Deep Camera-Based Movement Analysis for Remote Rehabilitation and Physical Therapy

Objective
To develop a mobile application that leverages advanced 3D pose estimation technology for tracking and analyzing a person’s movement outside traditional laboratory settings. This app aims to complement the clinician’s work by enabling remote monitoring and interaction between patients and healthcare providers, thus facilitating data-driven physiotherapy and rehabilitation sessions.

The benefits of such an approach include:

• Adaptive, personalized therapy to each patient’s progress and needs
• Progress tracking highlighting achievements and areas needing attention
• Assisting in correct execution by providing immediate feedback

Background
The need for innovative tools that offer quantitative insights into patients’ movements has become increasingly apparent, particularly for remote or home-based physiotherapy and rehabilitation. Traditional methods often rely on in-person assessments that may not fully capture the nuances of a patient’s progress or challenges.

An app that provides accurate, quantitative movement analysis can significantly enhance the clinician’s ability to tailor treatments, monitor progress remotely, and ensure patients perform exercises correctly while reducing the need for frequent in-person visits.

Status
The current app demo utilizes a single smartphone or tablet to capture a person’s 3D movement using the device’s depth camera capabilities. To ensure high accuracy and reliability, the app employs a machine learning model to refine and improve pose estimation based on data collected from a wide range of users. The current implementation primarily targets the lower extremities, focusing on walking. Efforts are underway to broaden the model’s scope to encompass a broader range of movements.

Crossdisciplinary collaboration
The project partners are Innovations Office Region Stockholm and Danderyd University Hospital.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event: