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Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and smartwatch Applications

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Big Data Mining and Analytics 2021年第2期4卷 124-138页

作者： Samaher Al-Janabi Ali Hamza Salman Department of Computer Science Faculty of Science for WomenUniversity of BabylonBabylon 964Iraq

This study proposes an intelligent data analysis model for finding optimal patterns in human activities on the basis of biometric features obtained from four sensors installed on smartphone and smartwatch devices. The proposed model, referred to as Scheduling Activities of smartphone and smartwatch based on Optimal Pattern Model(SA-OPM), consists of four main stages. The first stage relates to the collection of data from four sensors in real time(i.e., two smartphone sensors called accelerometer and gyroscope and two smartwatch sensors of the same name).The second stage involves the preprocessing of the data by converting them into graphs. As graphs are difficult to deal with directly, a deterministic selection algorithm is proposed as a new method to find the optimal root to split the graphs into multiple subgraphs. The third stage entails determining the number of samples related to each subgraph by using the optimization technique called the lion optimization algorithm. The final stage involves the generation of patterns from the optimal subgraph by using the association pattern algorithm called g Span. The pattern finder based on Forward-Backward Rules(FBR) generates the optimal patterns and thus aids humans in organizing their activities. Results indicate that the proposed SA-OPM model generates robust and authentic patterns of human activities.

关键词： optimization Ant Lion Optimization(ALO) g Span Forward-Backward Rules(FBR) Internet of Things(IoT) smartwatch smartphone

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smartwatch的控制和应用

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微型机与应用 1992年第1期11卷 31-33页

作者：倪永仁崔正红广州华南理工大学计算机系

本文从硬件,软件两个方面概述了对smartwatch芯片的时间寄存器和不破坏RAM存储器正确读写的控制和应用过程,并经静态测试和动态调试取得成功。

关键词： smartwatch 微机计存器控制应用

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ARISTOTLE SAID ＂HAPPINESS IS A STATE OF ACTIVITY＂ - PREDICTING MOOD THROUGH BODY SENSING WITH smartwatchES

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Journal of Systems Science and Systems Engineering 2018年第5期27卷 586-612页

作者： Peter A Gloor Andrea Fronzetti Colladon Francesca Grippa Pascal Budner Joscha Eirich MIT Center for Collective Intelligence 245 First Street 02142 Cambridge MA USA Department of Enterprise Engineering University of Rome Tor Vergata Via del Politecnico n°1 00133 Rome Italy Northeastern University 360 Huntington Avenue 02115 Boston MA USA University of Cologne Albertus-Magnus-PIatz 50923 Koln Germany University of Bamberg Kapuzinerstraβe1696047 Bamberg Germany

We measure and predict states of Activation and Happiness using a body sensing applicationconnected to smartwatches. Through the sensors of commercially available smartwatches we collectindividual mood states and correlate them with body sensing data such as acceleration, heart rate, lightlevel data, and location, through the GPS sensor built into the smartphone connected to the smartwatchWe polled users on the smartwatch for seven weeks four times per day asking for their mood state. Wefound that both Happiness and Activation are negatively correlated with heart beats and with the levelsof light. People tend to be happier when they are moving more intensely and are feeling less activatedduring weekends. We also found that people with a lower Conscientiousness and Neuroticism andhigher Agreeableness tend to be happy more frequently. In addition, more Activation can be predictedby lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find thattracking people＇s geographical coordinates might play an important role in predicting Happiness andActivation. The methodology we propose is a first step towards building an automated mood trackingsystem, to be used for better teamwork and in combination with social network analysis studies.

关键词： Body sensing systems mood tracking smartwatch experience sampling happiness activation

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Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

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Computers, Materials & Continua 2022年第2期70卷 3221-3234页

作者： Muhammad Atif Hanif Tallha Akram Aamir Shahzad Muhammad Attique Khan Usman Tariq Jung-In Choi Yunyoung Nam Zanib Zulfiqar Department of Electrical and Computer Engineering COMSATS University IslamabadWah Campus47040Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad Campus22040Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Electrical and Computer Engineering CUISahiwalPakistan

Sensors based Human Activity Recognition(HAR)have numerous applications in eHeath,sports,fitness assessments,ambient assisted living(AAL),human-computer interaction and many *** human physical activity can be monitored by using wearable sensors or external *** usage of external devices has disadvantages in terms of cost,hardware installation,storage,computational time and lighting conditions ***,most of the researchers used smart devices like smart phones,smart bands and watches which contain various sensors like accelerometer,gyroscope,GPS etc.,and adequate processing *** the task of recognition,human activities can be broadly categorized as basic and complex human *** of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart *** reasons include lack of sensor-based labeled dataset having several complex human daily life *** of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition,whereas a few of them used both pocket and wrist *** this research,we have proposed a novel framework which is capable to recognize both basic and complex human activities using builtin-sensors of smart phone and smart *** have considered 25 physical activities,including 20 complex ones,using smart device’s built-in *** the best of our knowledge,the existing literature consider only up to 15 activities of daily life.

关键词： Complex human activities human daily life activities features extraction data fusion multi-sensory smartwatch smartphone

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Motion-Based Activities Monitoring through Biometric Sensors Using Genetic Algorithm

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Computers, Materials & Continua 2021年第3期66卷 2525-2538页

作者： Mohammed Alshehri Purushottam Sharma Richa Sharma Osama Alfarraj Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia ASET Amity University NoidaNoida201301India Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia

Sensors and physical activity evaluation are quite limited for motionbased commercial *** the accelerometer of the smartwatch is utilized;walking is *** combination can perform better in terms of sensors and that can be determined by sensors on both the smartwatch and phones,i.e.,accelerometer and *** biometric efficiency,some of the diverse activities of daily routine have been evaluated,also with biometric *** result shows that using the different computing techniques in phones and watch for biometric can provide a suitable output based on the mentioned *** indicates that the high feasibility and results of continuous biometrics analysis in terms of average daily routine *** this research,the set of rules with the real-valued attributes are evolved with the use of a genetic *** the help of real value genes,the real value attributes cab be encoded,and presentation of new methods which are represents not to cares in the *** rule sets which help in maximizing the number of accurate classifications of inputs and supervise classifications are viewed as an optimization *** use of Pitt approach to the ML(Machine Learning)and Genetic based system that includes a resolution mechanism among rules that are competing within the same rule sets is *** enhances the efficiency of the overall system,as shown in the research.

关键词： Genetic algorithms biometrics data mining sensors smartphone smartwatch

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