ORCID Profile
0000-0001-9139-8955
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 03-2018
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 11-2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: ACM
Date: 25-12-2020
Publisher: Elsevier BV
Date: 05-2015
Publisher: Cold Spring Harbor Laboratory
Date: 03-11-2022
DOI: 10.1101/2022.10.30.22281728
Abstract: Heart rate variability (HRV) is the reflection of physiological effects modulating heart rhythm. In particular, spectral HRV metrics provide valuable information to investigate activities of the cardiac autonomic nervous system. However, uncertainties and artifacts from measurements can reduce signal quality and therefore affect the evaluation of HRV measures. In this paper, we propose a new method for HRV spectrum estimation with measurement uncertainties using matrix completion (MC). We show that missing values of HRV spectrum can be efficiently estimated using the MC method by leveraging the low rank property of the spectrum matrix. In addition, we proposed a refined matrix completion (RMC) method to improve the estimation accuracy and computational efficiency by introducing model information for the HRV spectrum. Experimental studies on five public benchmark datasets show the effectiveness and robustness of the developed RMC method for estimating missing entries for HRV spectrum with different masking ratios. Furthermore, our developed RMC method is compared with five deep learning models and the traditional MC method the results of this comparison study demonstrate that our developed RMC method obtains the least estimation error with the minimal computation cost, indicating the advantages of our developed method for HRV spectrum estimation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Elsevier BV
Date: 05-2014
Publisher: Elsevier BV
Date: 2016
Publisher: Trans Tech Publications, Ltd.
Date: 07-2011
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.80-81.875
Abstract: The detection and diagnosis of equipment failures are of great practical significance and paramount importance in the sense that an early detection of these faults may help to avoid performance degradation and major damage. In this work, a novel methodology based on improved Hilbert-Huang transform (HHT) and support vector machine (SVM) was proposed for incipient bearing fault diagnosis with insufficient training data. Singular value decomposition (SVD) was employed to detect periodic features, and then extending of the original signal was carried out based on support vector regression (SVR). A screening process was conducted to select the vital intrinsic mode functions (IMFs). Finally, features extracted from the obtained IMFs were applied to identify different bearing faults based on SVM. To investigate the property of proposed method, an experimental test rig was designed such that varying sizes defects of a test bearing could be seeded, and it’s concluded that the effectiveness of the proposed algorithm in early bearing fault diagnosis even with insufficient training data.
Publisher: Cold Spring Harbor Laboratory
Date: 16-04-2020
DOI: 10.1101/2020.04.15.043919
Abstract: Evaluating progress throughout a patient’s rehabilitation episode is critical for determining effectiveness of the selected treatments and contributing to the evidence-based practice. The evaluation process is complex due to the inherent large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients’ progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that can reflect monotonicity and trendability is proposed to evaluate the suitability of kinematic features, which are derived from the collected data of a population of stroke patients participating in robot-aided rehabilitation. The selected kinematic features allow for human variations across a population of patients as well as over the sequence of rehabilitation sessions. The study is based on data records pertaining to 41 stroke patients using three different robot assisted exercises for upper limb rehabilitation. Consistent with the literature, the results indicate that features based on movement smoothness are the best measures among 17 kinematic features used to evaluate rehabilitation progress.
Publisher: Elsevier BV
Date: 12-2016
Publisher: IEEE
Date: 07-2018
Publisher: ASME International
Date: 31-08-2018
DOI: 10.1115/1.4041180
Abstract: Cutting tool rotation errors have significant influence on the machined surface quality, especially in micromilling. Precision metrology instruments are usually needed to measure the rotation error accurately. However, it is difficult to directly measure the axial error of micromilling tools due to the small diameters and ultra-high rotational speed. To predict the axial error of high speed milling tools in the actual machining conditions and avoid the use of expensive metrology instruments, a novel method is proposed in this paper to quantify the cutting tool error in the axial direction based on the tool marks generated on the machined surface. A numerical model is established to simulate the surface topography generation, and the relationship between tool marks and the cutting tool axial error is then investigated. The tool axial errors at different rotational speeds can be detected by the proposed method. The accuracy and the reliability of the proposed method are verified by machining experiments.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: OAE Publishing Inc.
Date: 2023
Publisher: Elsevier BV
Date: 2016
Publisher: IOP Publishing
Date: 26-07-2023
Publisher: IEEE
Date: 27-09-2022
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: IEEE
Date: 08-2010
Publisher: Frontiers Media SA
Date: 06-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Cold Spring Harbor Laboratory
Date: 12-03-2020
DOI: 10.1101/2020.03.11.986653
Abstract: A forward head and rounded shoulder posture is a poor posture that is widely seen in everyday life. It is known that sitting in such a poor posture with long hours will bring health issues such as muscle pain. However, it is not known whether sitting in this poor posture for a short period of time will affect human activities. This paper investigates the effects of a shortduration poor posture before some typical physical activities such as push-ups. The experiments are set up as follows. Fourteen male subjects are asked to do push-ups until fatigue with two surface electromyography (sEMG) at the upper limb. Two days later, they are asked to sit in this poor posture for 15 mins with 8 sEMG sensors located at given back muscles. Then they do the push-ups after the short-duration poor posture. The observations from the median frequency of sEMG signals at the upper limb indicate that the short-duration poor posture does affect the fatigue procedure of push-ups. A significant decreasing trend of the performance of push-ups is obtained after sitting in this poor posture. Such effects indicate that some parts of the back muscles indeed get fatigued with only 15 minutes sitting in this poor posture. By further investigating the time-frequency components of sEMG of back muscles, it is observed that the low and middle frequencies of sEMG signals from the infraspinatus muscle of the dominant side are demonstrated to be more prone to fatigue with the poor posture. Although this study focuses only on push-ups, similar experiments can be arranged for other physical exercises as well. This study provides new insights into the effect of a short-duration poor posture before physical activities. These insights can be used to guide athletes to pay attention to postures before physical activities to improve performance and reduce the risk of injury.
Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.3390/BS13030243
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Lei Lu.