Decision Trees for Human Activity Recognition in Smart House Environments

Sánchez, Veralia Gabriela; Skeie, Nils-Olav

doi:10.3384/ecp18153222

Cited by 4 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several authors have opted to use decision-tree-based mechanisms to perform HAR [97]. In [98], the authors have used decision trees to analyse the database "Activity of Daily Living" (ADL) [99] obtaining an accuracy of 88.02% for 8 activities. A summary of all the work presented is given in Table 2.…”

Section: Summary Of the Main Har Unsupervised Learning Methodsmentioning

confidence: 99%

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Gómez-Ramos,

Duque-Domingo,

Zalama

et al. 2023

Electronics

View full text Add to dashboard Cite

As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time.

show abstract

Section: Summary Of the Main Har Unsupervised Learning Methodsmentioning

confidence: 99%

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Gómez-Ramos,

Duque-Domingo,

Zalama

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…We trained several models using different 6 machine learning [14] [15] algorithms namely, Logistic Regression [16], Random Forest [17], Decision Tree [18], Gaussian Naive Baye's, Support Vector, and K Neighbors Classifier [19]. So, we obtain different accuracies for each learning algorithm.…”

Section: Optimal Model Selection Algorithmmentioning

confidence: 99%

CURA: Real Time Artificial Intelligence and IoT based Fall Detection Systems for patients suffering from Dementia

Mishra,

Ngangbam,

Raj

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

According to the rising concern of the effects on the families due to dementia suffering patients, we aim to provide caretakers a work-life balance in which monitoring can be done with much more ease and efficiency in real time. This device can also be used in old age homes as well as hospitals which reduces the workload of the caretakers and helps them to easily monitor the patients. We aim to contribute for the betterment of the society and provide a virtual assistance for the patients suffering from dementia. The number of elderly people living alone has been increasing all over the world. If dementia has been detected at an early stage, the progress of disease can be slowed. The patients suffering from dementia are prone to falling quite frequently so as to detect that and to alert their caretakers to take necessary actions. In this study, we proposed a system in which we detect the real time state of the elderly people living alone by using the Machine Learning and IoT (Internet of Things) technology.We installed sensors inside a finger strap which is attached to the person. These sensors can detect the motions of the patient and predict their real time state to have a 24 by 7 support to provide assistance to the patients.

show abstract

“…Other classifiers, such as decision tree algorithms and its variants, have also been used for human activity recognition systems [129][130][131]. Table 5 summarizes machine learning classifiers that have been used in the literature for ADL recognition.…”

Section: The Role Of Artificial Intelligence (Ai) On Smart Tailored Ementioning

confidence: 99%

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Rodrigues

Postolache

Cercas

2020

Sensors

View full text Add to dashboard Cite

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

show abstract

Decision Trees for Human Activity Recognition in Smart House Environments

Cited by 4 publications

References 6 publications

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

CURA: Real Time Artificial Intelligence and IoT based Fall Detection Systems for patients suffering from Dementia

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Contact Info

Product

Resources

About