IoT-Based Vibration Sensor Data Collection and Emergency Detection Classification using Long Short Term Memory (LSTM)

This paper proposes a stair walking detection via Long-short Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.

Download Full-text

Multi-sensor Data Mining of Single Train Braking System Based on Long Short-term Memory Network

2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) ◽

10.1109/piers-spring46901.2019.9017906 ◽

2019 ◽

Author(s):

Wen Jing Liu ◽

Guo Chun Wan ◽

Mei Song Tong

Keyword(s):

Data Mining ◽

Short Term Memory ◽

Sensor Data ◽

Short Term ◽

Term Memory ◽

Braking System ◽

Memory Network ◽

Long Short Term Memory

Download Full-text

Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

Entropy ◽

10.3390/e23111457 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1457

Author(s):

Ifigenia Drosouli ◽

Athanasios Voulodimos ◽

Georgios Miaoulis ◽

Paris Mastorocostas ◽

Djamchid Ghazanfarpour

Keyword(s):

Short Term Memory ◽

Bayesian Optimization ◽

Sensor Data ◽

Learning Approaches ◽

Short Term ◽

Transportation Mode ◽

Term Memory ◽

Mode Detection ◽

Long Short Term Memory ◽

The Impact

The advancement of sensing technologies coupled with the rapid progress in big data analysis has ushered in a new era in intelligent transport and smart city applications. In this context, transportation mode detection (TMD) of mobile users is a field that has gained significant traction in recent years. In this paper, we present a deep learning approach for transportation mode detection using multimodal sensor data elicited from user smartphones. The approach is based on long short-term Memory networks and Bayesian optimization of their parameters. We conducted an extensive experimental evaluation of the proposed approach, which attains very high recognition rates, against a multitude of machine learning approaches, including state-of-the-art methods. We also discuss issues regarding feature correlation and the impact of dimensionality reduction.

Download Full-text

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Applied Sciences ◽

10.3390/app9194156 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4156 ◽

Cited By ~ 4

Author(s):

Zhengmin Kong ◽

Yande Cui ◽

Zhou Xia ◽

He Lv

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Remaining Useful Life ◽

Data Driven ◽

Sensor Data ◽

Complex Data ◽

Short Term ◽

Term Memory ◽

Useful Life ◽

Long Short Term Memory

Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

Download Full-text

Tool Remaining Useful Life Prediction Using Deep Transfer Reinforcement Learning Based on Long Short Term Memory Networks

10.21203/rs.3.rs-352693/v1 ◽

2021 ◽

Author(s):

Jiachen Yao ◽

Baochun Lu ◽

Junli Zhang

Keyword(s):

Reinforcement Learning ◽

Tool Wear ◽

Short Term Memory ◽

Remaining Useful Life ◽

Sensor Data ◽

Short Term ◽

Term Memory ◽

Useful Life ◽

Long Short Term Memory ◽

Lstm Network

Abstract Tool wear and faults will affect the quality of machined workpiece and damage the continuity of manufacturing. The accurate prediction of remaining useful life (RUL) is significant to guarantee processing quality and improve productivity of automatic system. At present, the most methods for tool RUL prediction are trained by history fault data. However, when researching on new types of tools or processing high value parts, fault datasets are difficult to acquired, which led to RUL prediction a challenge under limited fault data. To overcome shortcomings of above prediction methods, a deep transfer reinforcement learning (DTRL) network based on long short term memory (LSTM) network is presented in this paper. Local features are extracted from consecutive sensor data to track the tool states, and the trained network size can be dynamically adjusted by controlling time sequence length. Then in DTRL network, LSTM network is employed to construct the value function approximation for smoothly processing temporal information and mining long-term dependencies. On this basis, a novel strategies of Q-function update and transfer are presented to transfer the DRL network trained by historical fault data to a new tool for RUL prediction. Finally, tool wear experiments are performed to validate effectiveness of the DTRL model. The prediction result demonstrate that the proposed method has high accuracy and generalization for similar tools and cutting conditions.

Download Full-text