System version can be flashed on a Raspberry Pi 3 or 4:

This version of HA is especially suitable for developers. A Raspberry Pi 4 can easily be battery powered. Information about it can be found in this journal.

There are open source projects available for sports sensor data analysis, such as GoldenCheetah.

https://www.goldencheetah.org/

But contemporary and new platforms such as Home Assistant offer many new features, such as InfluxDB, Grafana, HA integrations and plugins from different developers, and a community which is alive.

We think a project where the sensors we introduced in our journal are connected to Home Assistant could also be a great device for sports men and women who swim, bike and run or tho other sporting activities.

Who wants to only look at their data in Garmin Connect, where users are harldy allowed to do any calculations? There are many specialized applications for sporting activities. But we promote an open source platform without any commercial interests.

We will give this journal a better structure sooner or later. It is our life-time journey, and it is highly desirable that the issues we publish are resolved by people who love to code, and can to this in an easy way. As easy as it was for us to write this journal.

We mainly reviewed sensor components for meditation research. But since we usually don t code, it would take a lot of time to integrate all these sensors into Home Assistant. But for professional coders is it not an easy job? We can try to develop new products for every specialized application. But we are convinced that the market for such devices and products is small, and we therefore promote an open source solution. We are furthermore convinced, that an open source project based on Home Assistant with the sensors introduced here would be for the benefit of many people and could be a sustainable and successful long-term project.

We will develop our own software application, eventually starting with SQlite, by entering values manually from the devices we use. Since we usually don t code, this will take time, and will only proceed slowly. We have no plans to publish our code or sell any soft- and hardware products.

As far as optical sports sensors are concerned, these have become more and more accurate in recent years, as this review shows:

Optical sensors used for physiology, as for instance finger plethysmographs are much more inaccurate than the latest optical sports watches. Unfortunately, they are not validated for biomedical and clinical research. It would be highly desirable, that the quality of sports watch accuracy validation papers increases and that standard protocols are used so that data from different papers can be compared. Eventually specialized devices will be developed for research, preferentially open source hardware and software products, and preferentially low-cost high quality devices. This increases the chance for their success, following the example of Raspberry Pi, which makes a big effort to keep prizes low. We suppose that this is a reason for the worldwide success story of Raspberry Pi single board computers. Low-cost high-quality physiological sensor platforms as described here, could follow the example of Raspberry, altough not as far as popularity is concencers, since only view people uses thoes. But they cna be helpful also for people in poor countries, which cannot afford expensive equipment. And the lower the costs for the devices are, the bigger the community will be.

Eventually also schools for yoga and meditation are interested in these kind of devices. But many of those are very simple, and people live a simple life there. So will the buy expensive equipement to study the effect of yoga and meditation? One of the Yoga schools which have already have published papter is Sadhgurus Isha Foundation and Yogi Bhajan Kundalini Research Institute. But both of these schools seem not to have a large research grant.