DECODING OF SIMPLE HAND MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROMYOGRAPHY (EMG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (04) ◽  
pp. 1950042 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI

Analysis of body movement is the most important aspect of rehabilitation science. Hand movement as one of the major movements of humans has aroused the attention of many researchers. For this purpose, decoding of movements by analysis of the related bio signals is very important. In this research, complexity analysis of Electromyography (EMG) signal that was recorded due to simple hand movements is done. For this purpose, we employ fractal dimension as the indicator of complexity of signal in this research. The EMG signal was recorded from subjects while they did six simple hand movements and accordingly we applied fractal analysis on the signal. The result of our analysis showed that the EMG signal has the greatest and lowest fractal dimension in case of lateral (for holding thin, flat objects) and hook (for supporting a heavy load) hand movements. The capability seen in this research can be applied to the analysis of other types of bio signals in order to investigate the reaction of humans to different types of stimuli.

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950022 ◽  
Author(s):  
HAMIDREZA NAMAZI

One of the major research areas in analysis of human movements is to investigate how different movements are related to biosignals. Hand gestures belong to major movements of human that have been considered widely by researchers. Therefore, decoding of different hand’s gestures by analysis of related biosignal is very important to be considered. In this paper, we analyze the complex structure of electromyography (EMG) signal from subjects who did eight hand gestures. For this purpose, we chose fractal dimension as the indicator of complexity. The analysis showed that the EMG signal has the greatest and lowest fractal dimensions in case of fingers flexed together in fist, and pointing index, respectively. The employed method in this research is not limited to the analysis of the influence of hand’s gestures on EMG signal. However, it can be widely applied to analyze the influence of different types of stimuli on different human’s biosignals.


Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950037 ◽  
Author(s):  
HAMIDREZA NAMAZI

Investigating human movements is the most important issue in rehabilitation science. Movements of fingers as one of the major movements of human has been considered by many scientists. Therefore, decoding of finger movements by analysis of related biosignal is very important to consider. In this research, we do the complexity analysis on the Electromyography (EMG) signal that was recorded due to basic movements of fingers. In fact, the EMG signal was classified in case of different movements of fingers by fractal analysis. The result of analysis showed that the EMG signal has the greatest and lowest fractal dimension (complexity) in case of thumb finger flexion and little finger extension. In further attempts, the fractal theory can be applied to investigate the influence of other types of stimulation on variations of the complexity of muscles’ reactions.


2015 ◽  
Vol 77 (7) ◽  
Author(s):  
Abdul Qayoom ◽  
Abdul Wahab ◽  
Norhaslinda Kamaruddin ◽  
Zahid Zahid

EEG data contamination due to artifacts, such as eye blink, muscle activity, body movement and others pose as an issue in EEG analysis. This study aims to classify three different types of artifacts in EEG signal, namely; ocular, facial muscle and hand movement using statistical features coupled with neural networks as classifier. Temporal averages of five features are used as the feature vector for MLP classification. The experimental results for ocular, facial muscle and hand movement artifacts identification are ranging between 80% and 92%. The classification accuracy for the combination of these EEG artifacts and normal EEG of the subject for resting and eyes-close state are 86% and 96% respectively


Fractals ◽  
2001 ◽  
Vol 09 (02) ◽  
pp. 155-163 ◽  
Author(s):  
BOMING YU ◽  
L. JAMES LEE ◽  
HANQIANG CAO

It is found that the pore microstructures of textile fabrics, widely used in the manufacture of fiber-reinforced composites, exhibit the fractal characters. The fractal behaviors are described by the proposed analytical method and measured by the box-counting method for the three different types of textile fabrics: plain woven, four-harness, bidirectional-stitched fiberglass mats. The pore area fractal dimension is derived analytically and found to be the function of the porosity and architectural parameters of fabrics. The results indicate that the fractal characters are isotropic although the fabrics are rothotropic in structures. The theoretical predictions by the proposed analytical model are in good agreement with those from the box-counting method, and this verifies the proposed fractal dimension model. The present fractal analysis may have the potential and significance on fractal analysis of transport properties (such as the permeability, dispersion, thermal and mechanical properties) in porous media.


2021 ◽  
Vol 7 (2) ◽  
pp. 15
Author(s):  
Tomohiro Shimizu ◽  
Ryo Hachiuma ◽  
Hiroki Kajita ◽  
Yoshifumi Takatsume ◽  
Hideo Saito

Detecting surgical tools is an essential task for the analysis and evaluation of surgical videos. However, in open surgery such as plastic surgery, it is difficult to detect them because there are surgical tools with similar shapes, such as scissors and needle holders. Unlike endoscopic surgery, the tips of the tools are often hidden in the operating field and are not captured clearly due to low camera resolution, whereas the movements of the tools and hands can be captured. As a result that the different uses of each tool require different hand movements, it is possible to use hand movement data to classify the two types of tools. We combined three modules for localization, selection, and classification, for the detection of the two tools. In the localization module, we employed the Faster R-CNN to detect surgical tools and target hands, and in the classification module, we extracted hand movement information by combining ResNet-18 and LSTM to classify two tools. We created a dataset in which seven different types of open surgery were recorded, and we provided the annotation of surgical tool detection. Our experiments show that our approach successfully detected the two different tools and outperformed the two baseline methods.


2000 ◽  
Vol 39 (02) ◽  
pp. 37-42 ◽  
Author(s):  
P. Hartikainen ◽  
J. T. Kuikka

Summary Aim: We demonstrate the heterogeneity of regional cerebral blood flow using a fractal approach and singlephoton emission computed tomography (SPECT). Method: Tc-99m-labelled ethylcysteine dimer was injected intravenously in 10 healthy controls and in 10 patients with dementia of frontal lobe type. The head was imaged with a gamma camera and transaxial, sagittal and coronal slices were reconstructed. Two hundred fifty-six symmetrical regions of interest (ROIs) were drawn onto each hemisphere of functioning brain matter. Fractal analysis was used to examine the spatial heterogeneity of blood flow as a function of the number of ROIs. Results: Relative dispersion (= coefficient of variation of the regional flows) was fractal-like in healthy subjects and could be characterized by a fractal dimension of 1.17 ± 0.05 (mean ± SD) for the left hemisphere and 1.15 ± 0.04 for the right hemisphere, respectively. The fractal dimension of 1.0 reflects completely homogeneous blood flow and 1.5 indicates a random blood flow distribution. Patients with dementia of frontal lobe type had a significantly lower fractal dimension of 1.04 ± 0.03 than in healthy controls. Conclusion: Within the limits of spatial resolution of SPECT, the heterogeneity of brain blood flow is well characterized by a fractal dimension. Fractal analysis may help brain scientists to assess age-, sex- and laterality-related anatomic and physiological changes of brain blood flow and possibly to improve precision of diagnostic information available for patient care.


2005 ◽  
Vol 1 (1) ◽  
pp. 21-24
Author(s):  
Hamid Reza Samadi

In exploration geophysics the main and initial aim is to determine density of under-research goals which have certain density difference with the host rock. Therefore, we state a method in this paper to determine the density of bouguer plate, the so-called variogram method based on fractal geometry. This method is based on minimizing surface roughness of bouguer anomaly. The fractal dimension of surface has been used as surface roughness of bouguer anomaly. Using this method, the optimal density of Charak area insouth of Hormozgan province can be determined which is 2/7 g/cfor the under-research area. This determined density has been used to correct and investigate its results about the isostasy of the studied area and results well-coincided with the geology of the area and dug exploratory holes in the text area


2020 ◽  
Vol 132 (5) ◽  
pp. 1358-1366
Author(s):  
Chao-Hung Kuo ◽  
Timothy M. Blakely ◽  
Jeremiah D. Wander ◽  
Devapratim Sarma ◽  
Jing Wu ◽  
...  

OBJECTIVEThe activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.METHODSThree neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.RESULTSIn all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).CONCLUSIONSHG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.


2021 ◽  
Vol 11 (5) ◽  
pp. 2376
Author(s):  
Sam Yu ◽  
Vasudevan Lakshminarayanan

Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool. This review aims to summarize the current scientific evidence regarding the relationship between fractal dimension and retinal pathology and thus assess the clinical value of retinal fractal dimension. Following the PRISMA guidelines, a literature search for research articles was conducted in several internet databases (EMBASE, MEDLINE, Web of Science, Scopus). This led to a result of 28 studies included in the final review, which were analyzed via meta-analysis to determine whether the fractal dimension changes significantly in retinal disease versus normal individuals. From the meta-analysis, summary effect sizes and 95% confidence intervals were derived for each disease category. The results for diabetic retinopathy and myopia suggest decreased retinal fractal dimension for those pathologies with the association for other diseases such as diabetes mellitus, hypertension, and glaucoma remaining uncertain. Due to heterogeneity in imaging/fractal analysis setups used between studies, it is recommended that standardized retinal fractal analysis procedures be implemented in order to facilitate future meta-analyses.


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