If you’ve ever been curious about how machines can understand human movement despite various influences, the solution is to analyze human posture. This article aims to show how Human Pose Estimation (HPE) technology can recognize and predict the human body’s position and alignment. This technology is currently transforming the healthcare industry, particularly in medical rehabilitation and fitness. Using Artificial Intelligence (AI) services makes this advancement feasible, demonstrating the true power of effective AI. Keep reading to discover how this groundbreaking technology is changing our lives.
Stay tuned and learn how this revolutionary technology can change our lives.
Table of Contents
What Is Human Pose Estimation?
Human Pose Estimation (HPE) is a way to identify and classify joints in the human body. It’s a way of recording a series of coordinates for each joint (arm, head, torso, etc.), called a key point, which can describe a person’s pose. In technical terms, the connection between these points is actually called a pair.
Types Of Models For Estimating Human Poses:
Human Pose Estimation (HPE) is a computer vision technique that identifies and classifies specific points on the human body. These points represent our limbs and joints to calculate bending angles and estimate the Pose or posture of the human body. Physiotherapists, fitness trainers, and artists work by knowing the correct joint angles for a particular exercise.
What if a machine could understand things like an experienced physical therapist or fitness trainer? The results it can produce will be amazing!
Most of HPE’s technology works by taking an RGB image with an optical sensor and using that image to recognize body parts and general pose. The basic function of this technology is to identify places of interest in the human body. We can even use these key details to create 2D or 3D human body models.
Types Of Human Pose Estimation Models:
There are three main types of dummies:
1.) Skeleton-Based Model:
This representation, also known as a kinematic model, consists of a set of key points (joints) such as ankles, knees, shoulders, elbows, and wrists and is primarily used for the 2D or 3D positive assessment. The relationship between different anatomical parts is often depicted using this model.
2.) Contour-Based Model:
Active shape modeling (ASM) uses principal component analysis (PCA) to capture the deformation of the human body. This method, called a plane model, calculates a 2D pose. The result is a general profile and measurements of the body, torso, and limbs.
3.) Volume-Based Model:
Volumetric models provide useful data for 3D pose estimation. The dataset contains several 3D human models and poses represented by masks and shapes.
How Does Human Pose Estimation Work?
Pose estimation tracks the location of an object or person using their pose and orientation. Allows software to estimate a body’s spatial positions or poses in a still image or video.
There are two types of pose estimation: multiple poses and single poses. Single-exposure estimation is used to estimate the poses of a single object in a particular scene, while multi-exposure estimation is used to detect poses for multiple objects.
The problem of estimating human poses has been tackled using different strategies. However, the existing techniques can be roughly divided into three groups:
- Absolute pose estimation: The absolute pose estimation method can use several active and passive landmarks as landmarks. It is based on heat map matching and satellite navigation data.
- Relative Pose Estimation: This is based on a method known as esteem, which calculates a person’s posture by comparing it to known joints, such as B. their starting position and orientation.
- Appropriate pose estimation: Posture reasonable estimation uses both absolute and relative approaches.
Human Pose Estimation Use Cases In Healthcare & Medical Rehabilitation:
Artificial intelligence and the proper use of AI services can do wonders, and that’s so clear from what we’ve learned so far, isn’t it?
Now let’s examine a few important use cases and applications of HPE in healthcare.
Physiotherapy & Rehabilitation:
The area of physiotherapy offers an example of how human activity can be monitored with comparable usage guidelines, particularly in the current age of telehealth, where in-home visits have become more versatile and varied. In this context, AI technology facilitates more sophisticated types of online computation and aids in developing more useful apps utilizing HPE.
Regarding the examination of rehabilitation activities, the use of HPE is akin to fitness apps, except for the need for greater precision. When dealing with injuries and their corresponding rehabilitation, HPE can assist in detecting postures and furnishing users with information regarding particular physical exercises. Again, the benefits are:
- The care costs can be decreased because physiotherapists may not need to be involved, or their involvement can be minimal.
- Users may experience improved health outcomes.
- The ease of exercising at home.
AI Fitness & Self-Training:
Posture estimation has many potential applications, including fitness apps and AI-driven training. In a phone app, a model can use the hardware camera as a sensor to record and analyze a person’s exercise. Human motion tracking allows movement to be divided into eccentric and concentric phases to analyze different flexion angles and overall posture. Key points are tracked to provide analysis through prompts or graphical analyses, either in real-time or with a brief delay, giving users valuable information on movement patterns and body mechanics.
In addition to fitness and physiotherapy, posture estimation has potential applications in various industries, including retail and manufacturing. In retail, posture estimation technology can be used to analyze customer behavior in stores and offer personalized product recommendations. In manufacturing, it can be used to track worker movements and prevent workplace injuries. AI and posture estimation technology are not limited to a single field and have numerous possibilities for improving efficiency and productivity.
The Future Of Human Pose Estimation:
Human pose estimation (HPE) is actually a very active and profound research area. There are many different tactics, and more are likely to start to emerge in the upcoming years. This research aims to create systems that can quickly and accurately recognize human poses in images or videos.
With numerous players offering AI-based personal trainers, teletherapy, digital MSK, and virtual rehabilitation support, the effect of HPE’s advancements on health and safety is already evident. As a result, thanks to modern computer technology, it is now easier and more practical to use HPE to create animated characters and superheroes. If you want to use HPE in your apps, be wise and contact professionals immediately.
About the Author:Vishnu Narayan is a content writer, working at ThinkPalm Technologies, a software & mobile app development services company focusing on technologies like BigData, IoT, and AI services. He is a passionate writer, a tech enthusiast, and an avid reader who tries to tour the globe with a heart that longs to see more sunsets than Netflix!
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