A Real-Time Patient Monitoring Framework for Fall Detection

Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.

Download Full-text

Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term Memory (LSTM) Network

Intelligent Data-Analytics for Condition Monitoring ◽

10.1016/b978-0-323-85510-5.00008-9 ◽

2021 ◽

pp. 165-192

Author(s):

Hasmat Malik ◽

Nuzhat Fatema ◽

Atif Iqbal

Keyword(s):

Wind Speed ◽

Wind Power ◽

Data Analytics ◽

Short Term Memory ◽

Power Production ◽

Short Term ◽

Term Memory ◽

Wind Speed Forecasting ◽

Long Short Term Memory ◽

Lstm Network

Download Full-text

Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices

Sensors ◽

10.3390/s22010164 ◽

2021 ◽

Vol 22 (1) ◽

pp. 164

Author(s):

Marek Wójcikowski

Keyword(s):

Heart Rate ◽

Real Time ◽

Time Domain ◽

Short Term Memory ◽

Computational Cost ◽

Computational Effort ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.

Download Full-text

Multi-camera, multi-person, and real-time fall detection using long short term memory

Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications ◽

10.1117/12.2580700 ◽

2021 ◽

Cited By ~ 1

Author(s):

Mohammad Taufeeque ◽

Samad Koita ◽

Nicolai Spicher ◽

Thomas M. Deserno

Keyword(s):

Real Time ◽

Short Term Memory ◽

Fall Detection ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Time Fall

Download Full-text

User Presence Inference via Encrypted Traffic of Wireless Camera in Smart Homes

Security and Communication Networks ◽

10.1155/2018/3980371 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Xiaoyu Ji ◽

Yushi Cheng ◽

Wenyuan Xu ◽

Xinyan Zhou

Keyword(s):

Short Term Memory ◽

Smart Homes ◽

Fall Detection ◽

Personal Privacy ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network ◽

Cumulative Sum Control ◽

Encrypted Traffic

Wireless cameras are widely deployed in smart homes for security guarding, baby monitoring, fall detection, and so on. Those security cameras, which are supposed to protect users, however, may in turn leak a user’s personal privacy. In this paper, we reveal that attackers are able to infer whether users are at home or not, that is, the user presence, by eavesdropping the traffic of wireless cameras from distance. We propose HomeSpy, a system that infers user presence by inspecting the intrinsic pattern of the wireless camera traffic. To infer the user presence, HomeSpy first eavesdrops the wireless traffic around the target house and detects the existence of wireless cameras with a Long Short-Term Memory (LSTM) network. Then, HomeSpy infers the user presence using the bitrate variation of the wireless camera traffic based on a cumulative sum control chart (CUSUM) algorithm. We implement HomeSpy on the Android platform and validate it on 20 cameras. The evaluation results show that HomeSpy can achieve a successful attack rate of 97.2%.

Download Full-text

Abstract: Multi-camera, Multi-person, and Real-time Fall Detection using Long Short Term Memory

Bildverarbeitung für die Medizin 2021 - Informatik aktuell ◽

10.1007/978-3-658-33198-6_29 ◽

2021 ◽

pp. 124-124

Author(s):

Christian Heinrich ◽

Samad Koita ◽

Mohammad Taufeeque ◽

Nicolai Spicher ◽

Thomas M. Deserno

Keyword(s):

Real Time ◽

Short Term Memory ◽

Fall Detection ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Time Fall

Download Full-text

A Distributed Denial of Service Attack Detection System using Long Short Term Memory with Singular Value Decomposition

2020 IEEE 2nd International Conference on Cyberspac (CYBER NIGERIA) ◽

10.1109/cybernigeria51635.2021.9428870 ◽

2021 ◽

Author(s):

Chukwuemeka Christian Ugwu ◽

Olumide Olayinka Obe ◽

Olugbemiga Solomon Popoqla ◽

Adebayo Olusola Adetunmbi

Keyword(s):

Short Term Memory ◽

Detection System ◽

Denial Of Service ◽

Attack Detection ◽

Distributed Denial Of Service ◽

Short Term ◽

Denial Of Service Attack ◽

Service Attack ◽

Long Short Term Memory ◽

Value Decomposition

Download Full-text

A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽

10.3390/s21041181 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1181

Author(s):

Chenhao Zhu ◽

Sheng Cai ◽

Yifan Yang ◽

Wei Xu ◽

Honghai Shen ◽

...

Keyword(s):

Kalman Filter ◽

Standard Deviation ◽

Error Compensation ◽

Random Vibration ◽

Short Term Memory ◽

Combined Method ◽

Short Term ◽

Mems Gyroscope ◽

Long Short Term Memory ◽

Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

Download Full-text

Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

SN Computer Science ◽

10.1007/s42979-021-00507-w ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Kate Highnam ◽

Domenic Puzio ◽

Song Luo ◽

Nicholas R. Jennings

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Traffic ◽

Short Term Memory ◽

Domain Names ◽

Control Networks ◽

Detection Techniques ◽

Lstm Network ◽

And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

Download Full-text

Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

Download Full-text