Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context

Monti, Lorenzo; Tse, Rita; Tang, Su-Kit; Mirri, Silvia; Delnevo, Giovanni; Maniezzo, Vittorio; Salomoni, Paola

doi:10.3390/s22103692

Cited by 12 publications

(9 citation statements)

References 56 publications

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“…Smart learning consists of two components: the classroom and the curriculum. 3) Smart environments [33][34][35][36][37] focus on quality improvement, increasing management efficiency, and effectiveness monitoring to systematically manage the environment. This includes water management and climate disaster monitoring, as well as increasing public participation in natural resource conservation.…”

Section: The Characteristic Of Smart Universitymentioning

confidence: 99%

Distributed Communicative Language Training Platform Using Automatic Speech Recognition Technology for Smart University

Phuengrod,

Wannapiroon,

Nilsook

2023

Int. J. Emerg. Technol. Learn.

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The purpose of this research is to achieve the following objectives: 1) Synthesize documents and international research on the characteristics of a smart university. 2) Synthesize the processes of distributed communicative language training (DCLT). 3) Design the system architecture of a DCLT platform that utilizes automatic speech recognition (ASR) technology for a smart university. 4) Evaluate the appropriateness of a DCLT platform that utilizes ASR technology for a smart university. Nine experts were selected for this research. They were required to have more than five years of relevant experience in the field, including expertise in system architecture, distributed enterprise, language teaching, and ASR. The research instruments included a suitable assessment form for evaluating the system architecture of a DCLT platform that utilizes ASR technology for a smart university. The results of this research indicate that the DCLT platform, which utilizes ASR technology, was considered suitable for a smart university.

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Section: The Characteristic Of Smart Universitymentioning

confidence: 99%

Distributed Communicative Language Training Platform Using Automatic Speech Recognition Technology for Smart University

Phuengrod,

Wannapiroon,

Nilsook

2023

Int. J. Emerg. Technol. Learn.

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“…Transfer learning was witnessed in work of Monti et al [31] to perform occupancy sensing in campus area.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

Mane,

Rao

2023

Preprint

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Occupancy sensing is one of the integral parts of modern evolving security surveillance and monitoring system used over different types of infrastructure. With an aid of multiple form of occupancy sensors, the prime idea of occupancy sensing is to identify the presence or absence of occupants in specifically monitored area followed by transmitting back the sensing information either for storage or for prompting a set of commands from the connected control units. Review of existing schemes exhibits the presence of adoption of multiple methodologies over different variants of use-cases; however, they are quite case specific, uses expensive deployment process, and performs highly sophisticated operation. At present, there are no studies specifically reported of using multi-scale occupancy sensing suitable for large and distributed environment of Internet-of-Things (IoT). Therefore, the proposed study introduces a mechanism of novel multi-scale occupancy sensing considering a use case of smart university campus, although, it can be implemented over any form of different infrastructures too connected over IoT environment. The proposed scheme is implemented considering different types of cost-effective sensors, handheld devices and access points in order to identify the state of occupancy in large number of rooms present in the campus. The sensed data from distributed connected campus are aggregated over cloud server where they are subjected to suitable preprocessing to increase the data quality suitable for reliable prediction. Multiple set of potential learning-based schemes are integrated with proposed model to explore best fit model. This assessment scenario is not found reported in existing scheme to perform classification of states of occupancy. The study outcome shows Convolution Neural Network and Long Short-Term Memory to accomplish higher accuracy compared to other learning approach.

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“…Domotics has a variety of devices that can be programmed according to the application context, demonstrating the potential to be adapted to different scenarios such as education [3], healthcare [4], assisted living [5], etc. Domotics is also used for relaxation therapies to reduce stress by generating scenarios with smart lighting systems, environmental sound players, temperature variation, and scent diffusers [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Development of a Framework for the Communication System Based on KNX for an Interactive Space for UX Evaluation

Lopez-Aguilar,

Bustamante-Bello,

Navarro-Tuch

et al. 2023

Sensors

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Domotics (Home Automation) aims to improve the quality of life of people by integrating intelligent systems within inhabitable spaces. While traditionally associated with smart home systems, these technologies have potential for User Experience (UX) research. By emulating environments to test products and services, and integrating non-invasive user monitoring tools for emotion recognition, an objective UX evaluation can be performed. To achieve this objective, a testing booth was built and instrumented with devices based on KNX, an international standard for home automation, to conduct experiments and ensure replicability. A framework was designed based on Python to synchronize KNX systems with emotion recognition tools; the synchronization of these data allows finding patterns during the interaction process. To evaluate this framework, an experiment was conducted in a simulated laundry room within the testing booth to analyze the emotional responses of participants while interacting with prototypes of new detergent bottles. Emotional responses were contrasted with traditional questionnaires to determine the viability of using non-invasive methods. Using emulated environments alongside non-invasive monitoring tools allowed an immersive experience for participants. These results indicated that the testing booth can be implemented for a robust UX evaluation methodology.

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Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context

Cited by 12 publications

References 56 publications

Distributed Communicative Language Training Platform Using Automatic Speech Recognition Technology for Smart University

Distributed Communicative Language Training Platform Using Automatic Speech Recognition Technology for Smart University

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

Development of a Framework for the Communication System Based on KNX for an Interactive Space for UX Evaluation

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