Recent studies show over 60% prevalence of postural disorders for healthy 20 to 50-years-olds,' and it starts to affect all demographics, moving far beyond pure aesthetics.
Is there a way to tackle this problem without regular physiotherapeutic appointments? Our client believes there is. And we help turn that faith into reality.
Revolution in motion
We were contracted by a NY-based healthtech startup that brings together the best physiotherapists and orthopedists from the US. They wanted to build a system to offer professional and fully automated movement-oriented aid via mobile app.
The beneficent of the applications are not just people with postural disorders. The client wants to help anyone in a group of risk - starting with those with desk jobs, ending on pro athletes looking for movement optimization guidance they can access anytime.
The application's core combines medical knowledge, machine learning proficiency, and modern 3D-scanning technology.
Physiotherapist in your pocket
It all starts with creating a digital twin of a patient's body. The scan-based silhouette model will serve as an input for diagnosis tools further in the process.
Sparkbit's team is responsible for the next step - the digitalization of posture diagnosis. Up until now, it was performed by a physiotherapist in his office. We replaced him (and the office) with a sequence of analyses performed by deep-learning 3D analysis models and mathematical algorithms we proposed.
We aim to assess, describe and evaluate the silhouette. We're currently capable of detecting over 20 different musculoskeletal disorders. Among others:
- lordosis and kyphosis associated with spine curvature,
- duck feet and pigeon toes based on feet position,
- valgus and varus stress connected with legs outline,
- leaning forward and backward, determined by measuring a center of mass.
As the last step, a set of algorithms assigns corrective exercises to help the patient. They build up to become a personalized movement plan that evolves with the patient's progress. Continuous therapy adjustments allowed by periodically performing new scans are what predestinates the product's upcoming success. It provides a defined and actionable path to full recovery.
The science of movement
Science is all about experimenting, and so is our part of the solution. We worked closely with the client's physiotherapists to evaluate the results of various experiments. The goal was to select the most promising ML models and proceed with them to reach the highest accuracy.
The most significant challenge we faced was insufficient model training data. How did we cope with it? The answer came with research. Since we knew how to perform "by the book" posture assessment, we could apply mathematical methods to it. We've proven they are more accurate than predictions offered by deep learning in certain cases. This way, we mitigated the problem of the time and resource costs of expanding the dataset with an up-and-coming heuristic method.
As our approach brought new business value to the product, the stakeholders decided that we should include it in our solution, combining mathematical algorithms with deep learning-based assessments.
MLOps - the fundamental process
We provided a complete MLOps cycle for the project. This way, the client could focus on expanding the product’s functionalities and further application development while we designed and delivered the technology, seamlessly integrating it with the system.