Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell t...Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.展开更多
该文阐述了基于.NET技术和ERP思想的蛋鸡健康养殖网络化管理信息系统的研发过程。该系统选用W indow s 2003 Server平台、SQL Server 2000数据库、V isua l S tud io.NET 2003开发环境和C#开发语言。系统包括员工服务、人事管理、文档...该文阐述了基于.NET技术和ERP思想的蛋鸡健康养殖网络化管理信息系统的研发过程。该系统选用W indow s 2003 Server平台、SQL Server 2000数据库、V isua l S tud io.NET 2003开发环境和C#开发语言。系统包括员工服务、人事管理、文档管理、饲料管理、蛋鸡管理、视频监控、鸡病查询、鸡蛋查询、系统维护等9个模块。借鉴“产前、产中和产后”的全程管理思想,利用“从车间到餐桌”的鸡蛋编码可追溯技术,采用了鸡舍环境预警模型,使得消费者可以从网上查询到具有Egg ID鸡蛋的相关信息(鸡蛋生产厂商、生产地点、生产时间、蛋鸡信息和生产环境等)。该系统在存栏120万羽蛋鸡的北京某蛋鸡场得以应用。展开更多
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number 223202.
文摘Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.
文摘该文阐述了基于.NET技术和ERP思想的蛋鸡健康养殖网络化管理信息系统的研发过程。该系统选用W indow s 2003 Server平台、SQL Server 2000数据库、V isua l S tud io.NET 2003开发环境和C#开发语言。系统包括员工服务、人事管理、文档管理、饲料管理、蛋鸡管理、视频监控、鸡病查询、鸡蛋查询、系统维护等9个模块。借鉴“产前、产中和产后”的全程管理思想,利用“从车间到餐桌”的鸡蛋编码可追溯技术,采用了鸡舍环境预警模型,使得消费者可以从网上查询到具有Egg ID鸡蛋的相关信息(鸡蛋生产厂商、生产地点、生产时间、蛋鸡信息和生产环境等)。该系统在存栏120万羽蛋鸡的北京某蛋鸡场得以应用。
