Step Size Research Articles

Optimal control of microgrid using ALO optimization MPPT with power quality enhancement

AbstractThe paper presents a normalized, varying step size-based least mean square-based control for a standalone microgrid and Ant Lion optimization-based maximum power point tracking. This modified version of the incremental conductance algorithm addresses issues like slow dynamic response, fixed step size issues, and steady state oscillations. Comparative analysis with well-known techniques shows that Ant Lion optimizes the tracking of maximum power points more accurately, with fewer oscillations and increased efficiency. Power management and power quality are maintained through proposed adaptive voltage source converter control. The proposed voltage control shows better performance under various dynamic and steady conditions.

Stochastic characterization of navigation strategies in an automated variant of the Barnes maze.

Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.

Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs

This paper introduces gradient-based adaptive neural networks to solve local fractional elliptic partial differential equations. The impact of physics-informed neural networks helps to approximate elliptic partial differential equations governed by the physical process. The proposed technique employs learning the behaviour of complex systems based on input-output data, and automatic differentiation ensures accurate computation of gradient. The method computes the singularity-embedded local fractional partial derivative model on a Hausdorff metric, which otherwise halts the computation by available approximating numerical methods. This is possible because the new network is capable of updating the weight associated with loss terms depending on the solution domain and requirement of solution behaviour. The semi-positive definite character of the neural tangent kernel achieves the convergence of gradient-based adaptive neural networks. The importance of hyperparameters, namely the number of neurons and the learning rate, is shown by considering a stationary anomalous diffusion-convection model on a rectangular domain. The proposed method showcases the network’s ability to approximate solutions of various local fractional elliptic partial differential equations with varying fractal parameters.

MechanoProDB: a web-based database for exploring the mechanical properties of proteins.

The mechanical stability of proteins is crucial for biological processes. To understand the mechanical functions of proteins, it is important to know the protein structure and mechanical properties. Protein mechanics is usually investigated through force spectroscopy experiments and simulations that probe the forces required to unfold the protein of interest. While there is a wealth of data in the literature on force spectroscopy experiments and steered molecular dynamics simulations of forced protein unfolding, this information is spread and difficult to access by non-experts. Here, we introduce MechanoProDB, a novel web-based database resource for collecting and mining data obtained from experimental and computational works. MechanoProDB provides a curated repository for a wide range of proteins, including muscle proteins, adhesion molecules and membrane proteins. The database incorporates relevant parameters that provide insights into the mechanical stability of proteins and their conformational stability such as the unfolding forces, energy landscape parameters and contour lengths of unfolding steps. Additionally, it provides intuitive annotations of the unfolding pathways of each protein, allowing users to explore the individual steps during mechanical unfolding. The user-friendly interface of MechanoProDB allows researchers to efficiently navigate, search and download data pertaining to specific protein folds or experimental conditions. Users can visualize protein structures using interactive tools integrated within the database, such as Mol*, and plot available data through integrated plotting tools. To ensure data quality and reliability, we have carefully manually verified and curated the data currently available on MechanoProDB. Furthermore, the database also features an interface that enables users to contribute new data and annotations, promoting community-driven comprehensiveness. The freely available MechanoProDB aims to streamline and accelerate research in the field of mechanobiology and biophysics by offering a unique platform for data sharing and analysis. MechanoProDB is freely available at https://mechanoprodb.ibdm.univ-amu.fr.

Some novel exact and non-standard finite difference schemes for a class of diffusion–advection–reaction equation

In this paper, a generalized diffusion–advection–reaction equation (GDARE) is considered and by using the solitary wave solution two new class of exact finite difference (EFD) schemes are proposed under certain functional relationship between the temporal and space step sizes. Suitable denominator functions are identified from these EFD schemes and a new non-standard finite difference (NSFD) scheme is developed. This NSFD scheme preserves positivity and boundedness properties of the solution of GDARE. The L 2 , L ∞ errors and CPU times are obtained for the proposed NSFD and EFD schemes to show the proficiency and accuracy of the schemes.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions.

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

Variance-Reduced Accelerated First-Order Methods: Central Limit Theorems and Confidence Statements

In this paper, we consider a strongly convex stochastic optimization problem and propose three classes of variable sample-size stochastic first-order methods: (i) the standard stochastic gradient descent method, (ii) its accelerated variant, and (iii) the stochastic heavy-ball method. In each scheme, the exact gradients are approximated by averaging across an increasing batch size of sampled gradients. We prove that when the sample size increases at a geometric rate, the generated estimates converge in mean to the optimal solution at an analogous geometric rate for schemes (i)–(iii). Based on this result, we provide central limit statements, whereby it is shown that the rescaled estimation errors converge in distribution to a normal distribution with the associated covariance matrix dependent on the Hessian matrix, the covariance of the gradient noise, and the step length. If the sample size increases at a polynomial rate, we show that the estimation errors decay at a corresponding polynomial rate and establish the associated central limit theorems (CLTs). Under certain conditions, we discuss how both the algorithms and the associated limit theorems may be extended to constrained and nonsmooth regimes. Finally, we provide an avenue to construct confidence regions for the optimal solution based on the established CLTs and test the theoretical findings on a stochastic parameter estimation problem. Funding: This work was supported by the Office of Naval Research [Grant N00014-22-1-2589] and Basic Energy Sciences [Grant DE-SC0023303]. The work of J. Lei was partially supported by the National Natural Science Foundation of China [Grants 72271187 and 62088101]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2021.0068 .

Advancing Hyperdimensional Computing Based on Trainable Encoding and Adaptive Training for Efficient and Accurate Learning

Hyperdimensional computing (HDC) is a computing paradigm inspired by the mechanisms of human memory, characterizing data through high-dimensional vector representations, known as hypervectors. Recent advancements in HDC have explored its potential as a learning model, leveraging its straightforward arithmetic and high efficiency. The traditional HDC frameworks are hampered by two primary static elements: randomly generated encoders and fixed learning rates. These static components significantly limit model adaptability and accuracy. The static, randomly generated encoders, while ensuring high-dimensional representation, fail to adapt to evolving data relationships, thereby constraining the model’s ability to accurately capture and learn from complex patterns. Similarly, the fixed nature of the learning rate does not account for the varying needs of the training process over time, hindering efficient convergence and optimal performance. This paper introduces \(\mathsf {TrainableHD} \) , a novel HDC framework that enables dynamic training of the randomly generated encoder depending on the feedback of the learning data, thereby addressing the static nature of conventional HDC encoders. \(\mathsf {TrainableHD} \) also enhances the training performance by incorporating adaptive optimizer algorithms in learning the hypervectors. We further refine \(\mathsf {TrainableHD} \) with effective quantization to enhance efficiency, allowing the execution of the inference phase in low-precision accelerators. Our evaluations demonstrate that \(\mathsf {TrainableHD} \) significantly improves HDC accuracy by up to 27.99% (averaging 7.02%) without additional computational costs during inference, achieving a performance level comparable to state-of-the-art deep learning models. Furthermore, \(\mathsf {TrainableHD} \) is optimized for execution speed and energy efficiency. Compared to deep learning on a low-power GPU platform like NVIDIA Jetson Xavier, \(\mathsf {TrainableHD} \) is 56.4 times faster and 73 times more energy efficient. This efficiency is further augmented through the use of Encoder Interval Training (EIT) and adaptive optimizer algorithms, enhancing the training process without compromising the model’s accuracy.

Study on Intelligent Scoring of English Composition Based on Machine Learning from the Perspective of Natural Language Processing

Knowledge management is crucial to the teaching and learning process in the current era of digitalization. The idea of "learning via working together" is making Natural Language Processing a popular tool to improve the learning process based on the intelligent system for evaluating the composition. English language learning is highly dependent on the composition written by the students under various topics. Teachers are facing huge difficulties in the evaluation of the composition as the level of writing by the students will vary for individual. In this research, Natural Language Processing concept is utilized for getting trained with the student's writing skills and Multiprocessor Learning Algorithm (MLA) combined with Convolutional Neural Network (CNN) (MLA-CNN) for evaluating the composition and declaring the scores for the students. The model's composition scoring rate is validated using a range of learning rate settings. Some theoretical notions for smart teaching are proposed, and it is hoped that this automatic composition scoring model would be used to grade student writing in English classes. When applied to the automatic scoring of students' English composition in schools, the suggested composition scoring system trained by the MLP-CNN has great performance and lays the groundwork for the educational applications of ML inside AI. The study results proved that the proposed model has provided an accuracy of 98%.

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

DNSRF: Deep Network-based Semi-NMF Representation Framework

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate superior performance of our framework, which is thus shown to have a great representation ability.

Attention 3D U-NET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Direction modulated brachytherapy tandem applicator.

Direction Modulated Brachytherapy (DMBT) enables conformal dose distributions. However, clinicians may face challenges in creating viable treatment plans within a fast-paced clinical setting, especially for a novel technology like DMBT, where cumulative clinical experience is limited. Deep learning-based dose prediction methods have emerged as effective tools for enhancing efficiency. To develop a voxel-wise dose prediction model using an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate (HDR) brachytherapy treatment planning with DMBT six-groove tandems with ovoids or ring applicators. A multi-institutional cohort of 122 retrospective clinical HDR brachytherapy plans treated to a prescription dose in the range of 4.8-7.0Gy/fraction was used. A DMBT tandem model was constructed and incorporated onto a research version of BrachyVision Treatment Planning System (BV-TPS) as a 3D solid model applicator and retrospectively re-planned all cases by seasoned experts. Those plans were randomly divided into 64:16:20 as training, validating, and testing cohorts, respectively. Data augmentation was applied to the training and validation sets to increase the size by a factor of 4. An attention-gated 3D UNET architecture model was developed to predict full 3D dose distributions based on high-risk clinical target volume (CTVHR) and organs at risk (OARs) contour information. The model was trained using the mean absolute error loss function, Adam optimization algorithm, a learning rate of 0.001, 250 epochs, and a batch size of eight. In addition, a baseline UNET model was trained similarly for comparison. The model performance was evaluated on the testing dataset by analyzing the outcomes in terms of mean dose values and derived dose-volume-histogram indices from 3D dose distributions and comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinically meaningful dosimetric indices. The proposed attention-gated 3D UNET model showed competitive accuracy in predicting 3D dose distributions that closely resemble the ground-truth dose distributions. The average values of the mean absolute errors were 1.82±29.09Gy (vs. 6.41±20.16Gy for a baseline UNET) in CTVHR, 0.89±1.25Gy (vs. 0.94±3.96Gy for a baseline UNET) in the bladder, 0.33±0.67Gy (vs. 0.53±1.66Gy for a baseline UNET) in the rectum, and 0.55±1.57Gy (vs. 0.76±2.89Gy for a baseline UNET) in the sigmoid. The results showed that the mean absolute error (MAE) for the bladder, rectum, and sigmoid were 0.22±1.22Gy (3.62%) (p=0.015), 0.21±1.06Gy (2.20%) (p=0.172), and -0.03±0.54Gy (1.13%) (p=0.774), respectively. The MAE for D90, V100%, and V150% of the CTVHR were 0.46±2.44Gy (8.14%) (p=0.018), 0.57±11.25% (5.23%) (p=0.283), and -0.43±19.36% (4.62%) (p=0.190), respectively. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64×64×64 voxels for any new patient plan, thus making it sufficient for near real-time applications and aiding with decision-making in the clinic. Attention gated 3D-UNET model demonstrated a capability in predicting voxel-wise dose prediction, in comparison to 3D UNET, for DMBT intracavitary brachytherapy planning. The proposed model could be used to obtain dose distributions for near real-time decision-making before DMBT planning and quality assurance. This will guide future automated planning, making the workflow more efficient and clinically viable.

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

A Study on the Effects of a Self-Administered Eye Exercise Program on the Balance and Gait Ability of Chronic Stroke Patients: A Randomized Controlled Trial.

This study investigates the effects of a self-administered eye exercise (SEE) program on the balance and gait ability of chronic stroke patients hospitalized due to hemiplegia. This study includes 42 patients diagnosed with stroke-related hemiplegia and hospitalized at D Rehabilitation Hospital. The researcher randomly allocated 42 patients into two groups: the experimental group (EG, n = 21, mean age = 58.14 ± 7.69 years, mean BMI = 22.83 ± 2.19 kg/m2) and the control group (CG, n = 21, mean age = 58.57 ± 6.53 years, mean BMI = 22.81 ± 2.36 kg/m2). The SEE program was applied to the EG and the general self-administered exercise (SE) program was applied to the CG. After 4 weeks of intervention, weight distribution of the affected side, the Timed Up and Go test (TUG), step length of the affected side, step length of the unaffected side, gait speed, and cadence were analyzed and compared. In the within-group comparison, both groups showed significant differences in weight distribution (p < 0.05), TUG (p < 0.05), step length of the affected side (p < 0.05), step length of the unaffected side (p < 0.05), gait speed (p < 0.05), and cadence (p < 0.05). In the between-group comparison, a significant difference in the TUG (p < 0.05) was observed. The SEE program had an overall similar effect to the SE program in improving the balance and gait ability of chronic stroke patients, and had a greater effect on dynamic balance ability. Therefore, the SEE program can be proposed as a self-administered exercise program to improve balance and gait ability in stroke patients who are too weak to perform the SE program in a clinical environment or have a high risk of falling.

The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability.

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Walking with increased step length variability increases the metabolic cost of walking.

For every 1% increase in step length variability, there is an 0.7% increase in the metabolic cost of walking.

Model-based cueing-as-needed for walking in Parkinson's disease: A randomized cross-over study

BackgroundCorrecting of the lack of regularity in steps is a key component of gait rehabilitation in Parkinson's disease. We proposed to introduce adaptive spatial auditory cueing (ASAC) based on verbal instruction “lengthen the step” automatically delivered when the stride length decreased below a predetermined threshold. ObjectivesThe present study compared the effect of usual rhythmic auditory cueing versus ASAC used during a walking training in Parkinson's disease. MethodsFifteen patients with Parkinson's disease performed both interventions in randomized order, one week apart: a 20-minute walking training with rhythmic auditory cueing, in form of a metronome adjusted on 110% of the patient's own cadence, or ASAC delivered when the stride length is less than 110% of the patient's own stride length. Assessment criteria were walking distance covered during the intervention, speed, step length, cadence, coefficients of variation of step length and step duration, and indexes of spatial and temporal asymmetry during a walking test before and just after the intervention. ResultsThe walking distance is higher with ASAC compared with rhythmic auditory cueing (rhythmic auditory cueing, 905 (203) m, mean (standard deviation); ASAC, 1043 (212) m; P=0.002). Between-intervention comparison showed some similar effects on walking after the intervention including free speed and step length increases (P<0.05). ConclusionThe distance covered during 20-minute walking with ASAC increases by 15% compared to the use of classical rhythmic auditory cueing, while the immediate therapeutic effects show similar spatial-temporal benefits on short-distance walking. Auditory biofeedback cueing promoting the increase in step length might improve gait relearning in Parkinson's disease.

FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior

&lt;span lang="EN-US"&gt;Financial Technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. Fintech can make any transactions anywhere with the pillars of Peet-to-Peer (P2P) lending, merchants and crowdfunding. In the P2P Lending pillar, there are borrowers and lenders who are digitized in Fintech devices. Fintech in Indonesia is controlled by a state agency called the Otoritas Jasa Keuangan or Financial Services Authority (OJK). In the movement of P2P Lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as ECoS, which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a Mean Absolute Percentage Error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1 = 0.6 and a learning rate 2 = 0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders.&lt;/span&gt;

Intelligent Dust Monitoring System Based on IoT

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

Inducing unstable walking conditions through visual and auditory stimuli.

[Purpose] Falls can significantly affect elderly individuals. However, most current methods used to detect and analyze high-risk conditions make use of simulated falling movements for data collection, which may not accurately represent actual falls. The present study aimed to induce natural falls using visual and auditory stimuli to create unstable walking conditions. [Participants and Methods] Two experiments were performed. The first experiment focused on inducing unstable walking using visual stimuli; whereas, the second experiment combined visual and auditory stimuli. To investigate the effects of stimuli on the induction of unstable walking, our results were compared with those of normal walking conditions. In addition, the two experimental conditions were compared to identify the most effective stimuli. [Results] Both experiments revealed a decrease in step length, an increase in step time and width, and an increase in the coefficient of variation of measurements, indicating an induced walking pattern with a higher risk of falls. Furthermore, combining visual and auditory stimuli caused deterioration of inter-limb coordination, as observed through an increased phase coordination index, thus resulting in further instability during walking. [Conclusion] Visual and auditory stimuli induced unstable walking. In particular, the combination of visual and auditory stimuli with a 0.8-s rhythm increased instability.