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Civil engineering stability inspection based on computer vision and sensors
بازرسی پایداری مهندسی عمران بر اساس بینایی ماشین و حسگرها-2021 A computer that combines the purchase of vision technology and remote cameras and drones offers a promising non-contact solution for the state evaluation of civil infrastructure. This system’s ultimate goal is too automatically and reliably converted to actionable information image or video data. This white paper provides an overview of computer vision technology’s latest development and applies it to the state evaluation of private infrastructure. Deep learning has been applied to various computer vision; deep learning course covers most of the application. Each application has its architecture, such as the input image and labels data loss function. To explain computer vision architecture in the following figure. Review of the work can be divided into two types: application checks and application monitoring. Review inspection applications include context identifiers, local and global features, visible damage, and changes in the reference image. Monitoring applications described herein include static and dynamic strain modal analysis measurement and displacement measurement. Next, several key challenges continue to move towards civilian infrastructure automation and monitoring of vision- based. Finally, aim to address some of the ongoing challenges in our work. Keywords: Monitoring applications | Computer vision | Accelerometer | Non-destructive evaluation | Conventional-contact displacement sensors |
مقاله انگلیسی |
2 |
Running power meters and theoretical models based on laws of physics: Effects of environments and running conditions
معیارهای قدرت دویدن و مدلهای نظری مبتنی بر قوانین فیزیک: تأثیر محیط و شرایط دویدن-2020 Training prescription and load monitoring in running activities have benefited from power output (PW) data
offered by new technologies. Nevertheless, to date, the sensitivity of PW data provided by these tools is still not
completely clear. The aim of this study was to analyze the level of agreement between the PW estimated by five
commercial technologies and the two main internationally theoretical models based on laws of physics, in
different environments and running conditions. Ten endurance-trained male athletes performed three submaximal
running protocols on a treadmill (indoor) and an athletic track (outdoor), with changes in speed, body
weight, and slope. PW was simultaneously registered by the commercial technologies Stryd (StrydApp and
StrydWatch), RunScribe, GarminRP and PolarV, whereas theoretical power output (TPW) was calculated by the two
mathematical models (TPW1 and TPW2). Statistics included, among others, the Pearsons correlation coefficient
(r) and standard error of measurement (SEM). The PolarV, and above all Stryd, showed the closest agreement
with the TPW1 (Stryd: r≥0.947, SEM ≤ 11 W; PolarV: r≥0.931, SEM ≤ 64 W) and TPW2 (Stryd: r≥0.933,
SEM ≤ 60 W; PolarV: r≥0.932, SEM ≤ 24 W), both indoors and outdoors. On the other hand, the devices
GarminRP (r≤0.765, SEM ≥ 59 W) and RunScribe. (r≤0.508, SEM ≥ 125 W) showed the lowest agreement
with the TPW1 and TPW2 models for all conditions and environments analyzed. The closest agreement of the
Stryd and PolarV technologies with the TPW1 and TPW2 models suggest these tools as the most sensitive, among
those analyzed, for PW measurement when changing environments and running conditions. Keywords: Endurance | Accelerometer | Variability | Physiology | Biomechanics |
مقاله انگلیسی |
3 |
Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches
کالیبراسیون و اعتبار سنجی فعالیتهای مبتنی بر شتاب سنج: یک بررسی منظم از رویکردهای یادگیری ماشین-2019 Background: Objective measures using accelerometer-based activity monitors have been extensively used in
physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting
towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity
monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including
data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML
technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models.
The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed
here. The open challenges were identified and recommendations are made for future studies and directions.
Method: We conducted a systematic search of PubMed and Scopus databases to identify studies published before
July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors.
Additional articles were manually identified from references in the identified articles. Results: A total of 62
studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based
models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure.
Conclusions: It appears that various ML-based techniques together with raw acceleration data sampled at
20–30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy
expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the
high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to
transitive and unseen activities together with differences in acceleration signals. Keywords: Objective measurement | Physical activity | Pattern recognition | Energy expenditure | Activity recognition |
مقاله انگلیسی |
4 |
Fall detection system for elderly people using IoT and Big Data
سیستم تشخیص سقوط برای سالمندان با استفاده از اینترنت اشیا و داده های بزرگ-2018 Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional
impairment in an elder and a significant decrease in his mobility, independence and life quality. In that sense, the present work
proposes an innovative IoT-based system for detecting falls of elderly people in indoor environments, which takes advantages of
low-power wireless sensor networks, smart devices, big data and cloud computing. For this purpose, a 3D-axis accelerometer
embedded into a 6LowPAN device wearable is used, which is responsible for collecting data from movements of elderly people
in real-time. To provide high efficiency in fall detection, the sensor readings are processed and analyzed using a decision trees
based Big Data model running on a Smart IoT Gateway. If a fall is detected, an alert is activated and the system reacts
automatically by sending notifications to the groups responsible for the care of the elderly people. Finally, the system provides
services built on cloud. From medical perspective, there is a storage service that enables healthcare professional to access to falls
data for perform further analysis. On the other hand, the system provides a service leveraging this data to create a new machine
learning model each time a fall is detected. The results of experiments have shown high success rates in fall detection in terms of
accuracy, precision and gain.
Keywords: Fall detection; Internet-of-Things; Big Data, 6LowPAN; wearable sensor; Smart IoT Gateway; fall detection; decision tree learning algorithm; accelerometer; elderly people. |
مقاله انگلیسی |
5 |
A practical guide to big data
راهنمای عملی برای داده های بزرگ-2018 Big Data is increasingly prevalent in science and data analysis. We provide a short tutorial
for adapting to these changes and making the necessary adjustments to the academic
culture to keep Biostatistics truly impactful in scientific research.
Keywords: Big data ، Wearable and implantable computing ، Accelerometer |
مقاله انگلیسی |