Hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition

Abudalfa, Shadi; Bouchard, Kévin

doi:10.1007/978-3-030-72802-1_11

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Working principle of most suggested systems is based on recognizing human activities of daily living (ADLs) [ 3 ]. Recognizing ADLs mainly deals with monitoring exact human activity [ 4 ] (such as squat on chair and rotation of the wrist) or monitoring items used by inhabitants in smart homes [ 5 ]. Our work mimics the second research direction by detecting position of objects located in smart homes.…”

Section: Introductionmentioning

confidence: 99%

Two-stage RFID approach for localizing objects in smart homes based on gradient boosted decision trees with under- and over-sampling

Abudalfa

Bouchard

2023

J Reliable Intell Environ

View full text Add to dashboard Cite

Developing automated systems with a reasonable cost for long-term care for elders is a promising research direction. Such smart systems are based on realizing activities of daily living (ADLs) to enable aging in place while preserving the quality of life of all inhabitants in smart homes. One of the research directions is based on localizing items used by elders to monitor their activities with fine-grained details of the progress. In this paper, we shed the light on this issue by presenting an approach for localizing items in smart homes. The presented method is based on applying machine learning algorithms to Radio Frequency IDentification (RFID) tags readings. Our approach achieves the required task through two stages. The first stage detects in which room the selected object is located. Then, the second one determines the exact position of the selected object inside the detected room. Additionally, we present an efficient approach based on gradient boosted decision trees for detecting the location of the selected object in a real-world smart home. Moreover, we employ some techniques of over- and under-sampling with data clustering for improving the performance of the presented techniques. Many experiments are conducted in this work to evaluate the performance of the presented approach for localizing objects in a real smart home. The results of the experiments have shown that our approach provides remarkable performance.

show abstract

Section: Introductionmentioning

confidence: 99%