Objectives: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise. Design: Audiometric and noise exposure data were collected on a population of screened workers (N = 1,113) from 17 factories located in Zhejiang province, China. All the subjects were exposed to complex noise. Each subject was given an otologic examination to determine their pure-tone hearing threshold levels and had their personal full-shift noise recorded. For each subject, the hearing loss was evaluated according to the hearing impairment definition of the National Institute for Occupational Safety and Health. Age, exposure duration, equivalent A-weighted SPL (LAeq), and median kurtosis were used as the input for four machine learning algorithms, that is, support vector machine, neural network multilayer perceptron, random forest, and adaptive boosting. Both classification and regression models were developed to predict noise-induced hearing loss applying these four machine learning algorithms. Two indexes, area under the curve and prediction accuracy, were used to assess the performances of the classification models for predicting hearing impairment of workers. Root mean square error was used to quantify the prediction performance of the regression models. Results: A prediction accuracy between 78.6 and 80.1% indicated that the four classification models could be useful tools to assess noise-induced hearing impairment of workers exposed to various complex occupational noises. A comprehensive evaluation using both the area under the curve and prediction accuracy showed that the support vector machine model achieved the best score and thus should be selected as the tool with the highest potential for predicting hearing impairment from the occupational noise exposures in this study. The root mean square error performance indicated that the four regression models could be used to predict noise-induced hearing loss quantitatively and the multilayer perceptron regression model had the best performance. Conclusions: This pilot study demonstrated that machine learning algorithms are potential tools for the evaluation and prediction of noise-induced hearing impairment in workers exposed to diverse complex industrial noises. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. ACKNOWLEDGMENTS: Y. Z., J. L., Y. T., and W. Q. designed and performed project; M. Z. and H. X. conducted field investigation, data collection, and quality control; Y. Z. and Y. L. analyzed the data; Y. Z. wrote the paper; and J. L., Y. T., and W. Q. provided critical revision and discussion. All authors discussed the results and implications and commented on the manuscript at all stages. We thank all reviewers and editors who helped to improve this work. This work was partially supported by Grant 200-2015-M-63857, 200-2016-M-91922 from the National Institute for Occupational Safety and Health, USA; Grant N00014-17-1-2198 from Office of Naval Research, USA; Grant 2015C03039 from Key Research and Development Program of Zhejiang Province, China; and Grant 81771936 from National Natural Science Foundation, China. The authors have no conflicts of interest to disclose. Address for correspondence: Yu Tian, Key Laboratory for Biomedical Engineering of Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China. E-mail: ty.1987823@163.com; and Wei Qiu, Auditory Research Laboratories, State University of New York at Plattsburgh, 101 Broad Street, Plattsburgh, NY 12901, USA. E-mail: qiuw@plattsburgh.edu Received December 21, 2017; accepted July 9, 2018. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
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Παρασκευή 24 Αυγούστου 2018
Objective Comparison of the Quality and Reliability of Auditory Brainstem Response Features Elicited by Click and Speech Sounds
Objectives: Auditory brainstem responses (ABRs) are commonly generated using simple, transient stimuli (e.g., clicks or tone bursts). While resulting waveforms are undeniably valuable clinical tools, they are unlikely to be representative of responses to more complex, behaviorally relevant sounds such as speech. There has been interest in the use of more complex stimuli to elicit the ABR, with considerable work focusing on the use of synthetically generated consonant–vowel (CV) stimuli. Such responses may be sensitive to a range of clinical conditions and to the effects of auditory training. Several ABR features have been documented in response to CV stimuli; however, an important issue is how robust such features are. In the current research, we use time- and frequency-domain objective measures of quality to compare the reliability of Wave V of the click-evoked ABR to that of waves elicited by the CV stimulus /da/. Design: Stimuli were presented to 16 subjects at 70 dB nHL in quiet for 6000 epochs. The presence and quality of response features across subjects were examined using Fsp and a Bootstrap analysis method, which was used to assign p values to ABR features for individual recordings in both time and frequency domains. Results: All consistent peaks identified within the /da/-evoked response had significantly lower amplitude than Wave V of the ABR. The morphology of speech-evoked waveforms varied across subjects. Mean Fsp values for several waves of the speech-evoked ABR were below 3, suggesting low quality. The most robust response to the /da/ stimulus appeared to be an offset response. Only click-evoked Wave V showed 100% wave presence. Responses to the /da/ stimulus showed lower wave detectability. Frequency-domain analysis showed stronger and more consistent activity evoked by clicks than by /da/. Only the click ABR had consistent time–frequency domain features across all subjects. Conclusions: Based on the objective analysis used within this investigation, it appears that the quality of speech-evoked ABR is generally less than that of click-evoked responses, although the quality of responses may be improved by increasing the number of epochs or the stimulation level. This may have implications for the clinical use of speech-evoked ABR. ACKNOWLEDGMENTS: Thanks to David Simpson for providing advice on bootstrap analysis. All data supporting this study are openly available from the University of Southampton repository at https://ift.tt/2PCeMMj. This research project was funded by grant No. EP/M026728/1 from the Engineering and Physical Sciences Research Council (EPSRC) and completed as part of an MSc degree funded by the Regional Medical Physics Department, Newcastle Upon Tyne NHS Foundation Trust. The authors have no conflicts of interest to disclose. Address for correspondence: Kimberley Novis, Sunderland Royal Hospital, Kayll Road, Sunderland, SR4 7TP, United Kingdom. E-mail: kimberley.novis@chsft.nhs.uk Received June 26, 2017; accepted June 6, 2018. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
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Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study
Objectives: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise. Design: Audiometric and noise exposure data were collected on a population of screened workers (N = 1,113) from 17 factories located in Zhejiang province, China. All the subjects were exposed to complex noise. Each subject was given an otologic examination to determine their pure-tone hearing threshold levels and had their personal full-shift noise recorded. For each subject, the hearing loss was evaluated according to the hearing impairment definition of the National Institute for Occupational Safety and Health. Age, exposure duration, equivalent A-weighted SPL (LAeq), and median kurtosis were used as the input for four machine learning algorithms, that is, support vector machine, neural network multilayer perceptron, random forest, and adaptive boosting. Both classification and regression models were developed to predict noise-induced hearing loss applying these four machine learning algorithms. Two indexes, area under the curve and prediction accuracy, were used to assess the performances of the classification models for predicting hearing impairment of workers. Root mean square error was used to quantify the prediction performance of the regression models. Results: A prediction accuracy between 78.6 and 80.1% indicated that the four classification models could be useful tools to assess noise-induced hearing impairment of workers exposed to various complex occupational noises. A comprehensive evaluation using both the area under the curve and prediction accuracy showed that the support vector machine model achieved the best score and thus should be selected as the tool with the highest potential for predicting hearing impairment from the occupational noise exposures in this study. The root mean square error performance indicated that the four regression models could be used to predict noise-induced hearing loss quantitatively and the multilayer perceptron regression model had the best performance. Conclusions: This pilot study demonstrated that machine learning algorithms are potential tools for the evaluation and prediction of noise-induced hearing impairment in workers exposed to diverse complex industrial noises. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. ACKNOWLEDGMENTS: Y. Z., J. L., Y. T., and W. Q. designed and performed project; M. Z. and H. X. conducted field investigation, data collection, and quality control; Y. Z. and Y. L. analyzed the data; Y. Z. wrote the paper; and J. L., Y. T., and W. Q. provided critical revision and discussion. All authors discussed the results and implications and commented on the manuscript at all stages. We thank all reviewers and editors who helped to improve this work. This work was partially supported by Grant 200-2015-M-63857, 200-2016-M-91922 from the National Institute for Occupational Safety and Health, USA; Grant N00014-17-1-2198 from Office of Naval Research, USA; Grant 2015C03039 from Key Research and Development Program of Zhejiang Province, China; and Grant 81771936 from National Natural Science Foundation, China. The authors have no conflicts of interest to disclose. Address for correspondence: Yu Tian, Key Laboratory for Biomedical Engineering of Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China. E-mail: ty.1987823@163.com; and Wei Qiu, Auditory Research Laboratories, State University of New York at Plattsburgh, 101 Broad Street, Plattsburgh, NY 12901, USA. E-mail: qiuw@plattsburgh.edu Received December 21, 2017; accepted July 9, 2018. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
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Objective Comparison of the Quality and Reliability of Auditory Brainstem Response Features Elicited by Click and Speech Sounds
Objectives: Auditory brainstem responses (ABRs) are commonly generated using simple, transient stimuli (e.g., clicks or tone bursts). While resulting waveforms are undeniably valuable clinical tools, they are unlikely to be representative of responses to more complex, behaviorally relevant sounds such as speech. There has been interest in the use of more complex stimuli to elicit the ABR, with considerable work focusing on the use of synthetically generated consonant–vowel (CV) stimuli. Such responses may be sensitive to a range of clinical conditions and to the effects of auditory training. Several ABR features have been documented in response to CV stimuli; however, an important issue is how robust such features are. In the current research, we use time- and frequency-domain objective measures of quality to compare the reliability of Wave V of the click-evoked ABR to that of waves elicited by the CV stimulus /da/. Design: Stimuli were presented to 16 subjects at 70 dB nHL in quiet for 6000 epochs. The presence and quality of response features across subjects were examined using Fsp and a Bootstrap analysis method, which was used to assign p values to ABR features for individual recordings in both time and frequency domains. Results: All consistent peaks identified within the /da/-evoked response had significantly lower amplitude than Wave V of the ABR. The morphology of speech-evoked waveforms varied across subjects. Mean Fsp values for several waves of the speech-evoked ABR were below 3, suggesting low quality. The most robust response to the /da/ stimulus appeared to be an offset response. Only click-evoked Wave V showed 100% wave presence. Responses to the /da/ stimulus showed lower wave detectability. Frequency-domain analysis showed stronger and more consistent activity evoked by clicks than by /da/. Only the click ABR had consistent time–frequency domain features across all subjects. Conclusions: Based on the objective analysis used within this investigation, it appears that the quality of speech-evoked ABR is generally less than that of click-evoked responses, although the quality of responses may be improved by increasing the number of epochs or the stimulation level. This may have implications for the clinical use of speech-evoked ABR. ACKNOWLEDGMENTS: Thanks to David Simpson for providing advice on bootstrap analysis. All data supporting this study are openly available from the University of Southampton repository at https://ift.tt/2PCeMMj. This research project was funded by grant No. EP/M026728/1 from the Engineering and Physical Sciences Research Council (EPSRC) and completed as part of an MSc degree funded by the Regional Medical Physics Department, Newcastle Upon Tyne NHS Foundation Trust. The authors have no conflicts of interest to disclose. Address for correspondence: Kimberley Novis, Sunderland Royal Hospital, Kayll Road, Sunderland, SR4 7TP, United Kingdom. E-mail: kimberley.novis@chsft.nhs.uk Received June 26, 2017; accepted June 6, 2018. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
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Verbal Agreement Inflection in German Children With Down Syndrome
Purpose
The study aims to explore whether finite verbal morphology is affected in children/adolescents with Down syndrome (DS), whether observed deficits in this domain are indicative of a delayed or deviant development, and whether they are due to phonetic/phonological problems or deficits in phonological short-term memory.
Method
An elicitation task on subject–verb agreement, a picture-naming task targeting stem-final consonants that also express verbal agreement, a nonword repetition task, and a test on grammar comprehension were conducted with 2 groups of monolingual German children: 32 children/adolescents with DS (chronological age M = 11;01 [years;months]) and a group of 16 typically developing children (chronological age M = 4;00) matched on nonverbal mental age.
Results
Analyses reveal that a substantial number of children/adolescents with DS are impaired in marking verbal agreement and fail to reach an acquisition criterion. The production of word-final consonants succeeds, however, when these consonants do not express verbal agreement. Performance with verbal agreement and nonword repetition are related.
Conclusions
Data indicate that a substantial number of children/adolescents with DS display a deficit in verbal agreement inflection that cannot be attributed to phonetic/phonological problems. The influence of phonological short-term memory on the acquisition of subject–verb agreement has to be further explored.from #Audiology via ola Kala on Inoreader https://ift.tt/2BInLst
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Verbal Agreement Inflection in German Children With Down Syndrome
Purpose
The study aims to explore whether finite verbal morphology is affected in children/adolescents with Down syndrome (DS), whether observed deficits in this domain are indicative of a delayed or deviant development, and whether they are due to phonetic/phonological problems or deficits in phonological short-term memory.
Method
An elicitation task on subject–verb agreement, a picture-naming task targeting stem-final consonants that also express verbal agreement, a nonword repetition task, and a test on grammar comprehension were conducted with 2 groups of monolingual German children: 32 children/adolescents with DS (chronological age M = 11;01 [years;months]) and a group of 16 typically developing children (chronological age M = 4;00) matched on nonverbal mental age.
Results
Analyses reveal that a substantial number of children/adolescents with DS are impaired in marking verbal agreement and fail to reach an acquisition criterion. The production of word-final consonants succeeds, however, when these consonants do not express verbal agreement. Performance with verbal agreement and nonword repetition are related.
Conclusions
Data indicate that a substantial number of children/adolescents with DS display a deficit in verbal agreement inflection that cannot be attributed to phonetic/phonological problems. The influence of phonological short-term memory on the acquisition of subject–verb agreement has to be further explored.from #Audiology via ola Kala on Inoreader https://ift.tt/2BInLst
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Dynamic response to sound and vibration of the guinea pig utricular macula, measured in vivo using Laser Doppler Vibrometry
Publication date: Available online 24 August 2018
Source: Hearing Research
Author(s): Christopher John Pastras, Ian S. Curthoys, Daniel John Brown
Abstract
With the use of a commercially available Laser Doppler Vibrometer (LDV) we have measured the velocity of the surgically exposed utricular macula in the dorsoventral plane, in anaesthetized guinea pigs, during Air Conducted Sound (ACS) or Bone Conducted Vibration (BCV) stimulation. We have also performed simultaneous measurements of otolithic function in the form of the Utricular Microphonic (UM) and the Vestibular short-latency Evoked Potential (VsEP). Based on the level of macular vibration measured with the LDV, the UM was most sensitive to ACS and BCV between 100-200Hz. The phase of the UM relative to the phase of the macular motion was relatively consistent across frequency for ACS stimulation, but varied by several cycles for BCV stimulation, suggesting a different macromechanical mode of utricular receptor activation. Moreover, unlike ACS, BCV evoked substantially distorted UM and macular vibration responses at certain frequencies, most likely due to complex resonances of the skull. Analogous to LDV studies of organ of Corti vibration, this method provides the means to study the dynamic response of the utricular macula whilst simultaneously measuring function.
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Dynamic response to sound and vibration of the guinea pig utricular macula, measured in vivo using Laser Doppler Vibrometry
Publication date: Available online 24 August 2018
Source: Hearing Research
Author(s): Christopher John Pastras, Ian S. Curthoys, Daniel John Brown
Abstract
With the use of a commercially available Laser Doppler Vibrometer (LDV) we have measured the velocity of the surgically exposed utricular macula in the dorsoventral plane, in anaesthetized guinea pigs, during Air Conducted Sound (ACS) or Bone Conducted Vibration (BCV) stimulation. We have also performed simultaneous measurements of otolithic function in the form of the Utricular Microphonic (UM) and the Vestibular short-latency Evoked Potential (VsEP). Based on the level of macular vibration measured with the LDV, the UM was most sensitive to ACS and BCV between 100-200Hz. The phase of the UM relative to the phase of the macular motion was relatively consistent across frequency for ACS stimulation, but varied by several cycles for BCV stimulation, suggesting a different macromechanical mode of utricular receptor activation. Moreover, unlike ACS, BCV evoked substantially distorted UM and macular vibration responses at certain frequencies, most likely due to complex resonances of the skull. Analogous to LDV studies of organ of Corti vibration, this method provides the means to study the dynamic response of the utricular macula whilst simultaneously measuring function.
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TMC1 Forms the Pore of Mechanosensory Transduction Channels in Vertebrate Inner Ear Hair Cells.
TMC1 Forms the Pore of Mechanosensory Transduction Channels in Vertebrate Inner Ear Hair Cells.
Neuron. 2018 Aug 22;99(4):736-753.e6
Authors: Pan B, Akyuz N, Liu XP, Asai Y, Nist-Lund C, Kurima K, Derfler BH, György B, Limapichat W, Walujkar S, Wimalasena LN, Sotomayor M, Corey DP, Holt JR
Abstract
The proteins that form the permeation pathway of mechanosensory transduction channels in inner-ear hair cells have not been definitively identified. Genetic, anatomical, and physiological evidence support a role for transmembrane channel-like protein (TMC) 1 in hair cell sensory transduction, yet the molecular function of TMC proteins remains unclear. Here, we provide biochemical evidence suggesting TMC1 assembles as a dimer, along with structural and sequence analyses suggesting similarity to dimeric TMEM16 channels. To identify the pore region of TMC1, we used cysteine mutagenesis and expressed mutant TMC1 in hair cells of Tmc1/2-null mice. Cysteine-modification reagents rapidly and irreversibly altered permeation properties of mechanosensory transduction. We propose that TMC1 is structurally similar to TMEM16 channels and includes ten transmembrane domains with four domains, S4-S7, that line the channel pore. The data provide compelling evidence that TMC1 is a pore-forming component of sensory transduction channels in auditory and vestibular hair cells.
PMID: 30138589 [PubMed - in process]
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Clinical Application of a New Approach to Identify Oral-Nasal Balance Disorders Based on Nasalance Scores.
| Related Articles |
Clinical Application of a New Approach to Identify Oral-Nasal Balance Disorders Based on Nasalance Scores.
Cleft Palate Craniofac J. 2018 Aug 22;:1055665618796012
Authors: Bettens K, de Boer G, Bressmann T, Bruneel L, Van Lierde K
Abstract
OBJECTIVE: A new approach to classify oral-nasal balance disorders based on instrumental measurements was developed based on linear discriminant analysis (LDA) of nasalance scores of simulated oral-nasal balance disorders by de Boer and Bressmann. The current study aimed to apply the newly developed functions to clinical data to investigate the applicability of this new approach.
DESIGN: Retrospective diagnostic accuracy study.
SETTING: Tertiary university hospital.
PARTICIPANTS: Fifty-five Dutch-speaking Flemish children (age 4-12 years) with normal (n = 20), hypernasal (n = 18), hyponasal (n = 12), or mixed nasality (n = 5).
INTERVENTIONS: Nasalance scores of an oral and a nasal text were used to calculate 3 sets of LDA function scores. Predicted classification was consecutively based on the function values of the group centroids originally determined by de Boer and Bressmann and adapted LDA functions and group centroids based on clinical data.
MAIN OUTCOME MEASURES: Discriminatory power of the linear discriminant formulas.
RESULTS: Based on the original LDA functions, 56% of the speech samples matched the perceptual classification. Applying a correction factor for age and language differences resulted in a 67% correct classification, although 83% of the hyponasal samples were ranked as "normal resonance." Rederivation of the LDA functions based on current clinical data resulted in an 80% correct classification.
CONCLUSIONS: The new approach of classifying oral-nasal balance disorders based on a combination of nasalance scores was promising. However, further clinical research is needed to refine the LDA functions and group centroids before clinical application is possible.
PMID: 30134743 [PubMed - as supplied by publisher]
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Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty
Publication date: Available online 24 August 2018
Source: Gait & Posture
Author(s): Felix Kluge, Julius Hannink, Cristian Pasluosta, Jochen Klucken, Heiko Gaßner, Kolja Gelse, Bjoern M. Eskofier, Sebastian Krinner
Abstract
Background
Despite the general success of total knee arthroplasty (TKA) regarding patient-reported outcome measures, studies investigating gait function have shown diverse functional outcomes. Mobile sensor-based systems have recently been employed for accurate clinical gait assessments, as they allow a better integration of gait analysis into clinical routines as compared to laboratory based systems.
Research question
In this study, we sought to examine whether an accurate assessment of gait function of knee osteoarthritis patients with respect to surgery outcome evaluation after TKA using a mobile sensor-based gait analysis system is possible.
Methods
A foot-worn sensor-based system was used to assess spatio-temporal gait parameters of 24 knee osteoarthritis patients one day before and one year after TKA, and in comparison to matched control participants. Patients were clustered into positive and negative responder groups using a heuristic approach regarding improvements in gait function. Machine learning was used to predict surgery outcome based on pre-operative gait parameters.
Results
Gait function differed significantly between controls and patients. Patient-reported outcome measures improved significantly after surgery, but no significant global gait parameter difference was observed between pre- and post-operative status. However, the responder groups could be correctly predicted with an accuracy of up to 89% using pre-operative gait parameters. Patients exhibiting high pre-operative gait function were more likely to experience a functional decrease after surgery. Important gait parameters for the discrimination were stride time and stride length.
Significance
The early identification of post-surgical functional outcomes of patients is of great importance to better inform patients pre-operatively regarding surgery success and to improve post-surgical management.
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P 123 – Does ultrasound imaging predict soleus activity during squat? A pilot study
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): I. Demirbüken, E. Timurtaş, B. Kapşigay, Z. Sarı, M.G. Polat
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Variations of handheld loads increase the range of motion of the lumbar spine without compromising local dynamic stability during walking
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): Kelsey Y. Gsell, Shawn M. Beaudette, Ivan M. Capcap, Stephen H.M. Brown
Abstract
Background
Walking is often considered a beneficial management strategy for certain populations of low back pain patients. However, little is known about how simple challenges that people often encounter, such as carrying loads in the hands, affect the low back during walking.
Research Question
How do variations in hand loading affect arm swing, lumbar spine range of motion (ROM), and lumbar spine local dynamic stability (LDS) during walking?
Methods
Sixteen young healthy participants (8 female) performed nine treadmill walking trials, each at 1.25 m/s for 3 consecutive minutes. Conditions manipulated the magnitude of hand loads (unloaded, low, high) and location of hand loads (directly in hands, in bags). Kinematic markers were used to measure sagittal plane arm swing, 3D lumbar spine ROM, and lumbar spine LDS during each trial.
Results
Arm swing was significantly (p < 0.001) reduced as load increased directly in the hands; however, when held in bags load magnitude had no effect. Further, arm swing was significantly (p < 0.0001) lower when loads were held in bags. Lumbar flexion/extension ROM was greatest with the low load compared to both unloaded (p = 0.012) and high load (p = 0.0717) conditions, and was also greater (p < 0.0001) with loads held directly in the hands compared to loads in bags. Despite these changes in lumbar spine ROM, lumbar spine LDS was not significantly affected by any of the variations in hand loading.
Significance
The greater lumbar spine cyclic motion, elicited by low hand loads held directly in the hands during walking, may be beneficial to the health of the low back. No changes in lumbar LDS were found, thereby suggesting that the small, likely beneficial, increases in lumbar spine ROM are well controlled by the motor control system and do not create an increased risk of injury.
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Analysis of the performance of 17 algorithms from a systematic review: influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): Giulia Pacini Panebianco, Maria Cristina Bisi, Rita Stagni, Silvia Fantozzi
Abstract
Background
The quantification of gait temporal parameters (i.e. step time, stance time) is crucial in human motion analysis and requires the accurate identification of gait events (i.e. heel strike, toe off). With the widespread use of inertial wearable sensors, many algorithms were proposed and applied for the purpose. Nevertheless, only few studies addressed the assessment of the actual performance of these algorithms, rather considering each proposed algorithm as a whole.
Research question
How different implementation characteristics influence the assessment of gait events and temporal parameters from inertial sensor measures in terms of accuracy and repeatability?
Methods
Seventeen different algorithms were identified from a systematic review and classified based on: 1) sensor position, 2) target variable, 3) computational approach. The influence of these characteristics was analysed on walking data of 35 healthy volunteers mounting 5 tri-axial inertial sensors. Foot contact events identified by 2 force platforms were assumed as gold standard. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of accuracy (error median value) and repeatability (error 25th and 75th percentile values).
Results
Shank- and foot-based algorithms performed better (in terms of accuracy and repeatability) in gait events detection and stance time estimation than lower trunk-based ones, while sensor position did not affect step estimate, given the error bias characteristics. Angular velocity-based algorithms performed significantly better than acceleration-based ones for toe off detection in terms of repeatability (68 ms and 102 ms, 25th-75th percentile error range, respectively) and, for heel strike detection, showed better repeatability (40 ms and 111 ms) and comparable accuracy (65 ms and 60 ms median error, respectively) than acceleration-based ones. The performance of different computational approaches varied depending on sensor positioning.
Significance
Present results support the selection of the proper algorithm for the estimation of gait events and temporal parameters in relation to the specific application.
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Validation of an Accelerometer for Measurement of Activity in Frail Older People
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): Nethra Ganesh Chigateri, Ngaire Kerse, Laurian Wheeler, Bruce MacDonald, Jochen Klenk
Abstract
Background
Specific gait parameters are associated with falls and injury. It is important to identify walking episodes in order to determine the associated gait parameters. Frail older people have a greater risk of falling due to increased probability of inactivity. Therefore, detection and analysis of their physical activities becomes significant. Furthermore, ascertainment of gait parameters and non-sedentary activities for frail older group is difficult in free living environments – an area which hasn’t been explored much.
Methods
Participants were 23 older people residing in independent-living retirement homes. Data was inertial sensor signals, attached to the L5 vertebral area using a belt, from scripted activities (a timed up and go, and sit to stand activities) and unscripted activities of daily living collected in a free-living environment. An algorithm designed to identify walking, standing/sitting and lying is applied to the uSense wearable accelerometer data which has been analysed by processing the raw data with a gait detection algorithm and the results were compared against annotated videos which served as the gold standard. Validity of gait assessment was based on the percentage of agreement between the analysed accelerometer data and the corresponding reference video with 100Hz sampling frequency and 0.01 frames/second.
Results
The median overall agreement between the processed accelerometer data and the annotated video was a match of approximately 92.8% and 95.1% for walking episodes for unscripted and scripted activities respectively.
Significance
The tri-axial accelerometer with a sampling frequency of 100 Hz provides a valid measure of gait detection in frail older people aged above 75 years. Since a limited number of studies have reported the use of accelerometers for older people in a free-living context, performance evaluation and establishing the validity of body worn sensors for physical activity and gait recognition is the key goal achieved.
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TMC1 Forms the Pore of Mechanosensory Transduction Channels in Vertebrate Inner Ear Hair Cells.
TMC1 Forms the Pore of Mechanosensory Transduction Channels in Vertebrate Inner Ear Hair Cells.
Neuron. 2018 Aug 22;99(4):736-753.e6
Authors: Pan B, Akyuz N, Liu XP, Asai Y, Nist-Lund C, Kurima K, Derfler BH, György B, Limapichat W, Walujkar S, Wimalasena LN, Sotomayor M, Corey DP, Holt JR
Abstract
The proteins that form the permeation pathway of mechanosensory transduction channels in inner-ear hair cells have not been definitively identified. Genetic, anatomical, and physiological evidence support a role for transmembrane channel-like protein (TMC) 1 in hair cell sensory transduction, yet the molecular function of TMC proteins remains unclear. Here, we provide biochemical evidence suggesting TMC1 assembles as a dimer, along with structural and sequence analyses suggesting similarity to dimeric TMEM16 channels. To identify the pore region of TMC1, we used cysteine mutagenesis and expressed mutant TMC1 in hair cells of Tmc1/2-null mice. Cysteine-modification reagents rapidly and irreversibly altered permeation properties of mechanosensory transduction. We propose that TMC1 is structurally similar to TMEM16 channels and includes ten transmembrane domains with four domains, S4-S7, that line the channel pore. The data provide compelling evidence that TMC1 is a pore-forming component of sensory transduction channels in auditory and vestibular hair cells.
PMID: 30138589 [PubMed - in process]
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Clinical Application of a New Approach to Identify Oral-Nasal Balance Disorders Based on Nasalance Scores.
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Clinical Application of a New Approach to Identify Oral-Nasal Balance Disorders Based on Nasalance Scores.
Cleft Palate Craniofac J. 2018 Aug 22;:1055665618796012
Authors: Bettens K, de Boer G, Bressmann T, Bruneel L, Van Lierde K
Abstract
OBJECTIVE: A new approach to classify oral-nasal balance disorders based on instrumental measurements was developed based on linear discriminant analysis (LDA) of nasalance scores of simulated oral-nasal balance disorders by de Boer and Bressmann. The current study aimed to apply the newly developed functions to clinical data to investigate the applicability of this new approach.
DESIGN: Retrospective diagnostic accuracy study.
SETTING: Tertiary university hospital.
PARTICIPANTS: Fifty-five Dutch-speaking Flemish children (age 4-12 years) with normal (n = 20), hypernasal (n = 18), hyponasal (n = 12), or mixed nasality (n = 5).
INTERVENTIONS: Nasalance scores of an oral and a nasal text were used to calculate 3 sets of LDA function scores. Predicted classification was consecutively based on the function values of the group centroids originally determined by de Boer and Bressmann and adapted LDA functions and group centroids based on clinical data.
MAIN OUTCOME MEASURES: Discriminatory power of the linear discriminant formulas.
RESULTS: Based on the original LDA functions, 56% of the speech samples matched the perceptual classification. Applying a correction factor for age and language differences resulted in a 67% correct classification, although 83% of the hyponasal samples were ranked as "normal resonance." Rederivation of the LDA functions based on current clinical data resulted in an 80% correct classification.
CONCLUSIONS: The new approach of classifying oral-nasal balance disorders based on a combination of nasalance scores was promising. However, further clinical research is needed to refine the LDA functions and group centroids before clinical application is possible.
PMID: 30134743 [PubMed - as supplied by publisher]
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Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty
Publication date: Available online 24 August 2018
Source: Gait & Posture
Author(s): Felix Kluge, Julius Hannink, Cristian Pasluosta, Jochen Klucken, Heiko Gaßner, Kolja Gelse, Bjoern M. Eskofier, Sebastian Krinner
Abstract
Background
Despite the general success of total knee arthroplasty (TKA) regarding patient-reported outcome measures, studies investigating gait function have shown diverse functional outcomes. Mobile sensor-based systems have recently been employed for accurate clinical gait assessments, as they allow a better integration of gait analysis into clinical routines as compared to laboratory based systems.
Research question
In this study, we sought to examine whether an accurate assessment of gait function of knee osteoarthritis patients with respect to surgery outcome evaluation after TKA using a mobile sensor-based gait analysis system is possible.
Methods
A foot-worn sensor-based system was used to assess spatio-temporal gait parameters of 24 knee osteoarthritis patients one day before and one year after TKA, and in comparison to matched control participants. Patients were clustered into positive and negative responder groups using a heuristic approach regarding improvements in gait function. Machine learning was used to predict surgery outcome based on pre-operative gait parameters.
Results
Gait function differed significantly between controls and patients. Patient-reported outcome measures improved significantly after surgery, but no significant global gait parameter difference was observed between pre- and post-operative status. However, the responder groups could be correctly predicted with an accuracy of up to 89% using pre-operative gait parameters. Patients exhibiting high pre-operative gait function were more likely to experience a functional decrease after surgery. Important gait parameters for the discrimination were stride time and stride length.
Significance
The early identification of post-surgical functional outcomes of patients is of great importance to better inform patients pre-operatively regarding surgery success and to improve post-surgical management.
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P 123 – Does ultrasound imaging predict soleus activity during squat? A pilot study
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): I. Demirbüken, E. Timurtaş, B. Kapşigay, Z. Sarı, M.G. Polat
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Variations of handheld loads increase the range of motion of the lumbar spine without compromising local dynamic stability during walking
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): Kelsey Y. Gsell, Shawn M. Beaudette, Ivan M. Capcap, Stephen H.M. Brown
Abstract
Background
Walking is often considered a beneficial management strategy for certain populations of low back pain patients. However, little is known about how simple challenges that people often encounter, such as carrying loads in the hands, affect the low back during walking.
Research Question
How do variations in hand loading affect arm swing, lumbar spine range of motion (ROM), and lumbar spine local dynamic stability (LDS) during walking?
Methods
Sixteen young healthy participants (8 female) performed nine treadmill walking trials, each at 1.25 m/s for 3 consecutive minutes. Conditions manipulated the magnitude of hand loads (unloaded, low, high) and location of hand loads (directly in hands, in bags). Kinematic markers were used to measure sagittal plane arm swing, 3D lumbar spine ROM, and lumbar spine LDS during each trial.
Results
Arm swing was significantly (p < 0.001) reduced as load increased directly in the hands; however, when held in bags load magnitude had no effect. Further, arm swing was significantly (p < 0.0001) lower when loads were held in bags. Lumbar flexion/extension ROM was greatest with the low load compared to both unloaded (p = 0.012) and high load (p = 0.0717) conditions, and was also greater (p < 0.0001) with loads held directly in the hands compared to loads in bags. Despite these changes in lumbar spine ROM, lumbar spine LDS was not significantly affected by any of the variations in hand loading.
Significance
The greater lumbar spine cyclic motion, elicited by low hand loads held directly in the hands during walking, may be beneficial to the health of the low back. No changes in lumbar LDS were found, thereby suggesting that the small, likely beneficial, increases in lumbar spine ROM are well controlled by the motor control system and do not create an increased risk of injury.
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Analysis of the performance of 17 algorithms from a systematic review: influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): Giulia Pacini Panebianco, Maria Cristina Bisi, Rita Stagni, Silvia Fantozzi
Abstract
Background
The quantification of gait temporal parameters (i.e. step time, stance time) is crucial in human motion analysis and requires the accurate identification of gait events (i.e. heel strike, toe off). With the widespread use of inertial wearable sensors, many algorithms were proposed and applied for the purpose. Nevertheless, only few studies addressed the assessment of the actual performance of these algorithms, rather considering each proposed algorithm as a whole.
Research question
How different implementation characteristics influence the assessment of gait events and temporal parameters from inertial sensor measures in terms of accuracy and repeatability?
Methods
Seventeen different algorithms were identified from a systematic review and classified based on: 1) sensor position, 2) target variable, 3) computational approach. The influence of these characteristics was analysed on walking data of 35 healthy volunteers mounting 5 tri-axial inertial sensors. Foot contact events identified by 2 force platforms were assumed as gold standard. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of accuracy (error median value) and repeatability (error 25th and 75th percentile values).
Results
Shank- and foot-based algorithms performed better (in terms of accuracy and repeatability) in gait events detection and stance time estimation than lower trunk-based ones, while sensor position did not affect step estimate, given the error bias characteristics. Angular velocity-based algorithms performed significantly better than acceleration-based ones for toe off detection in terms of repeatability (68 ms and 102 ms, 25th-75th percentile error range, respectively) and, for heel strike detection, showed better repeatability (40 ms and 111 ms) and comparable accuracy (65 ms and 60 ms median error, respectively) than acceleration-based ones. The performance of different computational approaches varied depending on sensor positioning.
Significance
Present results support the selection of the proper algorithm for the estimation of gait events and temporal parameters in relation to the specific application.
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Validation of an Accelerometer for Measurement of Activity in Frail Older People
Publication date: Available online 23 August 2018
Source: Gait & Posture
Author(s): Nethra Ganesh Chigateri, Ngaire Kerse, Laurian Wheeler, Bruce MacDonald, Jochen Klenk
Abstract
Background
Specific gait parameters are associated with falls and injury. It is important to identify walking episodes in order to determine the associated gait parameters. Frail older people have a greater risk of falling due to increased probability of inactivity. Therefore, detection and analysis of their physical activities becomes significant. Furthermore, ascertainment of gait parameters and non-sedentary activities for frail older group is difficult in free living environments – an area which hasn’t been explored much.
Methods
Participants were 23 older people residing in independent-living retirement homes. Data was inertial sensor signals, attached to the L5 vertebral area using a belt, from scripted activities (a timed up and go, and sit to stand activities) and unscripted activities of daily living collected in a free-living environment. An algorithm designed to identify walking, standing/sitting and lying is applied to the uSense wearable accelerometer data which has been analysed by processing the raw data with a gait detection algorithm and the results were compared against annotated videos which served as the gold standard. Validity of gait assessment was based on the percentage of agreement between the analysed accelerometer data and the corresponding reference video with 100Hz sampling frequency and 0.01 frames/second.
Results
The median overall agreement between the processed accelerometer data and the annotated video was a match of approximately 92.8% and 95.1% for walking episodes for unscripted and scripted activities respectively.
Significance
The tri-axial accelerometer with a sampling frequency of 100 Hz provides a valid measure of gait detection in frail older people aged above 75 years. Since a limited number of studies have reported the use of accelerometers for older people in a free-living context, performance evaluation and establishing the validity of body worn sensors for physical activity and gait recognition is the key goal achieved.
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