Literature DB >> 27305689

Quantifying the Consistency of Wearable Knee Acoustical Emission Measurements During Complex Motions.

Hakan Toreyin, Hyeon Ki Jeong, Sinan Hersek, Caitlin N Teague, Omer T Inan.   

Abstract

Knee-joint sounds could potentially be used to noninvasively probe the physical and/or physiological changes in the knee associated with rehabilitation following acute injury. In this paper, a system and methods for investigating the consistency of knee-joint sounds during complex motions in silent and loud background settings are presented. The wearable hardware component of the system consists of a microelectromechanical systems microphone and inertial rate sensors interfaced with a field programmable gate array-based real-time processor to capture knee-joint sound and angle information during three types of motion: flexion-extension (FE), sit-to-stand (SS), and walking (W) tasks. The data were post-processed to extract high-frequency and short-duration joint sounds (clicks) with particular waveform signatures. Such clicks were extracted in the presence of three different sources of interference: background, stepping, and rubbing noise. A histogram-vector Vn(→) was generated from the clicks in a motion-cycle n, where the bin range was 10°. The Euclidean distance between a vector and the arithmetic mean Vav(→) of all vectors in a recording normalized by the Vav(→) is used as a consistency metric dn. Measurements from eight healthy subjects performing FE, SS, and W show that the mean (of mean) consistency metric for all subjects during SS (μ [ μ (dn)] = 0.72 in silent, 0.85 in loud) is smaller compared with the FE (μ [ μ (dn)] = 1.02 in silent, 0.95 in loud) and W ( μ [ μ (dn)] = 0.94 in silent, 0.97 in loud) exercises, thereby implying more consistent click-generation during SS compared with the FE and W. Knee-joint sounds from one subject performing FE during five consecutive work-days (μ [ μ (dn) = 0.72) and five different times of a day (μ [ μ (dn) = 0.73) suggests high consistency of the clicks on different days and throughout a day. This work represents the first time, to the best of our knowledge, that joint sound consistency has been quantified in ambulatory subjects performing every-day activities (e.g., SS, walking). Moreover, it is demonstrated that noise inherent with joint-sound recordings during complex motions in uncontrolled settings does not prevent joint-sound-features from being detected successfully.

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Year:  2016        PMID: 27305689     DOI: 10.1109/JBHI.2016.2579610

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  12 in total

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Review 2.  Wearable knee health system employing novel physiological biomarkers.

Authors:  Omer T Inan; Daniel C Whittingslow; Caitlin N Teague; Sinan Hersek; Maziyar Baran Pouyan; Mindy Millard-Stafford; Geza F Kogler; Michael N Sawka
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3.  Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.

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4.  Quantifying the Effects of Increasing Mechanical Stress on Knee Acoustical Emissions Using Unsupervised Graph Mining.

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5.  A Feasibility Study on Tribological Origins of Knee Acoustic Emissions.

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Review 7.  Noise around the Knee.

Authors:  Sang Jun Song; Cheol Hee Park; Hu Liang; Sang Jun Kim
Journal:  Clin Orthop Surg       Date:  2018-02-27

8.  A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation.

Authors:  Nicholas B Bolus; Hyeon Ki Jeong; Daniel C Whittingslow; Omer T Inan
Journal:  Sensors (Basel)       Date:  2019-06-13       Impact factor: 3.576

9.  Use of acoustic emission to identify novel candidate biomarkers for knee osteoarthritis (OA).

Authors:  Daniela K Schlüter; Lucy Spain; Wei Quan; Harry Southworth; Nicola Platt; Joe Mercer; Lik-Kwan Shark; John C Waterton; Mike Bowes; Peter J Diggle; Mandy Dixon; Jane Huddleston; John Goodacre
Journal:  PLoS One       Date:  2019-10-16       Impact factor: 3.240

10.  A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health.

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Journal:  Sensors (Basel)       Date:  2018-09-06       Impact factor: 3.576

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