Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.

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FALL DETECTION SYSTEM FOR ELDERLY PEOPLE - A Neural Network Approach

Proceedings of the International Conference on Biomedical Electronics and Devices ◽

10.5220/0003792003550358 ◽

2012 ◽

Keyword(s):

Neural Network ◽

Elderly People ◽

Detection System ◽

Fall Detection ◽

Network Approach ◽

Neural Network Approach

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Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽

10.3390/s20133635 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3635 ◽

Cited By ~ 1

Author(s):

Guoming Zhang ◽

Xiaoyu Ji ◽

Yanjie Li ◽

Wenyuan Xu

Keyword(s):

Smart Grid ◽

Power Consumption ◽

Real Time ◽

Condition Monitoring ◽

Resource Constraints ◽

Detection System ◽

Stable Operation ◽

Detection Accuracy ◽

Monitoring Method ◽

Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

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Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Pervasive and Mobile Computing ◽

10.1016/j.pmcj.2016.05.002 ◽

2017 ◽

Vol 34 ◽

pp. 3-13 ◽

Cited By ~ 42

Author(s):

Miguel Ángel Álvarez de la Concepción ◽

Luis Miguel Soria Morillo ◽

Juan Antonio Álvarez García ◽

Luis González-Abril

Keyword(s):

Elderly People ◽

Activity Recognition ◽

Detection System ◽

Fall Detection

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Anomaly Detection Using Deep Neural Network for IoT Architecture

Applied Sciences ◽

10.3390/app11157050 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7050

Author(s):

Zeeshan Ahmad ◽

Adnan Shahid Khan ◽

Kashif Nisar ◽

Iram Haider ◽

Rosilah Hassan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Anomaly Detection ◽

Deep Neural Network ◽

Detection System ◽

Traffic Monitoring ◽

Detection Accuracy ◽

Learning Models ◽

Prime Concern ◽

Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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