scholarly journals UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1267 ◽  
Author(s):  
Macarena Espinilla ◽  
Javier Medina ◽  
Chris Nugent
Keyword(s):  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Proximity Data ◽  
Location Data ◽  
Smart Watch ◽  
Real World Applications ◽  
Tools And Techniques

Many real-world applications, which are focused on addressing the needs of a human, require information pertaining to the activities being performed. The UCAmI Cup is an event held within the context of the International Conference on Ubiquitous Computing and Ambient Intelligence, where delegates are given the opportunity to use their tools and techniques to analyse a previously unseen human activity recognition dataset and to compare their results with others working in the same domain. In this paper, the human activity recognition dataset used relates to activities of daily living generated in the UJAmI Smart Lab, University of Jaén. The dataset chosen for the first edition of the UCAmI Cup represents 246 activities performed over a period of ten days carried out by a single inhabitant. The dataset includes four data sources: (i) event streams from 30 binary sensors, (ii) intelligent floor location data, (iii) proximity data between a smart watch worn by the inhabitant and 15 Bluetooth Low Energy beacons and (iv) acceleration of the smart watch. In this first edition of the UCAmI Cup, 26 participants from 10 different countries contacted the organizers to obtain the dataset.‬‬‬‬‬

scholarly journals Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 216 ◽  
Author(s):  
Naomi Irvine ◽  
Chris Nugent ◽  
Shuai Zhang ◽  
Hui Wang ◽  
Wing W. Y. NG
Keyword(s):  
Neural Network ◽  
Conflict Resolution ◽  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Data Driven ◽  
Smart Environments ◽  
Class Level

In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.

Analytical Review on Ontological Human Activity Recognition Approaches

2017 ◽  
Vol 13 (2) ◽  
pp. 58-78 ◽  
Author(s):  
Samaneh Zolfaghari ◽  
Mohammad Reza Keyvanpour ◽  
Raziyeh Zall
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Pervasive Computing ◽  
Daily Living ◽  
Smart Homes ◽  
Human Activity Recognition ◽  
Computing Technology ◽  
Monitoring Tool ◽  
Vast Number ◽  
Analytical Review

New advancements in pervasive computing technology have turned smart homes into a daily living monitoring tool increasingly used for elderly. Recently, using knowledge driven approaches such as ontology to introduce semantic smart homes has received attention due to their flexibility, reasoning and knowledge representation. Due to the vast number of ontological human activity recognition methods, the proposed ontological human activity recognition framework can be effective in analyzing and evaluating different methods in different applications and dealing with various challenges. Also, due to numerous challenges involved in different aspects of ontology-based human activity recognition in smart homes, this paper offers a classification for challenges in human activity recognition in ontology based systems. Then the proposed ontological human activity recognition framework is evaluated based on the proposed classification and ontology-based techniques which are thought to solve some of the challenges are examined and analyzed.

scholarly journals Human Activity Recognition with Smart Watch Based on H-SVM

2017 ◽  
pp. 179-186 ◽  
Author(s):  
Tao Tang ◽  
Lingxiang Zheng ◽  
Shaolin Weng ◽  
Ao Peng ◽  
Huiru Zheng
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Smart Watch

scholarly journals An Efficient Data Imputation Technique for Human Activity Recognition

2020 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Faisal Hussain ◽  
Nuno M. Garcia ◽  
Eftim Zdravevski
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Learning Models ◽  
K Nearest Neighbors ◽  
Daily Living Activities ◽  
Daily Life Activities ◽  
Machine Learning Models

Abstract The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.

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