scholarly journals Deep stair walking detection using wearable inertial sensor via long short-term memory network

2020 ◽  
Vol 9 (1) ◽  
pp. 238-246
Author(s):  
Gan Wei Nie ◽  
Nurul Fathiah Ghazali ◽  
Norazman Shahar ◽  
Muhammad Amir As'ari
Keyword(s):  
Short Term Memory ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Window Size ◽  
Sensor Data ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Long Short Term Memory ◽  
Stair Walking

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.

Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4
Author(s):  
Zadid Khan ◽  
Mashrur Chowdhury ◽  
Mhafuzul Islam ◽  
Chin-Ya Huang ◽  
Mizanur Rahman
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Short Term Memory ◽  
Attack Detection ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Long Short Term Memory ◽  
Vehicle Network

scholarly journals Currency movement forecasting using time series analysis and long short-term memory

2020 ◽  
Vol 1 (2) ◽  
pp. 71
Author(s):  
Kristina Sanjaya Putri ◽  
Siana Halim
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Short Term Memory ◽  
Window Size ◽  
Short Term ◽  
Term Memory ◽  
Series Analysis ◽  
Long Short Term Memory

Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.

scholarly journals Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018 ◽  
Vol 10 (10) ◽  
pp. 3765 ◽  
Author(s):  
Hyejung Chung ◽  
Kyung-shik Shin
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Short Term Memory ◽  
Time Window ◽  
Hybrid Approach ◽  
Window Size ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

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

Entropy ◽  
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.

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