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Tsai CY, Majumdar A, Wang Y, Hsu WH, Kang JH, Lee KY, Tseng CH, Kuan YC, Lee HC, Wu CJ, Houghton R, Cheong HI, Manole I, Lin YT, Li LYJ, Liu WT. Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2023; 29:1429-1439. [PMID: 36281493 DOI: 10.1080/10803548.2022.2135281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.
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Affiliation(s)
- Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Yija Wang
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Wen-Hua Hsu
- College of Medicine, Taipei Medical University, Taiwan
| | - Jiunn-Horng Kang
- Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taiwan
- Research Centre of Artificial Intelligence in Medicine, Taipei Medical University, Taiwan
- College of Biomedical Engineering, Taipei Medical University, Taiwan
| | - Kang-Yun Lee
- Shuang Ho Hospital, Taipei Medical University, Taiwan
| | | | - Yi-Chun Kuan
- College of Medicine, Taipei Medical University, Taiwan
- Shuang Ho Hospital, Taipei Medical University, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taiwan
- Dementia Centre, Taipei Medical University-Shuang Ho Hospital, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taiwan
| | - Cheng-Jung Wu
- Shuang Ho Hospital, Taipei Medical University, Taiwan
| | - Robert Houghton
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - He-In Cheong
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Iulia Manole
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Yin-Tzu Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taiwan
| | - Lok-Yee Joyce Li
- Department of Medicine, Shin Kong Wu-Ho-Su Memorial Hospital, Taiwan
| | - Wen-Te Liu
- College of Medicine, Taipei Medical University, Taiwan
- Research Centre of Artificial Intelligence in Medicine, Taipei Medical University, Taiwan
- Shuang Ho Hospital, Taipei Medical University, Taiwan
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Rau F, Soto I, Zabala-Blanco D, Azurdia-Meza C, Ijaz M, Ekpo S, Gutierrez S. A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23114997. [PMID: 37299722 DOI: 10.3390/s23114997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023]
Abstract
This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.
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Affiliation(s)
- Francisco Rau
- CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
| | - Ismael Soto
- CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
| | - David Zabala-Blanco
- Department of Computer Science and Industry, Universidad Católica del Maule, Talca 3480112, Chile
| | - Cesar Azurdia-Meza
- Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile
| | - Muhammad Ijaz
- Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
| | - Sunday Ekpo
- Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
| | - Sebastian Gutierrez
- Faculty of Engineering, Universidad Autónoma de Chile, Santiago 7500912, Chile
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An MRI Scans-Based Alzheimer's Disease Detection via Convolutional Neural Network and Transfer Learning. Diagnostics (Basel) 2022; 12:diagnostics12071531. [PMID: 35885437 PMCID: PMC9318866 DOI: 10.3390/diagnostics12071531] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 02/04/2023] Open
Abstract
Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85−3.88%, 2.43−2.66%, and 1.8−40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.
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Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112411600] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many solutions have been proposed to address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver’s distraction due to the driver’s head panning. These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). The proposed approach is compared with six existing state-of-the-art approaches using four benchmark datasets, including DrivFace dataset, Boston University (BU) dataset, FT-UMT dataset, and Pointing’04 dataset. The proposed approach outperforms the existing approaches achieving an accuracy of 94.43%, 92.08%, 96.63%, and 83.25% on standard datasets.
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