A digital illustration showing a human figure made of sensors, representing advanced movement analysis.

Unlock Your Body's Secrets: How Inertial Sensors are Revolutionizing Movement Analysis

Soraya Malik in Tech & Innovation December 2025 • 5 min read.

"Discover the cutting-edge technology that's making human motion capture more accurate, accessible, and impactful than ever before, transforming healthcare, sports, and beyond."

Understanding the intricacies of human movement has long been a pursuit across various fields, from medicine to sports. Wearable Inertial Measurement Units (IMUs) are emerging as powerful tools for capturing and analyzing these movements in real-time. These tiny sensors, measuring acceleration, magnetic field, and gyroscopic data, offer a window into the complexities of human motion, paving the way for more accurate diagnoses, personalized treatments, and enhanced performance training.

The need for precise and robust motion capture is especially critical in addressing movement disorders. Many conditions require careful assessment of even the subtlest movements, making accuracy paramount. Practical considerations also play a significant role. For patients with movement disabilities, the ease of wearing and using these sensors is just as important as their technical capabilities. This means minimizing the number of sensors, positioning them comfortably, and ensuring they can be used for extended periods.

Estimating skeletal and limb orientations to describe human posture dynamically poses significant challenges. This article explores how innovative approaches in measurement conversion and data processing can enhance the accuracy and reliability of IMU-based motion capture. We'll delve into the use of quaternions to avoid common issues with traditional Euler angles, and discuss optimization techniques for improved accuracy. Using the human shoulder joint as a key example, we'll illustrate these concepts and their potential to transform movement analysis.

Improving Accuracy: A Converted Measurement Approach

A digital illustration showing a human figure made of sensors, representing advanced movement analysis.

Traditional methods for estimating limb orientation often involve complex, non-linear calculations. These can be prone to errors and instability, especially in systems with significant uncertainties. This research introduces a 'converted measurement' approach, which reframes the problem in a linear context. By representing the data in a way that allows for linear processing, the system can achieve greater accuracy and reduce the risk of divergence.

One key element of this approach is the use of quaternions, a mathematical tool that avoids the 'gimbal lock' issues common with Euler angles. Quaternions provide a more stable and reliable way to represent rotations in 3D space. The approach also includes a systematic method for quaternion normalization. This is essential for maintaining accuracy over time, as small errors can accumulate and lead to significant deviations.

Linearization for Accuracy: Measurement conversion ideas as a representation signifying a linear characterisation of an inherently non-linear estimation problem, pragmatically improves the overall estimation of the limb orientation.
Quaternion Advantage: A quaternion, as opposed to the euler angle based approach is adopted to avoid Gimbal lock scenarios.
Optimization-Based Normalization: Also lay a systematic basis for quaternion normalisation, typically performed in the pre-filtering stage, by introducing an optimisation based mathematical justification.

To validate this approach, a robust version of the Extended Kalman Filter (EKF) is used. The EKF is a powerful tool for combining data from multiple sensors and estimating the state of a system over time. In this case, it's configured to integrate the converted measurements and optimized quaternions, providing a comprehensive and structured approach to IMU-based human pose estimation.

The Future of Movement Analysis

This research demonstrates the potential of innovative data processing techniques to enhance the accuracy and reliability of IMU-based motion capture. By combining converted measurements, quaternion optimization, and robust Kalman filtering, the system achieves significant improvements in human pose estimation. These advancements pave the way for more effective and accessible movement analysis in a wide range of applications, from healthcare to sports. As the technology continues to evolve, we can expect even more sophisticated tools for understanding and improving the way we move.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/jtehm.2018.2877980, Alternate LINK

Title: Robust And Accurate Capture Of Human Joint Pose Using An Inertial Sensor

Subject: Biomedical Engineering

Journal: IEEE Journal of Translational Engineering in Health and Medicine

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Authors: Pubudu N. Pathirana, M. Sajeewani Karunarathne, Gareth L. Williams, Phan T. Nam, Hugh Durrant-Whyte

Published: 2018-01-01

Everything You Need To Know

What are Inertial Measurement Units (IMUs) and why are they important for analyzing human movement?

Inertial Measurement Units (IMUs) are small, wearable sensors that measure acceleration, magnetic field, and gyroscopic data. These sensors are used to capture and analyze human movement in real-time. IMUs are significant because they offer a way to accurately diagnose movement disorders, personalize treatments, and enhance performance training. The implications of using IMUs include more precise and robust motion capture, especially critical in addressing movement disorders where subtle movements need careful assessment. Ease of wear and use is also important for patients with movement disabilities.

What is the 'converted measurement' approach and why is it used to improve accuracy in motion capture?

The 'converted measurement' approach reframes the problem of estimating limb orientation in a linear context. Traditional methods often involve complex, non-linear calculations that are prone to errors. By representing the data in a linear format, the system achieves greater accuracy and reduces the risk of divergence. This is important because it allows for more reliable and precise motion capture, which is essential for accurate diagnoses and effective treatments. Implications include improved accuracy in estimating skeletal and limb orientations to dynamically describe human posture.

What are quaternions and what advantage do they offer over other methods in motion capture?

Quaternions are a mathematical tool used to represent rotations in 3D space, avoiding the 'gimbal lock' issues common with Euler angles. They provide a more stable and reliable way to represent rotations. The advantage of using quaternions is that they prevent instability and errors in estimating limb orientation, leading to more accurate motion capture. This is significant because it ensures the reliability of data used in various applications, from healthcare to sports. Implications include more precise and consistent representation of human posture.

What is optimization-based normalization and why is it important in maintaining accuracy when using quaternions?

Optimization-based normalization is a systematic method for ensuring the accuracy of quaternions over time. This process involves mathematically justifying the normalization, which is typically performed in the pre-filtering stage. It's important because small errors can accumulate and lead to significant deviations in accuracy. Implications include maintaining the reliability of motion capture data and ensuring accurate pose estimation over extended periods.

What is the Extended Kalman Filter (EKF) and what role does it play in IMU-based motion capture?

The Extended Kalman Filter (EKF) is a powerful tool used to combine data from multiple sensors and estimate the state of a system over time. In the context of IMU-based motion capture, the EKF integrates converted measurements and optimized quaternions. Its significance lies in providing a comprehensive and structured approach to human pose estimation. Implications include improved accuracy and reliability in motion capture, leading to more effective and accessible movement analysis in various applications.