Time Warping Research Articles

The Influence of Fatigue, Recovery, and Environmental Factors on the Body Stability of Construction Workers.

In the construction industry, falls, slips, and trips (FST) account for 42.3% of all accidents. The primary cause of FST incidents is directly related to the deterioration of workers' body stability. To prevent FST-related accidents, it is crucial to understand the interaction between physical fatigue and body stability in construction workers. Therefore, this study investigates the impact of fatigue on body stability in various construction site environments using Dynamic Time Warping (DTW) analysis. We conducted experiments reflecting six different fatigue levels and four environmental conditions. The analysis process involves comparing changes in DTW values derived from acceleration data obtained through wearable sensors across varying fatigue levels and construction environments. The results reveal the following changes in DTW values across different environments and fatigue levels: for non-obstacle, obstacle, water, and oil conditions, DTW values tend to increase as fatigue levels rise. In our experiments, we observed a significant decrease in body stability against external environments starting from fatigue Levels 3 or 4 (30% and 40% of the maximum failure point). In the non-obstacle condition, the DTW values were 9.4 at Level 0, 12.8 at Level 3, and 23.1 at Level 5. In contrast, for the oil condition, which exhibited the highest DTW values, the values were 10.5 at Level 0, 19.1 at Level 3, and 34.5 at Level 5. These experimental results confirm that the body stability of construction workers is influenced by both fatigue levels and external environmental conditions. Further analysis of recovery time, defined as the time it takes for body stability to return to its original level, revealed an increasing trend in recovery time as fatigue levels increased. This study quantitatively demonstrates through wearable sensor data that, as fatigue levels increase, workers experience decreased body stability and longer recovery times. The findings of this study can inform individual worker fatigue management in the future.

AlgoRitmo Literacies In Gaming: Leveraging Chicanx Praxis To Reimagine AI Systems

ABSTRACTThis article reports on findings from a social design‐based study conducted with an intergenerational group of youth, educators and researchers participating in the Learning to Transform (LiTT) Gaming Lab. We advance the notion of AlgoRitmo Literacies, to highlight the ingenuity of youth and educators as they used a tool called Character AI to author lore emerging within a virtual city called LiTT City. We conceptualize AlgoRitmo—a play on the word algorithm—as part inquiry and reflection (the algo or “something” of AI tools), and part action and future‐oriented (ritmo as in movement). Inspired by cosmogonies influenced by Coyolxauhqui, the fragmented Aztec moon goddess, this paper illustrates how young people reconfigure AI artifacts, reshape relationships with AI‐governed non‐playable characters, and repurpose AI tools to envision alternative futures and identities. In identifying AlgoRitmo Literacies, we provide examples of how ChicanX communities subvert ideologies embedded in AI through creative and ingenious interventions in video games and the construction of cyborg Chicanx subjectivities. This paper offers implications for how educators across content areas can leverage gaming, and AI tools, toward consequential literacy development.

Refined simulation and verification of blast loads on curved shells

Terrorist attacks and accidental explosions pose significant challenges to the security of public buildings. This study combines detailed test data from three different curved shells to simulate different blast scenarios, effectively validating the simulation of blast loads and blast waves. Based on dynamic time warping and blast damage criteria, a similarity algorithm for blast loads based on overpressure histories of multi-gauge is proposed, and the similarity of blast loads on curved shells under four different scenarios is systematically evaluated. The spatiotemporal distribution of blast pressure around curved shells is analyzed based on simulation results. This paper provides a convenient tool for predicting blast loads on complex building shapes and optimizing blast-resistant designs.

ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.

Resolving the Amino Acid Sequence of Aβ1‐42 at the Single‐Residue Level Using Subnanopores in Ultrathin Films

AbstractProteoforms are proteins derived from highly related genes or post translational modifications (PTMs) of the same protein. They share extremely similar primary structures but have varying functions. Unfortunately, protein de novo sequencing including specific PTM/mutation detection is still challenging. Herein, a nanopore‐based technique is reported to resolve the amino acid order of amyloid‐β (Aβ1‐42) with site specificity. Subnanopores are sputtered in 5 nm‐thick inorganic membranes with a sensing depth of 0.66 nm inferred by finite element analysis. Denatured molecules at 0.45 ng mL−1 translocate through subnanopores while the current traces are sampled at 500 kHz with rms noise <15 pA. Hundreds of blockades are clustered using machine learning, and multiple blockades are averaged to establish current consensus. Consensus traces strongly correlate with a linear model of amino acid volume of Aβ1‐42 at single residue resolution, with Pearson Correlation Coefficients (PCCs) of 0.81 ± 0.03 and 0.92 ± 0.03 before and after dynamic time warping (DTW). A scrambled version of Aβ1‐42 is tested for validation purposes. Deep learning classification reveals that different polypeptides generate distinct translocation fluctuating patterns, but variations become imperceptible for the same species measured across nanopores (Area Under the Curve, AUC 0.93 ± 0.05 vs 0.64 ± 0.12). Lastly, important PTMs and mutations are site‐specifically located along the primary structure, implying new potential clinical applications.

Spatial and temporal variation of biomass density beneath drifting fish‐aggregating devices in the western and central Pacific Ocean

AbstractWe analyzed three‐dimensional (3‐D) spatial and temporal variation of biomass density associated with drifting fish aggregating devices (dFADs) in the western and central Pacific Ocean. Detection depth of dFAD echosounder buoys was divided into three layers, for estimation of biomass density in each layer based on detected water volume. Temporal variation and spatial distribution of biomass density in each layer were compared. Similarity of biomass density gravity center shifts in each layer were assessed using the dynamic time warping (DTW) regularization algorithm. Biomass density varied regularly over ~1 month, with the 2nd and 3rd layers delayed ~14 days compared to the 1st layer. Biomass distribution range and density values were higher in the 1st layer than the 2nd and 3rd layers, but locations of maximum biomass density were close. The distribution of biomass density gravity centers between adjacent water layers were similar, with gravity centers of the 2nd and 3rd layers shifted over time in relationd to the 1st layer. Our study demonstrated that the aggregation behavior of species from different water layers attracted by dFADs were related, and emphasized the necessity for ecosystem‐based fisheries management.

The making(s) of more-than-human design: introduction to the special issue on more-than-human design and HCI

ABSTRACT Human activities have drastically altered the planet, with design playing a significant role. While design may intend to do good, its consequences are not always positive: from climate change to resource depletion to unforeseen social dynamics. These transformations also include ourselves, as our relationships with new technologies blur and complicate previous human and machine agency distinctions. Increasingly, design has become a matter of defining what it means to be human. This special issue explores the proposition that conventional human-centered design approaches may not adequately address the complex challenges we face, and that there is instead a need to ground design in more-than-human perspectives. This introduction outlines the evolving landscape of more-than-human design in the context of HCI. Articulating a series of emerging research trajectories, we aim to illuminate the transformative potential of more-than-human orientations to design, including how they both extend and depart from familiar lines of inquiry in HCI – for example, how designers are redefining data, interfaces, and responsibility, and reshaping posthuman knowledge through design. Ultimately, this special issue aims to explore new pathways for designing in the era of the more-than-human, challenging the perceived divide between practice and theory to imagine alternative futures for HCI.

Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms

Suicidal ideation fluctuates over time, as does its related risk factors. Little is known about the difference or similarities of the temporal patterns. The current exploratory secondary analysis examines which risk symptoms have similar time dynamics using a mathematical algorithm called dynamic time warping (DTW). Ecological momentary assessment data was used of 11 depressed psychiatric outpatients with suicidal ideation who answered three daytime surveys at semi-random sampling points for a period of three to six months. Patients with 45 assessments or more were included. Results revealed significant inter-individual variability in symptom dynamics and clustering, with certain symptoms often clustering due to similar temporal patterns, notably feeling sad, hopelessness, feeling stuck, and worrying.The directed network analyses shed light on the temporal order, highlighting entrapment and worrying as symptoms strongly related to suicide ideation. Still, all patients also showed unique directed networks. While for some patients changes in entrapment directly preceded change in suicide ideation, the reverse temporal ordering was also found. Relatedly, within some patients, perceived burdensomeness played a pivotal role, whereas in others it was unconnected to other symptoms. The study underscores the individualized nature of symptom dynamics and challenges linear models of progression, advocating for personalized treatment strategies.

Transient Attention Gates Access Consciousness: Coupling N2pc and P3 Latencies Using Dynamic Time Warping.

The N2pc and P3 event-related potentials (ERPs), used to index selective attention and access to working memory and conscious awareness, respectively, have been important tools in cognitive sciences. Although it is likely that these two components and the underlying cognitive processes are temporally and functionally linked, such links have not yet been convincingly demonstrated. Adopting a novel methodological approach based on dynamic time warping (DTW), we provide evidence that the N2pc and P3 ERP components are temporally linked. We analyzed data from an experiment where 23 participants (16 women) monitored bilateral rapid serial streams of letters and digits in order to report a target digit indicated by a shape cue, separately for trials with correct responses and trials where a temporally proximal distractor was reported instead (distractor intrusion). DTW analyses revealed that N2pc and P3 latencies were correlated in time, both when the target or a distractor was reported. Notably, this link was weaker on distractor intrusion trials. This N2pc-P3 association is discussed with respect to the relationship between attention and access consciousness. Our results demonstrate that our novel method provides a valuable approach for assessing temporal links between two cognitive processes and their underlying modulating factors. This method allows to establish links and their modulator for any two time-series across all domains of the field (general-purpose MATLAB functions and a Python module are provided alongside this paper).

Effects of rainfall pattern classification methods on the probability estimation of typhoon-induced debris-flow occurrence

ABSTRACT The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.

Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

Accurate daily air pollution forecasts play a pivotal role in enabling government to implement timely emergency responses and helping alert individuals sensitive to air pollution to take preventive measures. The atmospheric continuity fosters spatial correlations among air pollutants at various locations, which is a factor frequently overlooked in contemporary research focused on harnessing data-driven models for air quality prediction. Therefore, this study proposed a Spatial-Autocorrelation-based Long Short-Term Memory (SALSTM) model for the daily forecasting in Wuhan, Hubei Province, China. Using a multivariate prediction approach with daily air pollution data and meteorological data from Wuhan, as well as air pollution data from surrounding cities, from 2021 to 2022 as input, the model was applied for projecting the daily PM2.5 for Wuhan during the year 2023 and conducting accuracy cross-validation. The results were compared with a univariate prediction approach utilizing the Autoregressive Integrated Moving Average (ARIMA) model and the original Long Short-Term Memory (LSTM) model. Furthermore, this study utilized Dynamic Time Warping (DTW) for feature selection in multivariate prediction, comparing the accuracy of prediction results before and after feature selection. Experimental results indicated that the SALSTM model, incorporating the DTW method, achieved a Root Mean Squared Error (RMSE) of 6.92 μg/m3, a Mean Absolute Error (MAE) of 4.07 μg/m3 and a coefficient of determination (R2) of 0.95. Compared to the univariate forecasting method, the three accuracy metrics RMSE, MAE, and R2 have improved by 54.74%, 58.68%, and 37.68%, respectively. In comparison with the original LSTM, the improvement is 23.79%, 30.90%, and 4.40%. In conclusion, the SALSTM model established in this study demonstrates accurate daily forecasting of PM2.5 concentrations.

Improvement and Application of Hale’s Dynamic Time Warping Algorithm

Due to the different generation and propagation mechanisms of P- and S-waves, there may be significant differences in the seismic data collected by the two, which poses a great obstacle to the time domain matching of P- and S-waves in multiwave exploration. Furthermore, the quality and accuracy of the matching effect will directly affect the subsequent multiwave joint inversion and interpretation effect. Therefore, the study of P and S-wave-matching methods plays a crucial role in seismic exploration. In 2013, Hale improved the classical Dynamic Time Warping (DTW) algorithm applied to solve the problem of speech recognition, and obtained the DTW algorithm suitable for solving the matching of P-waves and S-waves. The seismic wave-matching results generated by this algorithm are horizontal discontinuous (different trajectories) and need further processing. This study analyses the algorithm based on simulations of seismic waves using Ricker wavelets. In response to existing problems, this paper proposes strategies to improve the DTW algorithm. The algorithm in this study significantly improved the continuity of the registration results of the actual seismic wave data in the horizontal direction (different traces).

The invisible pandemic of antimicrobial resistance and its possible endings.

Antimicrobial resistance (AMR) is often framed as a 'silent pandemic,' an invisible crisis unfolding beyond the public gaze. This unseen emergency narrative fuels policy responses aimed at re-establishing human control over antimicrobial use and benefits. In this commentary, we critique the reduction of AMR to a homogenising framework - a product of long-standing paradigms for disease control and elimination. We argue that AMR stems not merely from microbial exposure to drugs, but also involves broader anthropocentric practices. We assert that merely extending AMR concerns to encompass environmental factors is insufficient. Instead, we advocate for a paradigm shift towards a holistic understanding that respects the evolutionary adaptability and survival strategies of microbial life itself. Consequently, a fundamental re-evaluation of large-scale antibiotic use and production is necessary. Rather than seeking to control AMR as a pandemic, we propose exploring the inherent complexity and interdependence of AMR issues. Our proposition advocates for alternative futures that foster collaborations between human and non-human actors, ultimately envisioning a shift in human-microbial relationships towards more integrative health strategies.

Head Gesture Recognition Combining Activity Detection and Dynamic Time Warping.

The recognition of head movements plays an important role in human-computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human-computer interfaces.

Sociodigital futures of education: reparations, sovereignty, care, and democratisation

ABSTRACT As EdTech industries grow in reach and power it is imperative to motivate conditions for ethical challenge and contestation, always remaining attentive to the kinds of education futures that dominant imaginaries of technology foreclose. In this paper, we explore how the multiple lenses of reparations, sovereignty, care and democratisation can offer resources for envisaging alternative sociodigital futures of education. We identify how these ideas can disrupt dominant EdTech modalities, exploring how they foreground different kinds of educational relationships and priorities for education/social justice. The paper explores examples of how sociodigital futures-in-the-making have begun to materialise in a range of locations and how they urge new agendas for research, redesign and regulation in relation to EdTech. Whilst the power of the EdTech industry can be overwhelming, we suggest that critiques work from a position of abundance: there are always already many ways to radically reimagine sociodigital futures of education. We argue for the importance of recognising, surfacing, and working with these potentialities in ongoing debates about EdTech precisely to keep the future of education open.

Scripting solutions for the future: the OECD’s advocacy of happiness and well-being

ABSTRACT Over the past decade, the OECD has gradually shifted its governing mechanism from promoting ‘best practices’ based on comparative data on pupils’ cognitive skills to actively advocating for individual and collective well-being as an alternative and ideal future. This article focuses on the OECD’s use of ‘techno-scientific fictive scripts’ as a strategy to promote happiness and well-being as solutions to anticipated crises, despite their conceptual ambiguity and token usage. It analyses how the OECD’s recent ‘Future of Education’ projects have sought to steer its audience towards shared concerns and expectations of the future, while simultaneously asserting its technical expertise in future studies methodologies. It argues that by returning from endorsing data-driven policies to making futuristic claims using future studies methodologies, the OECD endeavours to redefine itself as both a pathfinder and a problem solver, simultaneously blending its human capital imperatives with technological inevitability in its vision of the future.

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to ‘confounding factors’ (i.e. factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1) limb position variability, (2) cross-day use, and a newly identified confound faced by discrete systems (3) gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded ( p< 0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

Speech under Stress: Analysis and Recognition using KNN

This work examines how speech characteristics might be analyzed to tailor automated conversation recognition to illness diagnosis. Voice analysis, which is now performed by a skilled physician using techniques based on auditory analysis, enables the diagnosis of illnesses that impact the vocal apparatus. The suggested work offers a cutting-edge way to monitor a patient's pathology. It is stress-free to use, quick, and affordable for the physician, and safe for the patient. This technique practices a parametric methodology (jitter, shimmer, harmonic to sound, etc.) to assess the sick vocal sound. Additionally, the technique for this work rest on on the feature extraction of Mel Frequency Cepstral Coefficient (MFCC) and the feature matching of Dynamic Time Warping (DTW). This article provides a step-by-step breakdown of the speech analysis procedure used on anxious individuals. The degree of classification accuracy attained for each of the retrieved characteristics confirms that our method is effective in differentiating between stressed individuals. Thus, the speech analysis feature on the PRAAT platform has produced positive results and shown to be an effective tool for differentiating between those who are under stress and those who are not. Using MATLAB 2018b, the work displays several 2D and 3D displays of teaching and anticipated matters. Keywords: Dynamic Time Warping, Mel Frequency Cepstral Coefficient, K nearest neighbor, Virtual Reality

Socio-environmental Opportunities for Organic Material Management in California's Sustainability Transition.

Contemporary resource management is doubly burdened by high rates of organic material disposal in landfills, generating potent greenhouse gases (GHG), and globally degraded soils, which threaten future food security. Expansion of composting can provide a resilient alternative, by avoiding landfill GHG emissions, returning valuable nutrients to the soil to ensure continued agricultural production, and sequestering carbon while supporting local communities. Recognizing this opportunity, California has set ambitious organics diversion targets in the Short-Lived Climate Pollutant Law (SB1383) which will require significant increases (5 to 8 million tonnes per year) in organic material processing capacity. This paper develops a spatial optimization model to consider how to handle this flow of additional material while achieving myriad social and ecological benefits through compost production. We consider community-based and on-farm facilities alongside centralized, large-scale infrastructure to explore decentralized and diversified alternative futures of composting infrastructure in the state of California. We find using a diversity of facilities would provide opportunity for cost savings while achieving significant emissions reductions of approximately 3.4 ± 1 MMT CO2e and demonstrate that it is possible to incorporate community protection into compost infrastructure planning while meeting economic and environmental objectives.

Traversing the Urban Soundscape: Black Sonic Geographies within The Minneapolis Sound

AbstractThis paper illuminates The Minneapolis Sound's emergence from the urban soundscapes of late 20th century Minneapolis. Turning to the 1960s and 1970s, I trace the genre's geohistorical emergence to a Black diasporic community who found within marginality the possibilities to spatialise an experimental world across the urban margins. Disclosing how this experimental world was upheld by improvisatory musical ensembles and their dynamic reaffirmations of a Black sense of place, the paper reveals how The Minneapolis Sound was insurgently pioneered as a Black sonic counter culture amidst unequivocal oppression. I then temporally propel the paper into the 1980s and 1990s and explore how the artist Prince and band The Time radically re‐imagined the city's anti‐Black spatial histories towards more just ends. This elucidates how, after emerging from the spatiality of the racialised metropolis, The Minneapolis Sound provided a speculative avenue of decolonial poetics through which alternative Black futures were made imaginable.