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Fulltext An adaptive handover prediction scheme for seamless mobility based wireless networks

Sadiq AS, Fisal NB, Ghafoor KZ, Lloret J

ScientificWorldJournal, 2014;2014:610652.
PMID: 25574490 DOI: 10.1155/2014/610652

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Matched MeSH terms: Computer Simulation
Fulltext An analysis of a nonlinear susceptible-exposed-infected-quarantine-recovered pandemic model of a novel coronavirus with delay effect

Raza A, Ahmadian A, Rafiq M, Salahshour S, Ferrara M

Results Phys, 2021 Feb;21:103771.
PMID: 33391985 DOI: 10.1016/j.rinp.2020.103771

In the present study, a nonlinear delayed coronavirus pandemic model is investigated in the human population. For study, we find the equilibria of susceptible-exposed-infected-quarantine-recovered model with delay term. The stability of the model is investigated using well-posedness, Routh Hurwitz criterion, Volterra Lyapunov function, and Lasalle invariance principle. The effect of the reproduction number on dynamics of disease is analyzed. If the reproduction number is less than one then the disease has been controlled. On the other hand, if the reproduction number is greater than one then the disease has become endemic in the population. The effect of the quarantine component on the reproduction number is also investigated. In the delayed analysis of the model, we investigated that transmission dynamics of the disease is dependent on delay terms which is also reflected in basic reproduction number. At the end, to depict the strength of the theoretical analysis of the model, computer simulations are presented.

Matched MeSH terms: Computer Simulation
An analysis of simple computational strategies to facilitate the design of functional molecular information processors

Lee Y, Roslan R, Azizan S, Firdaus-Raih M, Ramlan EI

BMC Bioinformatics, 2016 Oct 28;17(1):438.
PMID: 27793081

BACKGROUND: Biological macromolecules (DNA, RNA and proteins) are capable of processing physical or chemical inputs to generate outputs that parallel conventional Boolean logical operators. However, the design of functional modules that will enable these macromolecules to operate as synthetic molecular computing devices is challenging.
RESULTS: Using three simple heuristics, we designed RNA sensors that can mimic the function of a seven-segment display (SSD). Ten independent and orthogonal sensors representing the numerals 0 to 9 are designed and constructed. Each sensor has its own unique oligonucleotide binding site region that is activated uniquely by a specific input. Each operator was subjected to a stringent in silico filtering. Random sensors were selected and functionally validated via ribozyme self cleavage assays that were visualized via electrophoresis.
CONCLUSIONS: By utilising simple permutation and randomisation in the sequence design phase, we have developed functional RNA sensors thus demonstrating that even the simplest of computational methods can greatly aid the design phase for constructing functional molecular devices.

Matched MeSH terms: Computer Simulation
Fulltext An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Shukla S, Hassan MF, Khan MK, Jung LT, Awang A

PLoS One, 2019;14(11):e0224934.
PMID: 31721807 DOI: 10.1371/journal.pone.0224934

Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.

Matched MeSH terms: Computer Simulation
Fulltext An application of a hybrid MCDM method for the evaluation of entrepreneurial intensity among the SMEs: a case study

Rostamzadeh R, Ismail K, Bodaghi Khajeh Noubar H

ScientificWorldJournal, 2014;2014:703650.
PMID: 25197707 DOI: 10.1155/2014/703650

This study presents one of the first attempts to focus on critical success factors influencing the entrepreneurial intensity of Malaysian small and medium sized enterprises (SMEs) as they attempt to expand internationally. The aim of this paper is to evaluate and prioritize the entrepreneurial intensity among the SMEs using multicriteria decision (MCDM) techniques. In this research FAHP is used for finding the weights of criteria and subcriteria. Then for the final ranking of the companies, VIKOR (in Serbian: VlseKriterijumska Optimizacija I Kompromisno Resenje) method was used. Also, as an additional tool, TOPSIS technique, is used to see the differences of two methods applied over the same data. 5 main criteria and 14 subcriteria were developed and implemented in the real-world cases. As the results showed, two ranking methods provided different ranking. Furthermore, the final findings of the research based on VIKOR and TOPSIS indicated that the firms A3 and A4 received the first rank, respectively. In addition, the firm A4 was known as the most entrepreneurial company. This research has been done in the manufacturing sector, but it could be also extended to the service sector for measurement.

Matched MeSH terms: Computer Simulation
An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Saleh MD, Eswaran C

Comput Methods Biomech Biomed Engin, 2012;15(5):517-25.
PMID: 21331960 DOI: 10.1080/10255842.2010.545949

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.

Matched MeSH terms: Computer Simulation
Fulltext An efficient post-processing adaptive filtering technique to rectifying the flickering effects

Gandam A, Sidhu JS, Verma S, Jhanjhi NZ, Nayyar A, Abouhawwash M, et al.

PLoS One, 2021;16(5):e0250959.
PMID: 33970949 DOI: 10.1371/journal.pone.0250959

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7-1.9db) while (35.83-47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

Matched MeSH terms: Computer Simulation/standards*
Fulltext An expedient synthesis, acetylcholinesterase inhibitory activity, and molecular modeling study of highly functionalized hexahydro-1,6-naphthyridines

Almansour AI, Kumar RS, Arumugam N, Basiri A, Kia Y, Ali MA

Biomed Res Int, 2015;2015:965987.
PMID: 25710037 DOI: 10.1155/2015/965987

A series of hexahydro-1,6-naphthyridines were synthesized in good yields by the reaction of 3,5-bis[(E)-arylmethylidene]tetrahydro-4(1H)-pyridinones with cyanoacetamide in the presence of sodium ethoxide under simple mixing at ambient temperature for 6-10 minutes and were assayed for their acetylcholinesterase (AChE) inhibitory activity using colorimetric Ellman's method. Compound 4e with methoxy substituent at ortho-position of the phenyl rings displayed the maximum inhibitory activity with IC50 value of 2.12 μM. Molecular modeling simulation of 4e was performed using three-dimensional structure of Torpedo californica AChE (TcAChE) enzyme to disclose binding interaction and orientation of this molecule into the active site gorge of the receptor.

Matched MeSH terms: Computer Simulation
Fulltext An experimental study on pile spacing effects under lateral loading in sand

Khari M, Kassim KA, Adnan A

ScientificWorldJournal, 2013;2013:734292.
PMID: 24453900 DOI: 10.1155/2013/734292

Grouped and single pile behavior differs owing to the impacts of the pile-to-pile interaction. Ultimate lateral resistance and lateral subgrade modulus within a pile group are known as the key parameters in the soil-pile interaction phenomenon. In this study, a series of experimental investigation was carried out on single and group pile subjected to monotonic lateral loadings. Experimental investigations were conducted on twelve model pile groups of configurations 1 × 2, 1 × 3, 2 × 2, 3 × 3, and 3 × 2 for embedded length-to-diameter ratio l/d = 32 into loose and dense sand, spacing from 3 to 6 pile diameter, in parallel and series arrangement. The tests were performed in dry sand from Johor Bahru, Malaysia. To reconstruct the sand samples, the new designed apparatus, Mobile Pluviator, was adopted. The ultimate lateral load is increased 53% in increasing of s/d from 3 to 6 owing to effects of sand relative density. An increasing of the number of piles in-group decreases the group efficiency owing to the increasing of overlapped stress zones and active wedges. A ratio of s/d more than 6d is large enough to eliminate the pile-to-pile interaction and the group effects. It may be more in the loose sand.

Matched MeSH terms: Computer Simulation
An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems

Timmis J, Ismail AR, Bjerknes JD, Winfield AF

Biosystems, 2016 Aug;146:60-76.
PMID: 27178784 DOI: 10.1016/j.biosystems.2016.04.001

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.

Matched MeSH terms: Computer Simulation
Fulltext An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

Mustafa HMJ, Ayob M, Nazri MZA, Kendall G

PLoS One, 2019;14(5):e0216906.
PMID: 31137034 DOI: 10.1371/journal.pone.0216906

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

Matched MeSH terms: Computer Simulation*
An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways

Ismail AM, Mohamad MS, Abdul Majid H, Abas KH, Deris S, Zaki N, et al.

Biosystems, 2017 Dec;162:81-89.
PMID: 28951204 DOI: 10.1016/j.biosystems.2017.09.013

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.

Matched MeSH terms: Computer Simulation
Fulltext An improved swarm optimization for parameter estimation and biological model selection

Abdullah A, Deris S, Mohamad MS, Anwar S

PLoS One, 2013;8(4):e61258.
PMID: 23593445 DOI: 10.1371/journal.pone.0061258

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Matched MeSH terms: Computer Simulation
An in silico approach towards the identification of novel inhibitors of the TLR-4 signaling pathway

Mahita J, Harini K, Rao Pichika M, Sowdhamini R

J Biomol Struct Dyn, 2016 Jun;34(6):1345-62.
PMID: 26264972 DOI: 10.1080/07391102.2015.1079243

Precise functioning and fine-tuning of Toll-like receptor 4 (TLR4) signaling is a critical requirement for the smooth functioning of the innate immune system, since aberrant TLR4 activation causes excessive production of pro-inflammatory cytokines and interferons. This can result in life threatening conditions such as septic shock and other inflammatory disorders. The TRIF-related adaptor molecule (TRAM) adaptor protein is unique to the TLR4 signaling pathway and abrogation of TRAM-mediated TLR4 signaling is a promising strategy for developing therapeutics aimed at disrupting TRAM interactions with other components of the TLR4 signaling complex. The VIPER motif from the vaccinia virus-producing protein, A46 has been reported to disrupt TRAM-TLR4 interactions. We have exploited this information, in combination with homology modeling and docking approaches, to identify a potential binding site on TRAM lined by the BB loop and αC helix. Virtual screening of commercially available small molecules targeting the binding site enabled to short-list 12 small molecules to abrogate TRAM-mediated TLR4 signaling. Molecular dynamics and molecular mechanics calculations have been performed for the analysis of these receptor-ligand interactions.

Matched MeSH terms: Computer Simulation
An insight into structure and stability of DNA in ionic liquids from molecular dynamics simulation and experimental studies

Jumbri K, Abdul Rahman MB, Abdulmalek E, Ahmad H, Micaelo NM

Phys Chem Chem Phys, 2014 Jul 21;16(27):14036-46.
PMID: 24901033 DOI: 10.1039/c4cp01159g

Molecular dynamics simulation and biophysical analysis were employed to reveal the characteristics and the influence of ionic liquids (ILs) on the structural properties of DNA. Both computational and experimental evidence indicate that DNA retains its native B-conformation in ILs. Simulation data show that the hydration shells around the DNA phosphate group were the main criteria for DNA stabilization in this ionic media. Stronger hydration shells reduce the binding ability of ILs' cations to the DNA phosphate group, thus destabilizing the DNA. The simulation results also indicated that the DNA structure maintains its duplex conformation when solvated by ILs at different temperatures up to 373.15 K. The result further suggests that the thermal stability of DNA at high temperatures is related to the solvent thermodynamics, especially entropy and enthalpy of water. All the molecular simulation results were consistent with the experimental findings. The understanding of the properties of IL-DNA could be used as a basis for future development of specific ILs for nucleic acid technology.

Matched MeSH terms: Computer Simulation
An intelligent load shedding scheme using neural networks and neuro-fuzzy

Haidar AM, Mohamed A, Al-Dabbagh M, Hussain A, Masoum M

Int J Neural Syst, 2009 Dec;19(6):473-9.
PMID: 20039470

Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.

Matched MeSH terms: Computer Simulation*
An Îto stochastic differential equations model for the dynamics of the MCF-7 breast cancer cell line treated by radiotherapy

Oroji A, Omar M, Yarahmadian S

J Theor Biol, 2016 10 21;407:128-137.
PMID: 27457094 DOI: 10.1016/j.jtbi.2016.07.035

In this paper, a new mathematical model is proposed for studying the population dynamics of breast cancer cells treated by radiotherapy by using a system of stochastic differential equations. The novelty of the model is essentially in capturing the concept of the cell cycle in the modeling to be able to evaluate the tumor lifespan. According to the cell cycle, each cell belongs to one of three subpopulations G, S, or M, representing gap, synthesis and mitosis subpopulations. Cells in the M subpopulation are highly radio-sensitive, whereas cells in the S subpopulation are highly radio-resistant. Therefore, in the process of radiotherapy, cell death rates of different subpopulations are not equal. In addition, since flow cytometry is unable to detect apoptotic cells accurately, the small changes in cell death rate in each subpopulation during treatment are considered. Subsequently, the proposed model is calibrated using experimental data from previous experiments involving the MCF-7 breast cancer cell line. Consequently, the proposed model is able to predict tumor lifespan based on the number of initial carcinoma cells. The results show the effectiveness of the radiation under the condition of stability, which describes the decreasing trend of the tumor cells population.

Matched MeSH terms: Computer Simulation
Analysis of coupling efficiency on hemispherical fiber lens by method of lines

Lambak Z, Abdul Rahman F, Mokhtar MR, Tengku IA

Opt Express, 2009 Feb 16;17(4):2926-37.
PMID: 19219196

The method of lines (MoL) has been developed to study coupling efficiency on hemispherical lens. In this paper, the physical shape of the lens is approximated by cascading a number of straight waveguide segments. The perfectly matched layer (PML) is applied as an absorber for the MoL to reduce numerical reflection in the simulation region. Analysis is done by calculating coupling efficiency at the plane of integration where the coupling efficiency is an overlap integral between laser diode field and fiber field. The result of coupling efficiency in this analysis is compared to the experiment and ABCD matrix. It is found that MoL gives good result accuracy.

Matched MeSH terms: Computer Simulation
Fulltext Analysis of glass-reinforced epoxy material for radio frequency resonator

Zaman MR, Islam MT, Misran N, Yatim B

ScientificWorldJournal, 2014;2014:831435.
PMID: 24977230 DOI: 10.1155/2014/831435

A radio frequency (RF) resonator using glass-reinforced epoxy material for C and X band is proposed in this paper. Microstrip line technology for RF over glass-reinforced epoxy material is analyzed. Coupling mechanism over RF material and parasitic coupling performance is explained utilizing even and odd mode impedance with relevant equivalent circuit. Babinet's principle is deployed to explicate the circular slot ground plane of the proposed resonator. The resonator is designed over four materials from different backgrounds which are glass-reinforced epoxy, polyester, gallium arsenide (GaAs), and rogers RO 4350B. Parametric studies and optimization algorithm are applied over the geometry of the microstrip resonator to achieve dual band response for C and X band. Resonator behaviors for different materials are concluded and compared for the same structure. The final design is fabricated over glass-reinforced epoxy material. The fabricated resonator shows a maximum directivity of 5.65 dBi and 6.62 dBi at 5.84 GHz and 8.16 GHz, respectively. The lowest resonance response is less than -20 dB for C band and -34 dB for X band. The resonator is prototyped using LPKF (S63) drilling machine to study the material behavior.

Matched MeSH terms: Computer Simulation
Fulltext Analysis of human standing balance by largest lyapunov exponent

Liu K, Wang H, Xiao J, Taha Z

Comput Intell Neurosci, 2015;2015:158478.
PMID: 25866500 DOI: 10.1155/2015/158478

The purpose of this research is to analyse the relationship between nonlinear dynamic character and individuals' standing balance by the largest Lyapunov exponent, which is regarded as a metric for assessing standing balance. According to previous study, the largest Lyapunov exponent from centre of pressure time series could not well quantify the human balance ability. In this research, two improvements were made. Firstly, an external stimulus was applied to feet in the form of continuous horizontal sinusoidal motion by a moving platform. Secondly, a multiaccelerometer subsystem was adopted. Twenty healthy volunteers participated in this experiment. A new metric, coordinated largest Lyapunov exponent was proposed, which reflected the relationship of body segments by integrating multidimensional largest Lyapunov exponent values. By using this metric in actual standing performance under sinusoidal stimulus, an obvious relationship between the new metric and the actual balance ability was found in the majority of the subjects. These results show that the sinusoidal stimulus can make human balance characteristics more obvious, which is beneficial to assess balance, and balance is determined by the ability of coordinating all body segments.

Matched MeSH terms: Computer Simulation

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