Sensor Technology for Smart Homes

Ye, Juan; O’Grady, Michael J.; Baños, Oresti

doi:10.3390/s20247046

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…where L 2 is similar to L − 1 and x It p refers to a selected solution from X, and x rand denotes a random solution obtained using Equation (1).…”

Section: Gradient-based Optimization (Gbo)mentioning

confidence: 99%

“…The widespread use of mobile and smart devices has increased the demand for various smart home and Internet of Things (IoT) applications [1]. One of the most important applications is the Internet of Medical Things (IoMT) [2]/Internet of Healthcare Things (IoHT) [3], in which a real-time tracking, detection, and surveillance system is required for monitoring people's daily activities for medical diagnostics, healthy lifestyle purposes or assisted living environments [4].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

Helmi

Al-qaness

Dahou

et al. 2021

Entropy

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Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.

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“…where L 2 is similar to L − 1 and x It p refers to a selected solution from X, and x rand denotes a random solution obtained using Equation (1).…”

Section: Gradient-based Optimization (Gbo)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

Helmi

Al-qaness

Dahou

et al. 2021

Entropy

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show abstract

“…If a machine can detect human intention, it may be able to serve the human properly and safely. For instance, elderly individuals and patients feel it convenient to command against facilities in smart homes [2]. Collaborative robots might work safer with human workers in factories [3,4].…”

Section: Introductionmentioning

confidence: 99%

3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display

Yeamkuan

Chamnongthai

2021

Sensors

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This paper proposes a three-dimensional (3D) point-of-intention (POI) determination method using multimodal fusion between hand pointing and eye gaze for a 3D virtual display. In the method, the finger joint forms of the pointing hand sensed by a Leap Motion sensor are first detected as pointing intention candidates. Subsequently, differences with neighboring frames, which should be during hand pointing period, are checked by AND logic with the hand-pointing intention candidates. A crossing point between the eye gaze and hand pointing lines is finally decided by the closest distance concept. In order to evaluate the performance of the proposed method, experiments with ten participants, in which they looked at and pointed at nine test points for approximately five second each, were performed. The experimental results show the proposed method measures 3D POIs at 75 cm, 85 cm, and 95 cm with average distance errors of 4.67%, 5.38%, and 5.71%, respectively.

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A Novel Dynamic Chaotic Golden Jackal Optimization Algorithm for Sensor-Based Human Activity Recognition Using Smartphones for Sustainable Smart Cities

Abdel-salam,

Hassanien

2024

Studies in Systems, Decision and Control

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Sensor Technology for Smart Homes

Abstract: As advances in technology continue relentlessly, intriguing possibilities for smart home services have emerged [...]

Cited by 4 publications

References 10 publications

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display

A Novel Dynamic Chaotic Golden Jackal Optimization Algorithm for Sensor-Based Human Activity Recognition Using Smartphones for Sustainable Smart Cities

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