iPlena Uses AI to Transform the User Experience in Physiotherapy

iPlena partners with Neurons Lab to create a fully personalized, at home physiotherapy experience.

Partner Overview

iPlena is the first mobile body scanning AI for tailored osteopathic training aimed at improving posture and back health.

It offers users a fully personalized experience which only requires 3 minutes a day – quicker than a cup of coffee. The AI works by analyzing pictures taken by the user and provides recommendations for a healthier posture.

Business Challenges

Automating recommendation technology without losing quality.

iPlena needed to build a mobile application with an AI/ML component that could recommend a set of therapeutic exercises to compensate for the user’s physiological problems.

As well as this, they also wanted to develop AI/ML components with computer vision that could analyze human body posture and propose treatments and exercises.

The main challenge they would face would be to automate their technology without losing the quality of the diagnosis and recommendations.

Solution

A multi-output regression model to develop a diagnosing and physiotherapeutic exercises recommendation system.

The team at Neurons Lab, made up of scientists, engineers and developers passionate about transforming businesses with AI and machine learning technologies, partnered with iPlena to create a secure and scalable foundation for AWS.

This included AWS Control Tower, AWS Security Hub, AWS CloudTrail, AWS Config, Amazon GuardDuty, Amazon VPC, AWS Service Catalog, and other AWS services.

Neurons Lab created MLOps pipelines to ensure accessible building, training, and deployment processes for AI/ML models using Amazon SageMaker Pipelines, Amazon SageMaker Training and Inferences, AWS CodePipeline, AWS CodeBuild, and more.

They also created a self-service portal for infrastructure patterns using AWS Service Catalog, which included MLOps pipelines, VPC products, and the AWS Account Factory.

A key point detector AI/ML model was built, trained, and deployed using Amazon SageMaker to identify key points in the human body in photos and videos. An anomaly diagnostic AI/ML model was also built, trained, and deployed using Amazon SageMaker to detect anomalies in human posture.

An iOS mobile application was created that recommends a set of therapeutic exercises aimed at compensating for the physiological problems of the user based on photos and videos analyzed through the mobile application. The app was deployed on Amazon EKS, Amazon RDS, Amazon ElastiCache, and Amazon DynamoDB.

Results

Through this co-created project iPlena has been able to:

• Increase deployment frequency, reduced lead times, and decreased failure rates resulting in improved efficiency.
• Meet security and compliance requirements ensuring patient safety and privacy
• Provide a tool for self-diagnosis and therapeutic exercises through the mobile application, resulting in patients taking control over their own health and being able to continue treatment outside of the clinic
• Allow patients to carry out efficient and accurate diagnoses using AI technology, resulting in reduced appointment times and increased revenue for the clinic
• Improve patient experience by offering recommendations for a set of therapeutic exercises aimed at compensating for their physiological problems without visiting a physiotherapist.