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نتیجه جستجو - شتاب سنج

تعداد مقالات یافته شده: 5
ردیف عنوان نوع
1 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
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