Accelerometry - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Accelerometry

The Experts below are selected from a list of 13374 Experts worldwide ranked by ideXlab platform

Tom Chau – One of the best experts on this subject based on the ideXlab platform.

  • Automatic discrimination between cough and non-cough Accelerometry signal artefacts
    Biomedical Signal Processing and Control, 2019
    Co-Authors: Helia Mohammadi, Catriona M Steele, Ali-akbar Samadani, Tom Chau

    Abstract:

    Abstract Cough is the forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus, and may be symptomatic of respiratory conditions and swallowing difficulties. The evaluation of cough severity can thus inform appropriate treatment of the underlying issue, but assessment has historically been subjective (e.g., self-report) and episodic. Automatic cough detection has been proposed as a tool for long-term (e.g., nocturnal) monitoring of cough intensity and frequency, but the rejection of non-cough activities remains an elusive challenge. Cervical Accelerometry is a novel tool, which measures oropharyngeal and laryngopharyngeal vibrations associated with swallowing, coughing, tongue movements and speech. We propose an automatic cough detection system that discriminates Accelerometry signals associated with coughs from those representing swallows, tongue movements and speech. We consider both voluntary and reflexive coughs. Accelerometry signals were represented in term of time-frequency meta features. Using the binary genetic feature selection algorithm and a support vector machine classifier, the proposed system achieved a cough detection accuracy of 99.26 ± 0.12% when discriminating between voluntary cough and rest Accelerometry signals. An accuracy of 90 ± 13.9% was achieved using elastic net feature selection and support vector machine when classifying involuntary coughs and rest signals. When discriminating between voluntary and involuntary cough versus all other non-cough artefacts, the proposed system achieved accuracies of 90.2 ± 3.6% and 80.3 ± 10.5%, respectively. Compared to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders, the proposed method requires only a single accelerometer.

  • Classification of Penetration–Aspiration Versus Healthy Swallows Using Dual-Axis Swallowing Accelerometry Signals in Dysphagic Subjects
    IEEE Transactions on Biomedical Engineering, 2013
    Co-Authors: Ervin Sejdić, Catriona M Steele, Tom Chau

    Abstract:

    Swallowing Accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing Accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing Accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing Accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing Accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.

  • Noninvasive Detection of Thin-Liquid Aspiration Using Dual-Axis Swallowing Accelerometry
    Dysphagia, 2012
    Co-Authors: Catriona M Steele, Ervin Sejdic, Tom Chau

    Abstract:

    Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making noninstrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study we investigated the validity of a noninvasive Accelerometry signal-processing classifier for detecting aspiration. Dual-axis cervical Accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal-processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the Accelerometry classifier had a 10 % false-negative rate (90 % sensitivity) and a 23 % false-positive rate (77 % specificity) for detecting penetration-aspiration. We conclude that Accelerometry can be used to support valid, reliable, and efficient detection of aspiration risk in patients with suspected dysphagia.

Nigel H. Lovell – One of the best experts on this subject based on the ideXlab platform.

  • Spectral Analysis of Accelerometry Signals From a Directed-Routine for Falls-Risk Estimation
    IEEE Transactions on Biomedical Engineering, 2011
    Co-Authors: Stephen J. Redmond, Ning Wang, Michael R. Narayanan, Fernando Blumenkron, Nigel H. Lovell

    Abstract:

    Injurious falls are a prevalent and serious problem faced by a growing elderly population. Accurate assessment and long-term monitoring of falls-risk could prove useful in the prevention of falls, by identifying those at risk of falling early so targeted intervention may be prescribed. Previous studies have demonstrated the feasibility of using triaxial Accelerometry to estimate the risk of a person falling in the near future, by characterizing their movement as they execute a restricted sequence of predefined movements in an unsupervised environment, termed a directed routine. This study presents an improvement on this previously published system, which relied explicitly on time-domain features extracted from the Accelerometry signals. The proposed improvement incorporates features derived from spectral analysis of the same Accelerometry signals; in particular the harmonic ratios between signal harmonics and the fundamental frequency component are used. Employing these additional frequency-domain features, in combination with the previously reported time-domain features, an increase in the observed correlation with the clinical gold-standard risk of falling, from ρ = 0.81 to ρ = 0.96, was achieved when using manually annotated event segmentation markers; using an automated algorithm to segment the signals gave corresponding results of ρ = 0.73 and ρ = 0.99, before and after the inclusion of spectral features. The strong correlation with falls-risk observed in this preliminary study further supports the feasibility of using an unsupervised assessment of falls-risk in the home environment.

  • Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010
    Co-Authors: Federico Bianchi, Stephen J. Redmond, Michael R. Narayanan, Sergio Cerutti, Nigel H. Lovell

    Abstract:

    Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable Accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject’s waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing Accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using Accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.

  • Energy expenditure estimation using triaxial Accelerometry and barometric pressure measurement
    2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
    Co-Authors: Matteo Voleno, Sergio Cerutti, Stephen J. Redmond, Nigel H. Lovell

    Abstract:

    Energy expenditure (EE) is a parameter of great relevance in studies involving the assessment of physical activity. However, most reliable techniques for EE estimation are impractical for use in free-living environments, and those which are practically useful often poorly track EE when the subject is working to change their altitude, for example when ascending or descending stairs or slopes. The aim of this study is to evaluate the utility of adding barometric pressure related features, as a surrogate measure for altitude, to existing Accelerometry related features to estimate the subject’s EE. The EE estimation system described is based on a triaxial accelerometer (triax) and a barometric pressure sensor. The device is wireless, with Bluetooth connectivity for data retrieval, and is mounted at the subject’s waist. Using a number of features extracted from the triax and barometric pressure signals, a linear model is trained for EE estimation. This EE estimation model is compared to its counterpart, which solely utilizes Accelerometry signals. A protocol (comprising lying, sitting, standing, walking phases) was performed by 13 healthy volunteers (8 male and 5 female; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system. The model incorporating barometric pressure information estimated the oxygen uptake with the lowest mean square error of 4.5±1.7 (mlO2.min-1.kg-1)2, in comparison to 7.1±2.3 (mlO2.min-1.kg-1)2 using only Accelerometry-based features.

Catriona M Steele – One of the best experts on this subject based on the ideXlab platform.

  • Automatic discrimination between cough and non-cough Accelerometry signal artefacts
    Biomedical Signal Processing and Control, 2019
    Co-Authors: Helia Mohammadi, Catriona M Steele, Ali-akbar Samadani, Tom Chau

    Abstract:

    Abstract Cough is the forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus, and may be symptomatic of respiratory conditions and swallowing difficulties. The evaluation of cough severity can thus inform appropriate treatment of the underlying issue, but assessment has historically been subjective (e.g., self-report) and episodic. Automatic cough detection has been proposed as a tool for long-term (e.g., nocturnal) monitoring of cough intensity and frequency, but the rejection of non-cough activities remains an elusive challenge. Cervical Accelerometry is a novel tool, which measures oropharyngeal and laryngopharyngeal vibrations associated with swallowing, coughing, tongue movements and speech. We propose an automatic cough detection system that discriminates Accelerometry signals associated with coughs from those representing swallows, tongue movements and speech. We consider both voluntary and reflexive coughs. Accelerometry signals were represented in term of time-frequency meta features. Using the binary genetic feature selection algorithm and a support vector machine classifier, the proposed system achieved a cough detection accuracy of 99.26 ± 0.12% when discriminating between voluntary cough and rest Accelerometry signals. An accuracy of 90 ± 13.9% was achieved using elastic net feature selection and support vector machine when classifying involuntary coughs and rest signals. When discriminating between voluntary and involuntary cough versus all other non-cough artefacts, the proposed system achieved accuracies of 90.2 ± 3.6% and 80.3 ± 10.5%, respectively. Compared to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders, the proposed method requires only a single accelerometer.

  • Classification of Penetration–Aspiration Versus Healthy Swallows Using Dual-Axis Swallowing Accelerometry Signals in Dysphagic Subjects
    IEEE Transactions on Biomedical Engineering, 2013
    Co-Authors: Ervin Sejdić, Catriona M Steele, Tom Chau

    Abstract:

    Swallowing Accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing Accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing Accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing Accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing Accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.

  • Noninvasive Detection of Thin-Liquid Aspiration Using Dual-Axis Swallowing Accelerometry
    Dysphagia, 2012
    Co-Authors: Catriona M Steele, Ervin Sejdic, Tom Chau

    Abstract:

    Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making noninstrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study we investigated the validity of a noninvasive Accelerometry signal-processing classifier for detecting aspiration. Dual-axis cervical Accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal-processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the Accelerometry classifier had a 10 % false-negative rate (90 % sensitivity) and a 23 % false-positive rate (77 % specificity) for detecting penetration-aspiration. We conclude that Accelerometry can be used to support valid, reliable, and efficient detection of aspiration risk in patients with suspected dysphagia.