Using a marker-less method for estimating L5/S1 moments during symmetrical lifting

Mehrizi, Rahil; Xu, Xu; Zhang, Shaoting; Pavlović, Vladimir; Metaxas, Dimitris N.; Li, Kang

doi:10.1016/j.apergo.2017.01.007

Cited by 26 publications

(10 citation statements)

References 38 publications

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“…Generally, sending pulses with lower frequencies (LFS) guarantees less energy consumption. The blocking protocol of high and low frequency stimulation might be beneficial in the treatment of certain symptoms of PD such as postural instability, gait dysfunction and speech problems [ 29 , 77 , 78 ]. However, their low desynchronization effect has inspired this research for devising the FAS protocol with more control over the frequency of stimulation.…”

Section: Resultsmentioning

confidence: 99%

Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

2018

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The hyperkinetic symptoms of Parkinson’s Disease (PD) are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation (DBS) via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this study, we develop a reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. This model is able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Train of high frequency pulses in a delayed feedback stimulation has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitudes needed. The efficiency of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behavior observed in neurological disorders.

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Section: Resultsmentioning

confidence: 99%

Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

2018

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“…In the paper Diego-Mas and Alcaide-Marzal [16], skeleton data from Kinect V2 are used for assessing postural loads based on OWAS [25]. In Mehrizi et al [33,34], the authors adopt pose estimation techniques to estimate skeleton data from RGB images for calculating the low back (L5/S1) loading during lifting tasks. Their methods are designed for non-real time and controlled scenarios where the subjects have specific wearings, and their postures are symmetric.…”

Section: Related Workmentioning

confidence: 99%

“…In general, posture assessment usually follows the approach "pose estimation + regression" to identify risks of postures [16,33,34], and the pose could be estimated from RGB images or Kinect sensors. There are two main problems with this approach.…”

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confidence: 99%

A real-time webcam-based method for assessing upper-body postures

Ding

Ogunbona

et al. 2019

Machine Vision and Applications

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This paper presents a new vision-based method for real-time assessment of upper-body postures of a subject who is sitting in front of a desk studying or operating a computer. Unlike most existing vision-based methods that perform offline assessment from human skeletons extracted from RGB video or depth maps, the proposed method analyses directly single images captured by a webcam in front of the subject without the prone-to-error process of extracting the skeleton data from the images or depth maps. To this end, this paper proposes to assess postures by classifying them into predefined classes, without explicitly measuring the variables required for calculating risk scores. Each class of postures is associated with a configuration of the upper body, and an ergonomics risk score is assigned by following one of the scoring methods, e.g. Rapid Upper Limb Assessment (RULA). A data set of upper-body postures that cover the various scenarios when a subject is sitting in front of a desk as well as some extreme cases when the subject turns away from the desk is collected for evaluating the proposed method quantitatively. The proposed method achieved an on-average accuracy of 99.5% for binary classification (low-vs. high-risk postures), 88.2% for classification of 19 risk levels and 81.5% for classification of 30 risk levels on the data set, and the demo developed based on the method runs in real time on a regular computer. Keywords Posture assessment • Upper body • HOG • SVM • RULA 1 Introduction Posture refers to the arrangement of body segments/parts at a particular time [18], and it can be healthy (good) or harmful (poor). Poor posture increases the risk of work-related musculoskeletal disorders (WMSDs), such as carpal tunnel syndrome (CTS), back injury and back pain, and arthritis. WMSDs are associated with high costs to both employers and the whole society and are more severe than the average non-fatal injury or illness: nearly, one in every two Americans B Zewei Ding

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“…There are few studies which explored the field of computer vision and proposed marker-less methods for biomechanical and clinical applications. In particular, [9], [10] proposed a computer vision based method for estimation of 3D pose estimation and lower back loads in the symmetrical lifting tasks. In another study by [11], a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm was employed to estimate human motion through a markerless motion capture system.…”

Section: A Pose Estimation For Biomechanical Applicationmentioning

confidence: 99%

Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution

Mehrizi

Peng

Tang

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

Self Cite

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Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, timeconsuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a "view-specific perceptron" network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a "multi-view integration" network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of 14.72 ± 2.96 mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva-I dataset [1], which demonstrates the superior performance of our approach.

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Using a marker-less method for estimating L5/S1 moments during symmetrical lifting

Cited by 26 publications

References 38 publications

Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

A real-time webcam-based method for assessing upper-body postures

Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution

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