scholarly journals MHARS: A mobile system for human activity recognition and inference of health situations in ambient assisted living

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
José Daniel Pereira Ribeiro Filho ◽  
Francisco José Da Silva e Silva ◽  
Luciano Reis Coutinho ◽  
Berto De Tácio Pereira Gomes
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Human Activity Recognition ◽  
Mobile System

scholarly journals Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

2019 ◽  
Vol 8 (6S4) ◽  
pp. 531-540
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Independent Living ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Human Activity Recognition ◽  
Living Environment ◽  
Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

scholarly journals MHARS: Sistema Móvel de Reconhecimento de Atividades em Ambient Assisted Living

2015 ◽  
Author(s):  
J.D.P. Ribeiro Filho ◽  
F.J. Da Silva e Silva ◽  
L.R. Coutinho ◽  
B. Gomes
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Recognition System ◽  
Human Activity Recognition

O objetivo deste artigo é apresentar o MHARS (Mobile Human Activity Recognition System), um sistema móvel voltado para o acompanhamento de pacientes no contexto de Ambient Assisted Living (AAL), que permite o reconhecimento das atividades realizadas pelo usuário bem como a detecção da sua intensidade me tempo real. O MHARS foi projetado para poder obter dados de difererentes sensores, reconecer as atividades e medir sua intensidade em diferentes níveis de mobilidade do usuário, possui mecanismos para a inferência de situações relativas ao estado de saúde do paciente, bem como suporte à execução de ações de forma a poder reagir a eventos que mereçam a atenção por parte de seus cuidadores. Experimentos realizados demonstram que o MHARS possui boa acurácia e apresenta um consumo adequado de recursos do dispositivo móvel.

scholarly journals Human Activity Recognition in AAL Environments Using Random Projections

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Robertas Damaševičius ◽  
Mindaugas Vasiljevas ◽  
Justas Šalkevičius ◽  
Marcin Woźniak
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Feature Space ◽  
Human Activity Recognition ◽  
The State ◽  
Random Projections ◽  
Healthcare Applications ◽  
Subject Identification

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject’s body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

scholarly journals Smartphone and Smartwatch for Human Activity Recognition

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mashhour M Bani Amer
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Elderly Care ◽  
Confusion Matrix ◽  
Ambient Assisted Living ◽  
Human Activity Recognition ◽  
Learning System ◽  
Support Vector ◽  
Close Monitoring

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, elderly care, rehabilitation, and entertainment and close monitoring. Smartphones are already used to recognize activity. Most of the research done in this field placed a restriction on fixing the smartphone securely in a certain location on the human body, along with the machine learning system, to promote the process of classifying raw data from smartphone sensors to human activities. Smartwatches solve this limitation by placing them in a consistent position, which becomes steady and precisely sensitive to body movements. For this experiment, we evaluate both the accelerometer and the gyroscope sensor on the smartphone and the smartwatch, and decide which sensors hybrid does superiorly. 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 builtin MATLAB 2018a. We used confusion matrix and random simulation to compare the accuracy and efficiency of those models. The results showed that the accelerometer sensors combination has the highest accuracy among other combinations and achieved an overall accuracy of 97.7% using SVM that gives the best performance among all other classifiers.

scholarly journals A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Enea Cippitelli ◽  
Samuele Gasparrini ◽  
Ennio Gambi ◽  
Susanna Spinsante
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Cost Effective ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Recognition Algorithm ◽  
Support Vector

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Sign in / Sign up

Export Citation Format

Share Document