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Ann Biomed Eng. 2017 Dec;45(12):2784-2793. doi: 10.1007/s10439-017-1911-8. Epub 2017 Aug 30.

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

Author information

1
Department of Electrical & Computer Engineering, Temple University, Philadelphia, PA, 19122, USA. a.napoli@temple.edu.
2
Department of Physical Therapy, Temple University, Philadelphia, PA, 19140, USA.
3
Department of Electrical & Computer Engineering, Temple University, Philadelphia, PA, 19122, USA.

Abstract

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0.05. Despite some level of disagreement between human and AAPS-generated scores, the use of an automated system yields important advantages over currently available human-based alternatives. These results underscore the value of using the AAPS, that can be quickly deployed in the field and/or in outdoor settings with minimal set-up time. Finally, the AAPS can record multiple error types and their time course with extremely high temporal resolution. These features are not achievable by humans, who cannot keep track of multiple balance errors with such a high resolution. Together, these results suggest that computerized BESS calculation may provide more accurate and consistent measures of balance than those derived from human experts.

KEYWORDS:

Automated BEES; Automatic balance error scoring detection; Concussion detection; Field-expedient balance test; Kinect; Mild traumatic brain injury; On-field automatic balance detection; Return-to-duty evaluation

PMID:
28856486
DOI:
10.1007/s10439-017-1911-8
[Indexed for MEDLINE]

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