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

Sensors

Laisi Cai¹  Ye Ma²  Dongwei Liu³ 

[1] Faculty of Sports Science, Ningbo University, Ningbo 315000, China;Research Academy of Grand Health, Faculty of Sports Science, Ningbo University, Ningbo 315000, China;School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China;

关键词: upper limb functional assessment;    Kinect;    deep learning;    recurrent neural network;    kinematics;   

DOI  :  10.3390/s20071903

来源: DOAJ

【 摘 要 】

We develop a deep learning refined kinematic model for accurately assessing upper limb joint angles using a single Kinect v2 sensor. We train a long short-term memory recurrent neural network using a supervised machine learning architecture to compensate for the systematic error of the Kinect kinematic model, taking a marker-based three-dimensional motion capture system (3DMC) as the golden standard. A series of upper limb functional task experiments were conducted, namely hand to the contralateral shoulder, hand to mouth or drinking, combing hair, and hand to back pocket. Our deep learning-based model significantly improves the performance of a single Kinect v2 sensor for all investigated upper limb joint angles across all functional tasks. Using a single Kinect v2 sensor, our deep learning-based model could measure shoulder and elbow flexion/extension waveforms with mean CMCs >0.93 for all tasks, shoulder adduction/abduction, and internal/external rotation waveforms with mean CMCs >0.8 for most of the tasks. The mean deviations of angles at the point of target achieved and range of motion are under 5° for all investigated joint angles during all functional tasks. Compared with the 3DMC, our presented system is easier to operate and needs less laboratory space.

【 授权许可】

Unknown