Real-Time Human Activity Generation using Bidirectional Long Short Term Memory Networks

Aswal, Vivek; Sreeram, Vineet; Kuchik, Aishwarya; Ahuja, Simran; Patel, Himali

doi:10.1109/iciccs48265.2020.9121096

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another approach, Ref. [54] employs Bidirectional long short-term memory (LSTM) networks for data generation using the WISDM dataset, a publicly available tri-axial accelerometer dataset. The study focuses on assessing the similarity between generated and original data and explores the impact of synthetic data on classifier performance.…”

Section: Accelerometer and Imu Modalitiesmentioning

confidence: 99%

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

Kaseris,

Kostavelis,

Malassiotis

2024

MAKE

View full text Add to dashboard Cite

Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration.

show abstract

Section: Accelerometer and Imu Modalitiesmentioning

confidence: 99%

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

Kaseris,

Kostavelis,

Malassiotis

2024

MAKE

View full text Add to dashboard Cite

show abstract

Improving the Performance and Explainability of Indoor Human Activity Recognition in the Internet of Things Environment

et al. 2022

View full text Add to dashboard Cite

Traditional indoor human activity recognition (HAR) has been defined as a time-series data classification problem and requires feature extraction. The current indoor HAR systems still lack transparent, interpretable, and explainable approaches that can generate human-understandable information. This paper proposes a new approach, called Human Activity Recognition on Signal Images (HARSI), which defines the HAR problem as an image classification problem to improve both explainability and recognition accuracy. The proposed HARSI method collects sensor data from the Internet of Things (IoT) environment and transforms the raw signal data into some visual understandable images to take advantage of the strengths of convolutional neural networks (CNNs) in handling image data. This study focuses on the recognition of symmetric human activities, including walking, jogging, moving downstairs, moving upstairs, standing, and sitting. The experimental results carried out on a real-world dataset showed that a significant improvement (13.72%) was achieved by the proposed HARSI model compared to the traditional machine learning models. The results also showed that our method (98%) outperformed the state-of-the-art methods (90.94%) in terms of classification accuracy.

show abstract

Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset

Bollampally,

Kavitha,

Sumanya

et al. 2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

This study investigates the application of a Bidirectional Long Short-Term Memory (BiLSTM) model for Human Activity Recognition (HAR) using the PAMAP2 dataset. The aim was to enhance the accuracy and efficiency of recognizing daily activities captured by wearable sensors. The proposed BiLSTM-based model achieved outstanding performance, with 98.75% training accuracy and 99.27% validation accuracy. It also demonstrated high precision, recall, and F1 scores (all 0.99). Comparative analysis with state-of-the-art models, including Deep-HAR and CNN-BiLSTM-BiGRU, revealed that the proposed BiLSTM model surpassed their performance. These results highlight the potential of the proposed approach for real-time HAR applications in edge computing, particularly where accurate and efficient activity recognition is crucial.

show abstract

Real-Time Human Activity Generation using Bidirectional Long Short Term Memory Networks

Cited by 3 publications

References 17 publications

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

Improving the Performance and Explainability of Indoor Human Activity Recognition in the Internet of Things Environment

Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset

Contact Info

Product

Resources

About