Literature DB >> 27102160

Validation of the Leap Motion Controller using markered motion capture technology.

Anna H Smeragliuolo1, N Jeremy Hill2, Luis Disla3, David Putrino4.   

Abstract

The Leap Motion Controller (LMC) is a low-cost, markerless motion capture device that tracks hand, wrist and forearm position. Integration of this technology into healthcare applications has begun to occur rapidly, making validation of the LMC׳s data output an important research goal. Here, we perform a detailed evaluation of the kinematic data output from the LMC, and validate this output against gold-standard, markered motion capture technology. We instructed subjects to perform three clinically-relevant wrist (flexion/extension, radial/ulnar deviation) and forearm (pronation/supination) movements. The movements were simultaneously tracked using both the LMC and a marker-based motion capture system from Motion Analysis Corporation (MAC). Adjusting for known inconsistencies in the LMC sampling frequency, we compared simultaneously acquired LMC and MAC data by performing Pearson׳s correlation (r) and root mean square error (RMSE). Wrist flexion/extension and radial/ulnar deviation showed good overall agreement (r=0.95; RMSE=11.6°, and r=0.92; RMSE=12.4°, respectively) with the MAC system. However, when tracking forearm pronation/supination, there were serious inconsistencies in reported joint angles (r=0.79; RMSE=38.4°). Hand posture significantly influenced the quality of wrist deviation (P<0.005) and forearm supination/pronation (P<0.001), but not wrist flexion/extension (P=0.29). We conclude that the LMC is capable of providing data that are clinically meaningful for wrist flexion/extension, and perhaps wrist deviation. It cannot yet return clinically meaningful data for measuring forearm pronation/supination. Future studies should continue to validate the LMC as updated versions of their software are developed.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Leap motion; Motion capture; Motor recovery; Physical therapy; Rehabilitation; Telemedicine

Mesh:

Year:  2016        PMID: 27102160     DOI: 10.1016/j.jbiomech.2016.04.006

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  25 in total

1.  Analysis of the Leap Motion Controller's Performance in Measuring Wrist Rehabilitation Tasks Using an Industrial Robot Arm Reference.

Authors:  Rogério S Gonçalves; Marcus R S B de Souza; Giuseppe Carbone
Journal:  Sensors (Basel)       Date:  2022-06-28       Impact factor: 3.847

2.  Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients.

Authors:  Zun-Rong Wang; Ping Wang; Liang Xing; Li-Ping Mei; Jun Zhao; Tong Zhang
Journal:  Neural Regen Res       Date:  2017-11       Impact factor: 5.135

3.  Evaluation of the Leap Motion Controller during the performance of visually-guided upper limb movements.

Authors:  Ewa Niechwiej-Szwedo; David Gonzalez; Mina Nouredanesh; James Tung
Journal:  PLoS One       Date:  2018-03-12       Impact factor: 3.240

4.  Effectiveness of Serious Games for Leap Motion on the Functionality of the Upper Limb in Parkinson's Disease: A Feasibility Study.

Authors:  Edwin Daniel Oña; Carlos Balaguer; Roberto Cano-de la Cuerda; Susana Collado-Vázquez; Alberto Jardón
Journal:  Comput Intell Neurosci       Date:  2018-04-11

Review 5.  Perspective and Evolution of Gesture Recognition for Sign Language: A Review.

Authors:  Jesús Galván-Ruiz; Carlos M Travieso-González; Acaymo Tejera-Fettmilch; Alejandro Pinan-Roescher; Luis Esteban-Hernández; Luis Domínguez-Quintana
Journal:  Sensors (Basel)       Date:  2020-06-24       Impact factor: 3.576

6.  Development of a Diagnosis and Evaluation System for Hemiplegic Patients Post-Stroke Based on Motion Recognition Tracking and Analysis of Wrist Joint Kinematics.

Authors:  Subok Kim; Seoho Park; Onseok Lee
Journal:  Sensors (Basel)       Date:  2020-08-13       Impact factor: 3.576

7.  Reliability and validity of a computer game-based tool of upper extremity assessment for object manipulation tasks in children with cerebral palsy.

Authors:  Anuprita Kanitkar; Sanjay T Parmar; Tony J Szturm; Gayle Restall; Gina Rempel; Nilashri Naik; Neha Gaonkar; Nariman Sepehri; Bhavana Ankolekar
Journal:  J Rehabil Assist Technol Eng       Date:  2021-06-02

8.  Intraoperative Quantitative Measurements for Bradykinesia Evaluation during Deep Brain Stimulation Surgery Using Leap Motion Controller: A Pilot Study.

Authors:  Jingchao Wu; Ningbo Yu; Yang Yu; Haitao Li; Fan Wu; Yuchen Yang; Jianeng Lin; Jianda Han; Siquan Liang
Journal:  Parkinsons Dis       Date:  2021-06-15

9.  Usability of Videogame-Based Dexterity Training in the Early Rehabilitation Phase of Stroke Patients: A Pilot Study.

Authors:  Tim Vanbellingen; Suzanne J Filius; Thomas Nyffeler; Erwin E H van Wegen
Journal:  Front Neurol       Date:  2017-12-08       Impact factor: 4.003

10.  A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson's Disease.

Authors:  Claudia Ferraris; Roberto Nerino; Antonio Chimienti; Giuseppe Pettiti; Nicola Cau; Veronica Cimolin; Corrado Azzaro; Giovanni Albani; Lorenzo Priano; Alessandro Mauro
Journal:  Sensors (Basel)       Date:  2018-10-18       Impact factor: 3.576

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