Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data

Alqudah, Yazan A.; Sababha, Belal H.; Qaralleh, Esam A. Al; Yousseff, Tarek

doi:10.3991/ijim.v15i02.18303

Cited by 6 publications

(9 citation statements)

References 12 publications

(13 reference statements)

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“…This helped us to accurately identify the eight activities where some of them are difficult to distinguish between for the similarities among them. Different traditional and Deep learning algorithms have been applied to our dataset as found useful in several machine learning related works [39][40][41][42]. Among them, Bi-directional LSTM gives optimal accuracy.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>

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Section: Discussionmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

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“…When it came to the diagnosis of pneumonia, they had a success rate of 94.62%. The reader is encouraged to consult more recent review studies, such as [21][22][23], for further information about the current state of the art.…”

Section: Related Workmentioning

confidence: 99%

Automated Pneumonia Diagnosis using a 2D Deep Convolutional Neural Network with Chest X-Ray Images

Kassylkassova¹,

Omarov²,

Kazbekova³

et al. 2023

IJACSA

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Tiny air sacs in one or both lungs become inflamed as a result of the lung infection known as pneumonia. In order to provide the best possible treatment plan, pneumonia must be accurately and quickly diagnosed at initial stages. Nowadays, a chest X-ray is regarded as the most effective imaging technique for detecting pneumonia. However, performing chest X-ray analysis may be quite difficult and laborious. For this purpose, in this study we propose deep convolutional neural network (CNN) with 24 hidden layers to identify pneumonia using chest X-ray images. In order to get high accuracy of the proposed deep CNN we applied an image processing method as well as rescaling and data augmentation methods as shear_range, rotation, zooming, CLAHE, and vertical_flip. The proposed approach has been evaluated using different evaluation criteria and has demonstrated 97.2%, 97.1%, 97.43%, 96%, 98.8% performance in terms of accuracy, precision, recall, F-score, and AUC-ROC curve. Thus, the applied deep CNN obtain a high level of performance in pneumonia detection. In general, the provided approach is intended to aid radiologists in making an accurate pneumonia diagnosis. Additionally, our suggested models could be helpful in the early detection of other chest-related illnesses such as COVID-19.

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“…The work offered by [26] involved the development of a mobile application for smart bus transportation utilizing GPS. The work in [27,28] investigated actions using machine learning techniques to categorize them into distinct groups. The obtained data was used to train machine learning algorithms to classify the actions using mobile devices.…”

Section: Related Workmentioning

confidence: 99%

An Android Application Using Machine Learning Algorithm for Clique Detection in Issues Related to Transportation

Hussein

El-Salhi

ALazazma

et al. 2022

Int. J. Interact. Mob. Technol.

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This paper presents the development of a new variant mobile application with a local search algorithm that has been designed to detect routes in school transportation. The School bus routing problem relates to designing the optimum distribution/collection routes for the school buses serving geographically scattered students, and has been the focus of many academics for a long time. The proposed application system selects the best route for the buses to pick up the students from their houses. The system determines the optimal route for designated locations using google maps endings in efficient use of time and fuel. In order to meet the tremendous distance and the complex geographic structure, the proposed method divide the geographic regions into cliques that contain close houses to each other. The application is implemented on Android operating system since android is currently broadly utilized. The proposed framework is integrated with Google API to handle access to map display, data downloading and Google Maps servers. To evaluate the proposed application a survey was conducted. The findings from the reviews were impressive and substantial. The proposed solution of the school bus routing problem can be applied to solve problems related to the vehicles of institutions and organizations

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Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data

Cited by 6 publications

References 12 publications

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Automated Pneumonia Diagnosis using a 2D Deep Convolutional Neural Network with Chest X-Ray Images

An Android Application Using Machine Learning Algorithm for Clique Detection in Issues Related to Transportation

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