Machine Learning & Artificial Intelligence in the Meditation Industry

In this article, we’re going to talk about them in the context of meditation, mindfulness, and personal growth — industries where the efficiency of digital products highly depends on one’s always shifty mood and mental state. For that matter, those also require high-level personalization and an easy-to-train engine.

Plus, it’s essential to track one’s progress and spot any changes in indicators like heart rate while doing yoga, listening to a motivational speech, or processing an inner trauma within a certain course - all that to track a body’s reaction to internal/external irritants and make relevant recommendations.

Let’s take a look at how Machine Learning and Artificial Intelligence can contribute to handling these challenges and talk about a couple of real-life examples of applying such tech solutions.

📲 How Machine Learning Helps with Meditations Products’ Personalization

Apart from the fact that such products need to adjust their content to one’s interests, it’s also necessary to make real-time activity measurements to be able to instantly spot changes in user preferences. Machine Learning can easily help companies with these tasks. Let’s take a closer look at what capabilities Machine Learning has to offer to the meditation and mindfulness industry.

Meditation Machine Learning Use Cases 📝

First of all, it allows you to enable a smart search function for your app or website. Let’s say a user types in “relaxation after a long day” — what your digital product should offer them? That’s where machine learning tools step in. It allows you to train the engine to give all results under the category “Stress-relief meditations” for inquiries that contain certain keywords — in our case, it could be “relaxation” and “long day”.

Another use case could be making various measurements in real-time. It could be a course bounce rate, for example. Knowing such metrics, you can easily improve your marketing strategies (retargeting, for example) and make it generally more efficient.

The first step is pretty simple — a user needs to choose the desired inner state to reach (“Energized”, for example) and what their mood currently is. Right after the first step is completed, the magic starts to happen.

Mindwell’s system then takes a bunch of outer factors that might impact one’s mood and does it with high-level personalization. It considers a user’s location, time of the day, weather, worldwide political events, significant events in one’s area, etc.

After this behind-the-scenes process is successfully completed, Mindwell offers users meditations that meet their specific goals and background of their inner state the best. Surely, it’s essential to increase the accuracy every single time, which is why Mindwell’s Machine Learning soaks up as much user data as possible. Then it determines the meditation combination that’s most likely to help users stabilize and/or improve their mental well-being based on previous experiences.

❓ Is It Possible to Build your Own Machine Learning Infrastructure for a Meditation App?

Let’s not beat around the bush — the answer is yes, it’s possible to create your own machine learning infrastructure in case you don’t want to use third-party providers. However, it would be more reasonable to ask if it’s worth it.

To answer this question, why don’t we first take a look at what building a machine learning system looks like. Such an infrastructure consists out of:

• Data processing layer that needs to collect and categorize data.
• Configured servers to perform the training on and resources for maintenance.
• Testing frameworks to do version comparisons and testing out algorithms.
• Framework and tools to work with data in real-time & others.

Surely, all that requires deep technical and coding knowledge in the field of machine learning specifically. In case you or your current tech specialists don’t have such, you’ll need to hire a team (freelancers or a software development agency) to build a machine learning infrastructure for you. Plus, you’ll also need to integrate machine learning functionality with the backend of your digital solutions.

There’s a similar device from BrainTap that’s also able to use lights for additional therapeutic effects. Another more advanced version is provided by OmniPEMF. Practically, most sound stimulative devices use PEMF technology as it’s efficient and harmless to humans. However, there are certain contraindications that you and your users need to be aware of.

Most of the providers listed in this subsection haven’t partnered/integrated with other companies yet. However, there’s a first time for everything and inspiration in everything! 🚀

💡 Takeaways

To sum up, we’d like to say that integrating such technologies into your digital meditation and mindfulness solution should be neither forced nor rashed. As useful as it might be, you should spend some time “observing” your business.

Make a thorough research on what ML & AI might be used for, what it takes to integrate them and what problems they might solve, think about whether you have some of them or not, reach out to ML- or AI-based tools providers or software developers, and then make your decision.

Our team has quite an extensive expertise in this industry and our Meditation App Development company shares this experience with you.

In case you need help with integrating such systems into your app or have any other questions — feel free to contact us. We’d be happy to help you! 🚀