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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan, Chunjia Han
2021 Journal of Advanced Transportation  
It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively.  ...  Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models.  ...  For the real-world data, this study uses two human activity datasets in the following experiments. e first data is Smartphone-Based Recognition of Human Activities and Postural Transitions Data Set (HAPT  ... 
doi:10.1155/2021/2026895 fatcat:5u2mlffn5bgo5cuwdzdyg57zxu

Performance evaluation of machine learning based voting classifier system for human activity recognition

Sonika Jindal, I. K. Gujral Punjab Technical University, Jalandhar, India, Monika Sachdeva, Alok K. S. Kushwaha, I. K. Gujral Punjab Technical University, Jalandhar, India, Guru Ghasidas Vishwavidyalaya , Bilaspur, India
2022 Maǧallaẗ Al-Kuwayt li-l-ʿulūm  
The present work has proposed the voting classifier system for human activity recognition.  ...  and ascertaining the activity of a human being.  ...  Table 1 . 1 Existing studies related to human activity recognition.  ... 
doi:10.48129/kjs.splml.19189 fatcat:ap2noafcxnezbc6mpxxah3bu4a


K.H. Walse .
2016 International Journal of Research in Engineering and Technology  
The benchmark Human Activity Recognition dataset is considered for this work is acquired from UCI Machine Learning Repository which is available in public domain.  ...  Here, we presented our work in which certain types of human physical activities using accelerometer and gyroscope data generated by a mobile device.  ...  In the literature, extensive study found on approach in which mobile phone is used to collect human activity related data for recognition and adapt the interfaces to provide better usability experience  ... 
doi:10.15623/ijret.2016.0517002 fatcat:uiwjrmxgifgytkmk7gd6noco2e

A Review of Deep Learning-based Human Activity Recognition on Benchmark Video Datasets

Vijeta Sharma, Manjari Gupta, Anil Kumar Pandey, Deepti Mishra, Ajai Kumar
2022 Applied Artificial Intelligence  
This paper aims to present a comparative review of vision-based human activity recognition with the main focus on deep learning techniques on various benchmark video datasets comprehensively.  ...  Finally, we discuss future research directions and some open challenges on human activity recognition.  ...  This study evaluates the deep learning methods on mobile sensor-based human activity recognition datasets, not vision-based activity recognition datasets.  ... 
doi:10.1080/08839514.2022.2093705 fatcat:6on4g3sp3vaktnyyrk72k4mqta

Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition

Nhan Duc Nguyen, Duong Trong Bui, Phuc Huu Truong, Gu-Min Jeong
2018 Journal of Sensors  
This paper proposes a novel approach to recognize activities based on sensor-placement feature selection.  ...  After extracting various features, feature selection algorithms are applied separately on feature sets of each sensor to obtain the best feature set for each body position.  ...  In this paper, we propose a new scheme which is evaluated on the DaLiAc dataset for human daily living activity recognition based on sensor-placement feature selection.  ... 
doi:10.1155/2018/9762098 fatcat:o73wt6uv7ncs7p3pzgpuzopwry

Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data

Marco Manolo Manca, Barbara Pes, Daniele Riboni
2022 IEEE Access  
for activity recognition.  ...  INDEX TERMS Feature selection methods, human activity recognition, machine learning algorithms, mobile sensor data.  ...  DATASETS For our comparative study, we used five datasets meant for mobile human activity recognition.  ... 
doi:10.1109/access.2022.3183228 fatcat:dbhw3iq22fbmnkud7jvofs5zre

Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric Coordinates

Ibrahim Furkan Ince
2020 Applied Sciences  
Therefore, in this paper, a novel scale and rotation invariant human activity recognition system, which can also work in low dimensional feature space is presented.  ...  Human activity recognition (HAR) has been an active area in computer vision with a broad range of applications, such as education, security surveillance, and healthcare.  ...  There have been various studies for vision-based human activity recognition in the literature.  ... 
doi:10.3390/app10238474 fatcat:gbm66ezmgrg6nai5qzung5fmum

VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition [article]

Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian
2022 arXiv   pre-print
We apply VFDS on the Human Activity Recognition (HAR) task where the performance-cost trade-off is critical in its practice.  ...  In order to optimize the performance-cost trade-off, one would select features to observe a priori.  ...  We benchmark our model on four different activity recognition datasets and have compared it with various dynamic and static feature selection benchmarks.  ... 
arXiv:2204.00130v1 fatcat:hxbkpr33ojdhtakfscefoqkih4

Activity Recognition and Creation of Web Service for Activity Recognition using Mobile Sensor Data using Azure Machine Learning Studio

Muhammad Owais Raza, Nazia Pathan, Aqsa Umar, Raheem Bux
2021 Review of Computer Engineering Research  
Acknowledgement: All authors contributed equally to the conception and design of the study.  ...  CONCLUSION The motive of this paper is to create a web service for activity recognition using azure ml studio and a benchmarking dataset.  ...  Here in this case, we have used a benchmarking dataset [15] to train and evaluate model. This dataset has two parts; one for training and one for testing.  ... 
doi:10.18488/journal.76.2021.81.1.7 fatcat:5ttsgdk24zeh5pxhzgpru6na5m

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial [article]

Jianfei Yang, Xinyan Chen, Dazhuo Wang, Han Zou, Chris Xiaoxuan Lu, Sumei Sun, Lihua Xie
2022 arXiv   pre-print
In this paper, we highlight the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing  ...  Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition.  ...  NTU-Fi is our proposed dataset for this benchmark that includes both human activity recognition (HAR) and human identification (Human ID) tasks.  ... 
arXiv:2207.07859v1 fatcat:nf52mkumjbczpns54zayk3gcfa

The Action Similarity Labeling Challenge

O. Kliper-Gross, T. Hassner, L. Wolf
2012 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We present baseline results on our benchmark, and compare them to human performance.  ...  To facilitate the development of methods for action recognition, several video collections, along with benchmark protocols, have previously been proposed.  ...  Human Survey on ASLAN To validate our database, we have conducted a human survey on a randomly selected subset of ASLAN. 4 The survey results were used for the following purposes: 1) Test the difficulty  ... 
doi:10.1109/tpami.2011.209 pmid:22262724 fatcat:o4je6q3ilbhrpb6yfrinrsknsi

Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Chengyin Liu, Zhaoshuo Jiang, Xiangxiang Su, Samuel Benzoni, Alec Maxwell
2019 Sensors  
Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder.  ...  study.  ...  Benchmark Problem In the benchmark problem, human activity recognition was performed using acquired floor vibration signals.  ... 
doi:10.3390/s19173720 pmid:31466268 pmcid:PMC6749303 fatcat:sut62khls5hgjk2mx2lrzgebcm

A Novel Action Recognition Framework Based on Deep-Learning and Genetic Algorithms

Abdullah Asim Yilmaz, Mehmet Serdar Guzel, Erkan Bostanci, Iman Askerzade
2020 IEEE Access  
This study proposes a novel deep-learningbased architecture for the recognition and prediction of human actions based on a hybrid model.  ...  By adapting the aforementioned architecture, reliable features are obtained for the training procedure.  ...  This study presents generic dynamic Bayesian network models which combine multiple features for human activity recognition, and also a framework to learn the deep belief network (DBN) model which relates  ... 
doi:10.1109/access.2020.2997962 fatcat:ig7lvhlpjvbmzmn7yqtvvvbcku

Integrating Deep Features for Material Recognition [article]

Yan Zhang, Mete Ozay, Xing Liu, Takayuki Okatani
2016 arXiv   pre-print
We examine the proposed method on three benchmark datasets for material recognition.  ...  Given a set of representations of multiple pre-trained CNNs, we first compute activations of features using the representations on the images to select a set of samples which are best represented by the  ...  Section IV provides experiments conducted on several benchmark datasets. Section V concludes this study. II.  ... 
arXiv:1511.06522v6 fatcat:hsdty7pvdnfw5chiy36xkiguai

Study of Multi-Classification of Advanced Daily Life Activities on SHIMMER Sensor Dataset

Amir Mehmood, Akhter Raza, Adnan Nadeem, Umair Saeed
2016 International Journal of Communication Networks and Information Security  
benchmark data set of the shimmer sensors placed on human body, to recognize the human activity.  ...  Human activity recognition (HAR) is the one example, where data received from wearable sensors are further processed to identify the activities executed by the individuals.  ...  The main cause of study related to the recognition of human activity is to identify the pattern of physical or postural movements.  ... 
dblp:journals/ijcnis/MehmoodRNS16 fatcat:cp2afbupgnhxbifghrkr6tf4cu
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