Smartphone based context-aware driver behavior classification using dynamic bayesian network

Chhabra, Rishu; Krishna, C. Rama; Verma, Seema

doi:10.3233/jifs-169995

Cited by 15 publications

(5 citation statements)

References 42 publications

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“…The intelligent wheelchair applies artificial intelligence technology and robotic technology to the wheelchair. Compared with ordinary wheelchairs, this kind of wheelchair has better decision-making autonomy, human-machine interaction, and environmental adaptability, which greatly improves the quality of life of the elderly and the disabled [6]. Indoor mapping and autonomous navigation are the basic functions of intelligent wheelchairs, so they have very important research value and significance.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

Kong¹,

Li²

2022

Journal of Sensors

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An intelligent wheelchair is a kind of service robot. The most critical part of its research field is the safe navigation control of a wheelchair, and path planning is the core part of the navigation control system. The use of a reasonable obstacle avoidance algorithm can not only ensure the safe navigation of wheelchair but also protect the personal safety of users. This paper analyzes the actual needs of the wheelchair following system according to the application scenarios and determines the way to follow the target. The sensor scheme and the wheelchair mobile platform are selected through the analysis of the following methods and the characteristics of each sensor, and the overall architecture of the wheelchair hardware system is designed. Then, the key components such as processor, laser ranging radar, signal strength positioning module, and ultrasonic sensor in the hardware system are selected, and the corresponding peripheral circuit is designed according to the interface type and working voltage of the sensor. In order to solve the problems of the complex calculation process, one-way reasoning, and poor accuracy of the original fault tree method, this paper designs an expert knowledge inference machine based on the Bayesian network. After completing the definition of component vulnerability, the inference engine can be activated, the construction of the directed acyclic network is completed based on the knowledge model mapping of the fault tree, the conditional probability table definition of the directed edge is completed, and the bidirectional probability inference of the Bayesian network can be started. Diagnostic inference obtains the posterior probability to characterize the system impact degree of the bottom event failure. Based on a comprehensive analysis of the research status and research trend of intelligent wheelchair obstacle avoidance path planning methods, this paper mainly studies the intelligent wheelchair path planning method. The environment modeling and path planning of global path planning for static obstacles and dynamic obstacles are realized, and a simulation platform for wheelchair obstacle avoidance path planning based on MATLAB is designed, and simulation experiments are carried out on this basis. The effectiveness of the studied algorithms and strategies in the dynamic obstacle environment is verified by simulation experiments.

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Section: Introductionmentioning

confidence: 99%

Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

Kong¹,

Li²

2022

Journal of Sensors

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“…The proliferation of mobile and wearable devices has opened new avenues for research into understanding human beings using the data collected from these devices 1 , 2 . For example, studies have utilized ubiquitous sensors installed in various locations, such as in the human body, a vehicle, or a room, to understand diverse user behaviors and situations, including a user’s preference for shopping items 3 and moments suitable for engaging in secondary tasks 4 , 5 .…”

Section: Background and Summarymentioning

confidence: 99%

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Kang

Choi

Park³

et al. 2023

Sci Data

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With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

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“…MCAS sebagai suatu sistem memiliki kemampuan untuk adaptasi terhadap suatu perubahan utamanya berkaitan dengan lokasi dan ruang (Bobek & Nalepa, 2017). Kemampuan adaptasi menjadikan MCAS memiliki akselerasi pada bidang organisasi informasi dan komunikasi dan memberikan banyak kelebihan bagi penggunannya (Chhabra, Krishna, & Verma, 2019;Mishra, Mishra, & Agrawal, 2010). Kelebihan demikian memberikan peluang untuk diterapkan dalam proses pembelajaran, utamanya Geografi.…”

Section: Pendahuluanunclassified

Implementasi m-learning berbasis Mobile Context Aware System (MCAS) dalam pembelajaran Geografi pada masa pandemi Covid-19

Putra

Islam

Sasmito

et al. 2021

JIHI3S

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Learning during the Covid-19 pandemic caused learning activity to be online and causes student’s boredom in Geography. Therefore, teachers must integrating the technology in learning process, with mobile learning (M-Learning) based on mobile context aware systems (MCAS). The study purpose is to determine student’s opinions about implementation of MCAS based M-Learning during the pandemic. This research is a descriptive qualitative with a mix method approach used collection techniques field research and literature study. The results showed that students had a positive opinion regarding the implementation of MCAS based M-Learning during the pandemic, with an average score of 3.40-3.70 out of 4.00. Pembelajaran pada masa pandemi Covid-19 menyebabkan pembelajaran menjadi online sehingga menyebabkan terjadinya kejenuhan siswa dalam proses pembelajaran Geografi. Oleh karena itu, guru harus mampu mengintegrasikan teknologi dalam pembelajaran, melalui mobile learning (M-Learning) berbasis mobile context aware systems (MCAS). Penelitian ini bertujuan mengetahui opini siswa dalam penerapan M-Learning berbasis MCAS pada masa pandemi. Jenis penelitian ini termasuk kualitatif deskriptif dengan pendekatan mix method serta teknik pengumpulan data berupa penelitian lapangan serta studi kepustakaan. Hasil penelitian menunjukkan bahwa siswa memiliki opini positif terkait implementasi M-Learning berbasis MCAS pada masa pandemi, dengan perolehan skor rata-rata skor 3,40-3,70 dari 4,00.

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Smartphone based context-aware driver behavior classification using dynamic bayesian network

Cited by 15 publications

References 42 publications

Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Implementasi m-learning berbasis Mobile Context Aware System (MCAS) dalam pembelajaran Geografi pada masa pandemi Covid-19

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