Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

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.

The effects of plyometric jump training on physical fitness attributes in basketball players:A meta-analysis`

Background:There is a growing body of experimental evidence examining the effects of plyometric jump training(PJT)on physical fitness attributes in basketball players;however,this evidence has not yet been comprehensively and systematically aggregated.Therefore,our objective was to meta-analyze the effects of PJT on physical fitness attributes in basketball players,in comparison to a control condition.Methods:A systematic literature search was conducted in the databases PubMed,Web of Science,and Scopus,up to July 2020.Peer-reviewed controlled trials with baseline and follow-up measurements investigating the effects of PJT on physical fitness attributes(muscle power,i.e.,jumping performance,linear sprint speed,change-of-direction speed,balance,and muscle strength)in basketball players,with no restrictions on their playing level,sex,or age.Hedge’s g effect sizes(ES)were calculated for physical fitness variables.Using a random-effects model,potential sources of heterogeneity were selected,including subgroup analyses(age,sex,body mass,and height)and single training factor analysis(program duration,training frequency,and total number of training sessions).Computation of metaregression was also performed.Results:Thirty-two studies were included,involving 818 total basketball players.Significant(p<0.05)small-to-large effects of PJT were evident on vertical jump power(ES=0.45),countermovement jump height with(ES=1.24)and without arm swing(ES=0.88),squat jump height(ES=0.80),drop jump height(ES=0.53),horizontal jump distance(ES=0.65),linear sprint time across distances≤10 m(ES=1.67)and>10 m(ES=0.92),change-of-direction performance time across distances≤40 m(ES=1.15)and>40 m(ES=1.02),dynamic(ES=1.16)and static balance(ES=1.48),and maximal strength(ES=0.57).The meta-regression revealed that training duration,training frequency,and total number of sessions completed did not predict the effects of PJT on physical fitness attributes.Subgroup analysis indicated greater improvements in older compared to younger players in horizontal jump distance(>17.15 years,ES=2.11;≤17.15 years,ES=0.10;p<0.001),linear sprint time>10 m(>16.3 years,ES=1.83;≤16.3 years,ES=0.36;p=0.010),and change-of-direction performance time≤40 m(>16.3 years,ES=1.65;≤16.3 years,ES=0.75;p=0.005).Greater increases in horizontal jump distance were apparent with>2 compared with≤2 weekly PJT sessions(ES=2.12 and ES=0.39,respectively;p<0.001).Conclusion:Data from 32 studies(28 of which demonstrate moderate-to-high methodological quality)indicate PJT improves muscle power,linear sprint speed,change-of-direction speed,balance,and muscle strength in basketball players independent of sex,age,or PJT program variables.However,the beneficial effects of PJT as measured by horizontal jump distance,linear sprint time>10 m,and change-of-direction performance time≤40 m,appear to be more evident among older basketball players.

Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.