Background: Young women of reproductive age experience various physiological changes, which they measure and track using various devices, including fitness trackers and smartwatches. However, fitness tracking assessme...Background: Young women of reproductive age experience various physiological changes, which they measure and track using various devices, including fitness trackers and smartwatches. However, fitness tracking assessment methods are ambiguous because they may differ from model to model. Objective: This study aimed to compare the stress level, heart rate, sleep time, number of steps, and distance traveled, which were calculated using fitness tracking methods for daily-life free activity installed in various smartwatches. Materials and Methodology: Healthy women in their 20s to 30s were recruited for this study, which was conducted from December 2021 to June 2022. The finalized participants wore three different smartwatch models (Mi smartband 6, vivosmart<sup>®</sup>4, and Band 6) simultaneously on their person for 48 hours and performed their daily activities and recorded them on an hour-based activity chart. Each smartwatch’s measured data (e.g., age, height, weight, and oral medications) were extracted into five datasets: heart rate, stress level, number of steps, distance, and sleep time. Data analyses were conducted using Spearman’s rank correlation coefficient ρ (for comparing heart rates) and Bland-Altman plots (for assessing heart rate agreement). The smartwatches’ fitness trackers were compared using the mean absolute percentage error. Results: The correlation coefficient showed that vivosmart<sup>®</sup>4 and Band 6 had a higher heart rate agreement (ρ = 0.684). The Bland-Altman plots showed high agreement between Band 6, Mi smartband 6, and vivosmart<sup>®</sup>4. The heart rate measurement method used under free movement was found to be consistent. The examined smartwatches were able to measure heart rate at the same level even under daily-life free movements. Conclusion: Several different smartwatches’ calculated measured values for heart rate had a high agreement. The smartwatches provided accurate heart rate measurements under daily-life free movement conditions. Furthermore, the calculation methods for stress level were found to differ in the fitness tracking of all the smartwatches. .展开更多
Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain ph...Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.展开更多
Low cycle fatigue life consumption analysis was carried out in this work. Fatigue cycles accumulation method suitable even if engine is not often shut down was applied together with the modified universal sloped metho...Low cycle fatigue life consumption analysis was carried out in this work. Fatigue cycles accumulation method suitable even if engine is not often shut down was applied together with the modified universal sloped method for estimating fatigue cycles to failure. Damage summation rule was applied to estimate the fatigue damage accumulated over a given period of engine operation. The concept of fatigue factor which indicates how well engine is operated was introduced to make engine life tracking feasible. The developed fatigue life tracking method was incorporated in PYTHIA, Cranfield University in-house software and applied to 8 months of engine operation. The results obtained are similar to those of real engine operation. At a set power level, fatigue life decreases with increase in ambient temperature with the magnitude of decrease greater at higher power levels. The fatigue life tracking methodology developed could serve as a useful tool to engine operators.展开更多
文摘Background: Young women of reproductive age experience various physiological changes, which they measure and track using various devices, including fitness trackers and smartwatches. However, fitness tracking assessment methods are ambiguous because they may differ from model to model. Objective: This study aimed to compare the stress level, heart rate, sleep time, number of steps, and distance traveled, which were calculated using fitness tracking methods for daily-life free activity installed in various smartwatches. Materials and Methodology: Healthy women in their 20s to 30s were recruited for this study, which was conducted from December 2021 to June 2022. The finalized participants wore three different smartwatch models (Mi smartband 6, vivosmart<sup>®</sup>4, and Band 6) simultaneously on their person for 48 hours and performed their daily activities and recorded them on an hour-based activity chart. Each smartwatch’s measured data (e.g., age, height, weight, and oral medications) were extracted into five datasets: heart rate, stress level, number of steps, distance, and sleep time. Data analyses were conducted using Spearman’s rank correlation coefficient ρ (for comparing heart rates) and Bland-Altman plots (for assessing heart rate agreement). The smartwatches’ fitness trackers were compared using the mean absolute percentage error. Results: The correlation coefficient showed that vivosmart<sup>®</sup>4 and Band 6 had a higher heart rate agreement (ρ = 0.684). The Bland-Altman plots showed high agreement between Band 6, Mi smartband 6, and vivosmart<sup>®</sup>4. The heart rate measurement method used under free movement was found to be consistent. The examined smartwatches were able to measure heart rate at the same level even under daily-life free movements. Conclusion: Several different smartwatches’ calculated measured values for heart rate had a high agreement. The smartwatches provided accurate heart rate measurements under daily-life free movement conditions. Furthermore, the calculation methods for stress level were found to differ in the fitness tracking of all the smartwatches. .
基金The authors wish to express their sincere thanks to the Department of Science&Technology,New Delhi,India(Project ID:SR/FST/ETI-371/2014)express their sincere thanks to the INSPIRE fellowship(DST/INSPIRE Fellowship/2016/IF160837)for their financial support.The authors also thank SASTRA Deemed to be University,Thanjavur,India for extending the infrastructural support to carry out this work.
文摘Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.
文摘Low cycle fatigue life consumption analysis was carried out in this work. Fatigue cycles accumulation method suitable even if engine is not often shut down was applied together with the modified universal sloped method for estimating fatigue cycles to failure. Damage summation rule was applied to estimate the fatigue damage accumulated over a given period of engine operation. The concept of fatigue factor which indicates how well engine is operated was introduced to make engine life tracking feasible. The developed fatigue life tracking method was incorporated in PYTHIA, Cranfield University in-house software and applied to 8 months of engine operation. The results obtained are similar to those of real engine operation. At a set power level, fatigue life decreases with increase in ambient temperature with the magnitude of decrease greater at higher power levels. The fatigue life tracking methodology developed could serve as a useful tool to engine operators.
