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Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Min-Cheol Kwon, Sunwoong Choi
2018 Wireless Communications and Mobile Computing  
In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification.  ...  Human activity recognition using wearable devices has been actively investigated in a wide range of applications.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2018/2618045 fatcat:g645d7ljrfeetkwkbsywboi7jq

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches [chapter]

Serkan Balli, Ensar Arif Sağbas
2017 Advances in Statistical Methodologies and Their Application to Real Problems  
The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data.  ...  To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second  ...  [8] studied classification of five human activities by using only accelerometer data and two learning algorithms: Artificial Neural Networks and Decision Tree C4.5.  ... 
doi:10.5772/66213 fatcat:kvkvl4yqlzbard3xiy2a56itjy

Human Activity Recognition Using Smartphone and Smartwatch

Hamid M. Ali, Ali M. Muslim
2016 International Journal of Computer Engineering in Research Trends  
Human activity recognition is influential subject in different fields of human daily life especially in the mobile health.  ...  of the human activity recognition system.  ...  vector machine, and artificial neural networks as classifiers to classify 5 activities.  ... 
doi:10.22362/ijcert/2016/v3/i10/48906 fatcat:wn7ic5gtcbhlbhe4uoslq6uize

Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

Muhammad Atif Hanif, Tallha Akram, Aamir Shahzad, Muhammad Attique Khan, Usman Tariq, Jung-In Choi, Yunyoung Nam, Zanib Zulfiqar
2022 Computers Materials & Continua  
For the task of recognition, human activities can be broadly categorized as basic and complex human activities.  ...  Some 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 positions.  ...  The sensor based human physical activity recognition using smartphone and smartwatch (wrist wearable devices) has been widely studied from last few years due to its various application in daily life specially  ... 
doi:10.32604/cmc.2022.019815 fatcat:evjurqnqhbdjpoqauaq4oe5bjq

Smartphone and Smartwatch for Human Activity Recognition

Mashhour M Bani Amer
2021 Annals of Advanced Biomedical Sciences  
Five daily physical human activities are evaluated using five classifiers from WEKA, in addition to Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms  ...  Human activity recognition (HAR) systems are developed as aspect of a model to allow continual assessment of human behaviors in IoT environments in the areas of ambient assisted living, sports injury detection  ...  Acknowledgment The authors would like to thank Electrical and Biomedical Engineering Departments, JUST, Irbid, Jordan for their support.  ... 
doi:10.23880/aabsc-16000159 fatcat:pqfkjxmoord6tbpybqfncrdohe

Human activity recognition based on machine learning classification of smartwatch accelerometer dataset
Raspoznavanje ljudskih aktivnosti klasifikacijom akcelerometarskih podataka sa pametnih satova pomoću modela mašinskog učenja

Dušan Radivojević, Nikola Mirkov, Slobodan Maletić
2021 FME Transaction  
For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms.  ...  The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset.  ...  ACKNOWLEDGMENTS The research was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia.  ... 
doi:10.5937/fme2101225r fatcat:eumkojbzkbgzzcubdrv3v6rctq

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey [article]

Florenc Demrozi, Graziano Pravadelli, Azra Bihorac, Parisa Rashidi
2020 arXiv   pre-print
In the last decade, Human Activity Recognition (HAR) has become a very important research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present  ...  This survey focuses on critical applications of Machine Learning (ML) in the fields of HAR, largely oriented to Daily Life Activities by presenting an overview of the publications on HAR based on ML and  ...  The CNN models obtained an average accuracy of 93.7% in activity recognition over an average number of 11 activities of daily life.  ... 
arXiv:2004.08821v1 fatcat:yd3tqw46lza3xamk4n2ekn254q

WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

Aras R. Dargazany, Paolo Stegagno, Kunal Mankodiya
2018 Mobile Information Systems  
This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.  ...  In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers.  ...  analytics. 3) Embedded DL for wearable multimodal sensor data fusion and integration: Radu et al. (2016) [6] is using smartphone and smartwatch for human or user activity recognition (HAR).  ... 
doi:10.1155/2018/8125126 fatcat:ty3a7n4in5aahbqyl7wum5vonq

Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

Dimitri Kraft, Karthik Srinivasan, Gerald Bieber
2020 Technologies  
Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems.  ...  The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly.  ...  To enrich the Notch, Smartwatch, and SmartFall datasets with additional activities of daily living, we added 500 random 10 s segments from the RealWorld Human Activity Recognition [47] dataset to the  ... 
doi:10.3390/technologies8040072 fatcat:l5bhldeubrf7rgrhmtqh64tzcm

User-centric Activity Recognition and Prediction Model using Machine Learning Algorithms

Namrata Roy, Rafiul Ahmed, Mohammad Rezwanul Huq, Mohammad Munem Shahriar
2021 International Journal of Advanced Computer Science and Applications  
Human Activity Recognition has been a dynamic research area in recent years. Various methods of collecting data and analyzing them to detect activity have been well investigated.  ...  We created our activity recognition dataset and used six machine learning algorithms to evaluate the recognition task.  ...  Min-Cheol Kwon and Sunwoong Choi built a system for recognizing activity using accelerometer and location data generated from a wrist-worn smartwatch using an Artificial Neural Network.  ... 
doi:10.14569/ijacsa.2021.0121265 fatcat:33keotfvvfazfcjyikxlr2kkia

Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

Hwiwon Lee, Sekyoung Youm
2021 Sensors  
As for an artificial intelligence (AI) algorithm to analyze the medication behavior, we constructed an object detection model (Model 1) using the faster region-based CNN technique and a second model that  ...  The collected data are used as a training dataset based on applying the latest convolutional neural network (CNN) technique.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21113594 pmid:34064177 fatcat:b5hvakfxcnccfj6xre6eiuvmuy

Review of Wearable Devices and Data Collection Considerations for Connected Health

Vini Vijayan, James Connolly, Joan Condell, Nigel McKelvey, Philip Gardiner
2021 Sensors  
This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology  ...  Wearable sensors can typically assess and quantify the wearer's physiology and are commonly employed for human activity detection and quantified self-assessment.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21165589 pmid:34451032 pmcid:PMC8402237 fatcat:qubolay6wjb35orvkwtunwz7e4

Human Activity Recognition for Physical Rehabilitation Using Wearable Sensors Fusion and Artificial Neural Networks

Eliasz Kantoch
2017 2017 Computing in Cardiology Conference (CinC)  
Each volunteer was asked to attach wearable device to the lower chest using elastic belt and perform activities of daily living (ADL) including sit, walk, stand and squats.  ...  The prototype of battery-operated wearable health tracking device which tracks body temperature and body motions was developed. Pulse oximeter sensor was used to track heart rate.  ...  Acknowledgements The scientific work is supported by the AGH University of Science and Technology in year 2017 as a research project No. 11.11.120.612.  ... 
doi:10.22489/cinc.2017.296-332 dblp:conf/cinc/Kantoch17 fatcat:x6hqe6syhzgcpj2w6qbt77zdwq

Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition

Emanuele Lattanzi, Matteo Donati, Valerio Freschi
2022 Sensors  
(namely multilayer and convolutional neural networks) trained for human activity recognition on board of a typical low-power wearable device.Through extensive experimental results, obtained on a public  ...  human activity recognition dataset, we derive Pareto curves that demonstrate the possibility of achieving a 4× reduction in memory usage and a 36× reduction in energy consumption, at fixed accuracy levels  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22072637 pmid:35408250 pmcid:PMC9003270 fatcat:jmdmksstivahpeszhwdrycgo34

Efficient Data Forwarding in Internet of Things and Sensor Networks

Dongkyun Kim, Houbing Song, Juan C. Cano, Wei Wang
2018 Wireless Communications and Mobile Computing  
Acknowledgments e guest editors would like to thank the authors for the great level of the contributions included in this special issue and the experts who participated in the review process to provide  ...  timely and constructive comments to the authors.  ...  Another approach in the area of WBAN that classifies the human activity based on artificial neural networks is presented in the paper titled "Recognition of Daily Human Activity using an Artificial Neural  ... 
doi:10.1155/2018/1736562 fatcat:godjnht5tvfobnj6ltxaloacda
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