scholarly journals Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4260
Author(s):  
Ryan Mattfeld ◽  
Elliot Jesch ◽  
Adam Hoover
Keyword(s):  
Everyday Life ◽  
Ground Truth ◽  
The Body ◽  
Data Availability ◽  
Data Set ◽  
Speed Sensor ◽  
Accelerometer Signal ◽  
Sensor Position ◽  
Need To Evaluate ◽  
Everyday Life Activities

Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5–466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Validation method for body-mounted sensor attachments

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Katharina Schmidt ◽  
David Hochmann
Keyword(s):  
Evaluation Method ◽  
Tracking System ◽  
The Body ◽  
Exploratory Research ◽  
Body Segment ◽  
Validation Method ◽  
Sensor Position ◽  
Validation Parameters ◽  
Measurement Units ◽  
Soft Tissue Artifact

AbstractSmall sensor devices like inertial measurement units enable mobile movement and gait analysis, whereby existing systems differ in data acquisition, data processing, and gait parameter calculation. Concerning the validation, recent studies focus on the captured motion and the influence of sensor positioning with respect to the accuracy of the computed biomechanical parameters in comparison to a reference system. Although soft tissue artifact is a major source of error for skin-mounted sensors, there are no investigations regarding the relative movement between the body segment and sensor attachment itself. The aim of this study is to find an evaluation method and to determine parameters that allow the validation of various sensor attachment types and different sensor positionings. The analysis includes the comparison between an adhesive and strap attachment variant as well as the frontal and lateral sensor placement. To validate different attachments, an optical marker-based tracking system was used to measure the body segment and sensor position during movement. The distance between these two positions was calculated and analyzed to determine suitable validation parameters. Despite the exploratory research, the results suggest a feasible validation method to detect differences between the attachments, independent of the sensor type. To have representative and statistically validated results, further studies that involve more participants are necessary.

Can’t wait to feel better: Facebook Live and the recalibration of downtime in tending to the body

2021 ◽  
pp. 016344372110034
Author(s):  
Dang Nguyen
Keyword(s):  
Everyday Life ◽  
Mobile Technology ◽  
Social Practices ◽  
The Body ◽  
Therapeutic Method ◽  
Temporal Space ◽  
The Everyday ◽  
Temporal Structures

This article explores the temporality of liveness on Facebook Live through the analytical lens of downtime. Downtime is conceptualized here as multiscale: downtime exists in between the micro action and inaction of everyday life, but also in larger episodes of personal and health crises that reorient the body toward technologies for instantaneous replenishment of meaning and activity. Living through downtime with mobile technology enables the experience of oscillation between liveness as simultaneity and liveness as instantaneity. By juxtaposing time-as-algorithmic against time-as-lived through the livestreaming practices of diện chẩn, an emergent unregulated therapeutic method, I show how different enactments of liveness on Facebook Live recalibrate downtime so that the body can reconfigure its being-in-time. The temporal reverberation of downtime and liveness creates an alternative temporal space wherein social practices that are shunned by the temporal structures of institution and society can retune and continue to thrive at the margin of these structures and at the central of the everyday.

scholarly journals Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

2020 ◽  
Vol 499 (4) ◽  
pp. 5641-5652
Author(s):  
Georgios Vernardos ◽  
Grigorios Tsagkatakis ◽  
Yannis Pantazis
Keyword(s):  
Confidence Intervals ◽  
Galaxy Evolution ◽  
Gravitational Lensing ◽  
Probability Distributions ◽  
Mass Density ◽  
Ground Truth ◽  
Gaussian Random Fields ◽  
Training Data ◽  
Gravitational Lens ◽  
Data Set

ABSTRACT Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.

Tuning of a Basic Coordination Pattern Constructs Straight-Ahead and Curved Walking in Humans

2004 ◽  
Vol 91 (4) ◽  
pp. 1524-1535 ◽  
Author(s):  
Grégoire Courtine ◽  
Marco Schieppati
Keyword(s):  
Principal Components ◽  
Lower Limb ◽  
Time Course ◽  
Body Movement ◽  
The Body ◽  
Whole Body ◽  
Coordination Pattern ◽  
Data Set ◽  
Coordination Patterns ◽  
Straight Ahead

We tested the hypothesis that common principles govern the production of the locomotor patterns for both straight-ahead and curved walking. Whole body movement recordings showed that continuous curved walking implies substantial, limb-specific changes in numerous gait descriptors. Principal component analysis (PCA) was used to uncover the spatiotemporal structure of coordination among lower limb segments. PCA revealed that the same kinematic law accounted for the coordination among lower limb segments during both straight-ahead and curved walking, in both the frontal and sagittal planes: turn-related changes in the complex behavior of the inner and outer limbs were captured in limb-specific adaptive tuning of coordination patterns. PCA was also performed on a data set including all elevation angles of limb segments and trunk, thus encompassing 13 degrees of freedom. The results showed that both straight-ahead and curved walking were low dimensional, given that 3 principal components accounted for more than 90% of data variance. Furthermore, the time course of the principal components was unchanged by curved walking, thereby indicating invariant coordination patterns among all body segments during straight-ahead and curved walking. Nevertheless, limb- and turn-dependent tuning of the coordination patterns encoded the adaptations of the limb kinematics to the actual direction of the walking body. Absence of vision had no significant effect on the intersegmental coordination during either straight-ahead or curved walking. Our findings indicate that kinematic laws, probably emerging from the interaction of spinal neural networks and mechanical oscillators, subserve the production of both straight-ahead and curved walking. During locomotion, the descending command tunes basic spinal networks so as to produce the changes in amplitude and phase relationships of the spinal output, sufficient to achieve the body turn.

Twitter users exhibited coping behaviours during the COVID-19 lockdown: an analysis of tweets using mixed methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruchi Mittal ◽  
Wasim Ahmed ◽  
Amit Mittal ◽  
Ishan Aggarwal
Keyword(s):  
Data Analysis ◽  
The Body ◽  
Data Set ◽  
Content Type ◽  
Twitter Data ◽  
Long Run ◽  
The World ◽  
Twitter Users ◽  
The Government ◽  
Using Data

Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

Beyond censorship: politics, teens, and ELA teacher candidates

2017 ◽  
Vol 16 (3) ◽  
pp. 303-318 ◽  
Author(s):  
Denise Dávila ◽  
Meghan E. Barnes
Keyword(s):  
Teacher Candidates ◽  
English Language ◽  
Teacher Education Program ◽  
Research Question ◽  
The Body ◽  
Data Set ◽  
Secondary English ◽  
Content Type ◽  
Political Issues ◽  
Practical Experiences

Purpose Grounded in the scholarship addressing teacher self-censorship around controversial topics, this paper aims to investigate a three-part research question: How do secondary English language arts (ELA) teacher–candidates (TCs) in the penultimate semester of their undergraduate teacher education program position political texts/speeches, interpret high school teens’ political standpoints and view the prospects of discussing political texts/speeches with students? The study findings provide insights to the ways some TCs might position themselves as novice ELA teachers relative to political texts/speeches, students, colleagues and families in their future school communities. Design/methodology/approach Audio-recorded data from whole-class and small-group discussions were coded for TCs’ positioning of political texts/speeches, interpretations of teens’ political standpoints and viewpoints on discussing with students President Obama’s speech, “A More Perfect Union” (“A.M.P.U.”) The coded data set was further analyzed to identify themes across the TCs’ perspectives. Findings The data set tells the story of a group of TCs whose positionalities, background knowledge and practical experiences in navigating divergent perspectives would influence both their daily selection and censorship of political texts/speeches like “A.M.P.U.” and their subsequent willingness to guide equitable yet critical conversations about controversial issues in the secondary ELA classroom. Originality/value In advance of the 2018 midterm elections, this paper considers how the common core state standards’ (CCSS) recommendations to include more nonfiction documents in ELA instruction positions ELA teachers to provide interdisciplinary support in helping students think critically about political issues. It expands on the body of scholarship that, thus far, has been primarily grounded in the research on social studies instruction.

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