scholarly journals Adaptive Accumulation of Plantar Pressure for Ambulatory Activity Recognition and Pedestrian Identification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3842
Author(s):  
Phuc Huu Truong ◽  
Sujeong You ◽  
Sang-Hoon Ji ◽  
Gu-Min Jeong
Keyword(s):  
Activity Recognition ◽  
Plantar Pressure ◽  
High Performance ◽  
Support Vector ◽  
Ambulatory Activity ◽  
One Step ◽  
Multiclass Support Vector Machine ◽  
Ambulatory Activities ◽  
Comparative Results ◽  
Step Over

In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.

scholarly journals Human Activity Recognition Using Smart phones

2020 ◽  
Vol 6 (12) ◽  
pp. 445-448
Author(s):  
Aman Gupta and Nidhi Senger
Keyword(s):  
Activity Recognition ◽  
Daily Living ◽  
Inertial Sensors ◽  
Research Field ◽  
Smart Phones ◽  
Body Motion ◽  
Support Vector ◽  
On Line ◽  
Multiclass Support Vector Machine ◽  
Infor Mation

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context infor-mation about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repos-itory. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals N, S and Transition-Metal Co-Doped Graphene Nanocomposites as High-Performance Catalyst for Glucose Oxidation in a Direct Glucose Alkaline Fuel Cell

Nanomaterials ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 202
Author(s):  
Yexin Dai ◽  
Jie Ding ◽  
Jingyu Li ◽  
Yang Li ◽  
Yanping Zong ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Active Sites ◽  
High Performance ◽  
Glucose Oxidation ◽  
Alkaline Fuel Cell ◽  
Blank Group ◽  
Graphene Nanocomposites ◽  
Doped Graphene ◽  
One Step ◽  
Hydrothermal Approach

In this work, reduced graphene oxide (rGO) nanocomposites doped with nitrogen (N), sulfur (S) and transitional metal (Ni, Co, Fe) were synthesized by using a simple one-step in-situ hydrothermal approach. Electrochemical characterization showed that rGO-NS-Ni was the most prominent catalyst for glucose oxidation. The current density of the direct glucose alkaline fuel cell (DGAFC) with rGO-NS-Ni as the anode catalyst reached 148.0 mA/cm2, which was 40.82% higher than the blank group. The DGAFC exhibited a maximum power density of 48 W/m2, which was more than 2.08 folds than that of blank group. The catalyst was further characterized by SEM, XPS and Raman. It was speculated that the boosted performance was due to the synergistic effect of N, S-doped rGO and the metallic redox couples, (Ni2+/Ni3+, Co2+/Co3+ and Fe2+/Fe3+), which created more active sites and accelerated electron transfer. This research can provide insights for the development of environmental benign catalysts and promote the application of the DGAFCs.

scholarly journals Facile Synthesis of Uniform Mesoporous Nb2O5 Micro-Flowers for Enhancing Photodegradation of Methyl Orange

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3783
Author(s):  
Jian-Qing Qiu ◽  
Huan-Qing Xie ◽  
Ya-Hao Wang ◽  
Lan Yu ◽  
Fang-Yuan Wang ◽  
...  
Keyword(s):  
Methyl Orange ◽  
Active Sites ◽  
High Performance ◽  
Diffuse Reflectance Spectroscopy ◽  
X Ray Diffraction ◽  
Flower Structure ◽  
Electron Microscopies ◽  
Bet Method ◽  
One Step ◽  
Catalytic Active Sites

The removal of organic pollutants using green environmental photocatalytic degradation techniques urgently need high-performance catalysts. In this work, a facile one-step hydrothermal technique has been successfully applied to synthesize a Nb2O5 photocatalyst with uniform micro-flower structure for the degradation of methyl orange (MO) under UV irradiation. These nanocatalysts are characterized by transmission and scanning electron microscopies (TEM and SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) method, and UV-Vis diffuse reflectance spectroscopy (DRS). It is found that the prepared Nb2O5 micro-flowers presents a good crystal phases and consist of 3D hierarchical nanosheets with 400–500 nm in diameter. The surface area is as large as 48.6 m2 g−1. Importantly, the Nb2O5 micro-flowers exhibit superior catalytic activity up to 99.9% for the photodegradation of MO within 20 mins, which is about 60-fold and 4-fold larger than that of without catalysts (W/O) and commercial TiO2 (P25) sample, respectively. This excellent performance may be attributed to 3D porous structure with abundant catalytic active sites.

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

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