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Related Experiment Video

Updated: Dec 25, 2025

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

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Published on: February 12, 2018

Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor.

Ye Ma¹, Dongwei Liu², Laisi Cai³

¹Research Academy of Grand Health, Faculty of Sports Science, Ningbo University, Ningbo 315000, China.

Sensors (Basel, Switzerland)

|April 3, 2020

Summary

This summary is machine-generated.

This study introduces a deep learning model to enhance upper limb joint angle accuracy using Kinect v2. The refined kinematic model significantly improves motion analysis for functional tasks, offering a more accessible alternative to 3D motion capture.

Keywords:

Kinect deep learning kinematics recurrent neural network upper limb functional assessment

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Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

Area of Science:

Biomechanics
Machine Learning
Human Motion Analysis

Background:

Accurate assessment of upper limb joint angles is crucial for clinical and research applications.
Existing motion capture systems can be expensive, complex, or require specialized environments.
Consumer-grade sensors like Kinect v2 offer potential but require refinement for precise kinematic analysis.

Purpose of the Study:

To develop and validate a deep learning-refined kinematic model for accurate upper limb joint angle assessment using a single Kinect v2 sensor.
To improve the systematic error compensation of the Kinect v2 kinematic model.
To provide a more accessible and user-friendly alternative to traditional 3D motion capture systems.

Main Methods:

A long short-term memory recurrent neural network was trained using a supervised machine learning architecture.
The model was trained to compensate for systematic errors in the Kinect v2 kinematic model, using a marker-based 3D motion capture system (3DMC) as the gold standard.
Experiments involved functional upper limb tasks: hand to contralateral shoulder, hand to mouth/drinking, combing hair, and hand to back pocket.

Main Results:

The deep learning-based model significantly improved the performance of the Kinect v2 sensor for all upper limb joint angles across all functional tasks.
Mean coefficients of multiple correlation (CMCs) for shoulder and elbow flexion/extension waveforms exceeded 0.93.
Mean deviations for joint angles at target achievement and range of motion were under 5° compared to 3DMC.

Conclusions:

The developed deep learning model enhances the accuracy of upper limb joint angle measurement using a single Kinect v2 sensor.
The system demonstrates high reliability and accuracy for various functional tasks, comparable to gold-standard 3DMC.
This approach offers a practical, cost-effective, and space-efficient solution for human motion analysis.