Pose Estimations

Successfully Predicting and Tracking the Location of a Person/Object

Presenting Our Client

Our prestigious client was a healthcare start-up situated in the United States that excelled in human activity recognition and motion analysis. They created a mobile application for healthcare that captures and estimates human body movements, particularly during workouts. The app has a variety of useful tracking functions that will assist users in exercising effectively. The software assists users in achieving their fitness goals.

Challenges Faced by Our Client

Our client encountered a problem with open-source alternatives for estimating mobile posture. Real-time human posture estimate was a critical concern for our client. The primary goal was to establish a modern human pose estimation model capable of detecting a person's posture in a real-time setting.

Client’s Requirement

To compete in the healthcare application market, our client desired to enhance their app with artificial intelligence. The following is a breakdown of our client's requirements.

1. The objective was to increase the accuracy of human body posture estimation without sacrificing speed.

2. Our client desired to optimize their fitness app's functionality by detecting real-time problems during workouts.

All of these features may assist the user in avoiding typical training mistakes, hence lowering the chance of physical injury. Allianze InfoSoft's dedicated team provided the client with robust pose estimation and error detection features.

Solutions Framed by Team InfoSoft

To begin, let us explain that posture estimate can be classified into the following categories:

• Single Person
• Multi Person
• 3D or 2D
• Real-Time or Offline

After analyzing our client's requirements, we decided to develop real-time, two-dimensional, and single-person posture estimate. This is easily applicable to a variety of physical workouts. We used deep learning techniques to detect real-time movement of human joints. Our artificial intelligence developers created a new neural network technology that may be used to harness a variety of thoughts and ideas.

We gathered a variety of publicly available datasets for various types of human posture estimation. This generates a massive amount of data, which is a necessary component of high-quality deep neural networks. It operates precisely and solidly. To enhance the dataset, we devised a method for data augmentation. Additionally, we built a sophisticated human skeleton model, which enables the analysis of fitness and treatment exercises. Next problem was to implement the error detection during workouts. The concept of error detection aids in determining which forms of physical activity are appropriate and which are not. It is utilized to detect human joints and serves as a foundation for proper exercise technique. Additionally, we developed specialized algorithms to detect problems.

Outcome of Our Service

We used our computer vision and deep learning expertise to successfully provide our client with an AI-powered solution. Our deep learning expertise aided our customer in overcoming the human activity recognition barrier. For the fitness app and rehabilitation activities, our real-time human position estimation neural network and error detection algorithms were effectively incorporated. At the moment, the app assures that the user is performing the activity correctly and securely achieving their fitness goals. If you wish to seek our assistance in pose estimation, reach us at [email protected]