An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers

Mahariba, A. Jackulin; Uthra, R. Annie; Brunet, R. Golda

doi:10.1016/j.eswa.2021.116389

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…One of the most influential derivatives of the Transformer architecture is Bidirectional Encoder Representations from Transformers (BERT), an acronym for Bidirectional Encoder Representations from Transformers. BERT is a pre-trained transformer model specially designed for natural language understanding tasks [35]. What sets BERT apart is its capacity to generate contextualised word representations, meaning it comprehends words based on their context within a sentence.…”

Section: Deep Learning Architectures For Animalmentioning

confidence: 99%

A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

Kumar,

Luo,

Shaukat

2023

IJACSA

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Integrating deep learning techniques into computer vision application has ushered in a new era of automated analysis and interpretation of visual data. In recent years, a surge of interest has been witnessed in applying these methodologies towards detecting animals in video streams, promising transformative impacts on diverse fields such as ecology and agriculture. This paper presents an extensive and meticulous review of the latest deep-learning approaches employed for animal detection in video data. This study looks closely at ways to detect animals in videos using deep learning. This study explores various Deep learning methods for detecting many animals in multiple environments. The analysis also pays close attention to preparing the data, picking out important features, and reusing what has been learned from one task to help with another. In addition to highlighting successful methodologies, this review addresses the challenges and limitations inherent in these approaches issues such as limited data availability and adapting to technological advancements present significant hurdles. Recognising and understanding these challenges is crucial in shaping the future focus of research endeavours. Thus, this comprehensive review is an indispensable tool for anyone keen on employing these potent computer methods for animal detection in videos. It takes the latest ideas and shows where study can explore further to improve them. Furthermore, this comprehensive review has demonstrated that a more sustainable and balanced relationship between humans and animals can be achieved by harnessing the power of deep learning in animal detection. This research contributes to computer vision and holds immense promise in safeguarding biodiversity and promoting responsible land use practices, especially within agricultural domains. The insights from this study propel us towards a future where advanced technology and ecological harmony go hand in hand, ultimately benefiting both humans and the animal kingdom. The survey aims to provide a comprehensive overview of the cutting-edge developments in applying deep learning models for animal detection through cameras by elucidating the significance of these techniques in advancing the accuracy and efficiency of animal detection processes.

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Section: Deep Learning Architectures For Animalmentioning

confidence: 99%

A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

Kumar,

Luo,

Shaukat

2023

IJACSA

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“…Several types of fall detection and prediction methods have been devised and developed based on the sensors they use. The types are wearable accelerometer Fall detection systems such as those of [3,4,5,6] and IoT based Fall Detection systems by [7,8,9,10,11,12,13] and camera-based fall detection systems [14,15,16,17]. Fall risk assessment-involves studying the medical records and history of falls of an individual who is most likely old or unhealthy or are in-patients who are recovering from a surgery [18,19,20,21].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of LSTM Model for Fall Prediction Using IMU

Chandramouleesvar

Swetha

Visalakshi

2023

IoT, Cloud and Data Science

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One of the major contributor leading to cause of unintentional injuries after motor vehicle crashes and poisoning, is Falls. The existing Fall Prediction Algorithms are used to predict falls in older or disabled people by analyzing their fall history, capturing their movements through visual sensors (cameras, thermal imaging etc.) in a fixed environment, using inertial sensors to identify the patterns of movements. These algorithms are monologues for each person as they learn from their history and predict falls specific only to that person. The algorithm proposed in this paper aims to predict falls using kinematic data such as accelerometer, magnetometer, and gyroscopic values, for any user. This work involves developing an algorithm capable of predicting falls and to achieve this, we use Long Short-Term Memory (LSTM). The benefit of this algorithm is to prevent trauma to the body or at least reduce the impact of fall and the fatality caused by it. In the future, this algorithm can be used to design a device to predict falls in real-time to scenario be used by everyone irrespective of gender, age, and health.

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“…In recent years, machine-learning algorithms have been widely used as a data-driven modeling method in many fields, such as geotechnical engineering [ 27 ], traffic safety [ 28 ], material engineering [ 29 ], and biomedicine [ 30 ]. To improve computational accuracy and computational efficiency, Breima proposed the random forest (RF) algorithm in 2001 [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Both E-Jet Printing Ejection Cycle Time and Droplet Diameter Based on Random Forest Regression

et al. 2023

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Electrohydrodynamic jet (E-jet) printing has broad application prospects in the preparation of flexible electronics and optical devices. Ejection cycle time and droplet size are two key factors affecting E-jet-printing quality, but due to the complex process of E-jet printing, it remains a challenge to establish accurate relationships among ejection cycle time and droplet diameter and printing parameters. This paper develops a model based on random forest regression (RFR) for E-jet-printing prediction. Trained with 72 groups of experimental data obtained under four printing parameters (voltage, nozzle-to-substrate distance, liquid viscosity, and liquid conductivity), the RFR model achieved a MAPE (mean absolute percent error) of 4.35% and an RMSE (root mean square error) of 0.04 ms for eject cycle prediction, as well as a MAPE of 2.89% and an RMSE of 0.96 μm for droplet diameter prediction. With limited training data, the RFR model achieved the best prediction accuracy among several machine-learning models (RFR, CART, SVR, and ANN). The proposed prediction model provides an efficient and effective way to simultaneously predict the ejection cycle time and droplet diameter, advancing E-jet printing toward the goal of accurate, drop-on-demand printing.

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An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers

Cited by 11 publications

References 22 publications

A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

Development of LSTM Model for Fall Prediction Using IMU

Prediction of Both E-Jet Printing Ejection Cycle Time and Droplet Diameter Based on Random Forest Regression

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