Literature DB >> 34372404

Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review.

Nicky Baker1, Claire Gough1, Susan J Gordon1.   

Abstract

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls.

Entities:  

Keywords:  inertial measurement unit; postural balance

Year:  2021        PMID: 34372404     DOI: 10.3390/s21155167

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

1.  Balance performance analysis after the COVID-19 quarantine in children aged between 8 and 12 years old: Longitudinal study.

Authors:  Vicenta Martínez-Córcoles; Pilar Nieto-Gil; Laura Ramos-Petersen; Javier Ferrer-Torregrosa
Journal:  Gait Posture       Date:  2022-03-27       Impact factor: 2.746

2.  Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor.

Authors:  Kittichai Wantanajittikul; Chakrit Wiboonsuntharangkoon; Busaba Chuatrakoon; Siriphan Kongsawasdi
Journal:  ScientificWorldJournal       Date:  2022-06-22
  2 in total

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