Case Study – Using Pose Estimation for Physical Therapy Assessments

Using Pose Estimation for Physical Therapy Assessments

Physical therapy is a crucial part of the rehabilitation process for many patients with musculoskeletal injuries, conditions, and disabilities. The goal of physical therapy is to help patients regain function, strength, and mobility. A key part of this process is the initial assessment, where the physical therapist evaluates the patient's condition and develops a treatment plan.

The Problem

The traditional method of physical therapy assessment involves manual evaluation, which can be time-consuming, subject to human error, and reliant on the therapist’s individual expertise. This can lead to inconsistent assessments and treatment plans, and make it difficult to track progress over time.

The Solution

Pose estimation, a computer vision technique that uses deep learning algorithms to estimate the pose of a person in real-time, can be used to assist in the physical therapy assessment process. By using a camera and machine learning algorithms, a system can automatically estimate a patient’s posture, movements, and overall body language to provide a more accurate and objective assessment of their condition.

The Implementation

With QuickPose SDK – GitHub Repo

An app is created that uses the QuickPose SDK and has our health module active.

The device can be placed like a kiosk or held by a practitioner. 

In Kiosk mode the patient can select the joint or movement that they want to measure and will be guided by a timer when the photo will be taken for the measurement. 

In handheld mode the practitioner will be able to guide the patient into specific movements and capture the image with just a tap of the screen. 

Based on the implementation this data can be stored or deleted depending on your needs.

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