Literature DB >> 27210852

A reliability study of the new sensors for movement analysis (SHARIF-HMIS).

Mohen Abedi1, Farideh Dehghan Manshadi2, Minoo Khalkhali Zavieh1, Sajad Ashouri3, Hadi Azimi4, Mohamad Parnanpour5.   

Abstract

AIM: SHARIF-HMIS is a new inertial sensor designed for movement analysis. The aim of the present study was to assess the inter-tester and intra-tester reliability of some kinematic parameters in different lumbar motions making use of this sensor.
MATERIALS AND METHODS: 24 healthy persons and 28 patients with low back pain participated in the current reliability study. The test was performed in five different lumbar motions consisting of lumbar flexion in 0, 15, and 30° in the right and left directions. For measuring inter-tester reliability, all the tests were carried out twice on the same day separately by two physiotherapists. Intra-tester reliability was assessed by reproducing the tests after 3 days by the same physiotherapist.
FINDINGS: The present study revealed satisfactory inter- and intra-tester reliability indices in different positions. ICCs for intra-tester reliability ranged from 0.65 to 0.98 and 0.59 to 0.81 for healthy and patient participants, respectively. Also, ICCs for inter-tester reliability ranged from 0.65 to 0.92 for the healthy and 0.65 to 0.87 for patient participants.
CONCLUSION: In general, it can be inferred from the results that measuring the kinematic parameters in lumbar movements using inertial sensors enjoys acceptable reliability.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Inertial sensor; Inter-tester reliability; Intra-tester reliability; Kinematic parameters; SHARIF–HMIS

Mesh:

Year:  2015        PMID: 27210852     DOI: 10.1016/j.jbmt.2015.10.004

Source DB:  PubMed          Journal:  J Bodyw Mov Ther        ISSN: 1360-8592


  3 in total

1.  A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings.

Authors:  Mehrdad Davoudi; Seyyed Mohammadreza Shokouhyan; Mohsen Abedi; Narges Meftahi; Atefeh Rahimi; Ehsan Rashedi; Maryam Hoviattalab; Roya Narimani; Mohamad Parnianpour; Kinda Khalaf
Journal:  Sensors (Basel)       Date:  2020-05-20       Impact factor: 3.576

2.  Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.

Authors:  Masoud Abdollahi; Sajad Ashouri; Mohsen Abedi; Nasibeh Azadeh-Fard; Mohamad Parnianpour; Kinda Khalaf; Ehsan Rashedi
Journal:  Sensors (Basel)       Date:  2020-06-26       Impact factor: 3.576

3.  Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients.

Authors:  Paul Thiry; Olivier Nocent; Fabien Buisseret; William Bertucci; André Thevenon; Emilie Simoneau-Buessinger
Journal:  Entropy (Basel)       Date:  2022-03-22       Impact factor: 2.738

  3 in total

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