Publications | Laboratory of Smart, PErvasive and Mobile systems Engineering (SPEME)

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Ma, C; Du, J; Gravina, R

Abnormal Behavior Detection Based on Activity Level Using Fuzzy Inference System for Wheelchair Users Journal Article

In: Human-centric Computing and Information Sciences, vol. 12, 2022, (cited By 0).

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Valero, C I; Gravina, S; Ciuffetti, A; Gioia, M Di; Gravina, R; Boronat, F; Palau, C E

EBASI: IoT-Based Emotion and Behaviour Recognition System Against Elderly People Social Isolation Conference

International Symposium on Intelligent and Distributed Computing (IDC 2021), vol. 1026, 2022, (cited By 1).

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Ma, C; Lee, C K Man; Du, J; Li, Q; Gravina, R

Work Engagement Recognition in Smart Office Conference

3rd International Conference on Industry 4.0 and Smart Manufacturing, vol. 200, 2022, (cited By 0).

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Apicella, Giulia; D’Aniello, Giuseppe; Fortino, Giancarlo; Gaeta, Matteo; Gravina, Raffaele; Tramuto, Luca Giuseppe

An Adaptive Neuro-Fuzzy Approach for Activity Recognition in Situation-aware Wearable Systems Proceedings Article

In: 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), pp. 1-6, 2022.

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Zan, Shiming; Pang, Yu; Gravina, Raffaele; Cao, Enling; Li, Ye; Zang, Weilin

A Deep Reinforcement Learning Based Approach for Intelligent Reconfigurable Surface Elements Selection Proceedings Article

In: 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), pp. 1-7, 2022.

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Apicella, Giulia; D’Aniello, Giuseppe; Fortino, Giancarlo; Gaeta, Matteo; Gravina, Raffaele; Tramuto, Luca Giuseppe

A Situation-aware Wearable Computing System for Human Activity Recognition Proceedings Article

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Borelli, Antonio; Spagnolo, Fanny; Gravina, Raffaele; Frustaci, Fabio

An FPGA-Based Hardware Accelerator for the k-Nearest Neighbor Algorithm Implementation in Wearable Embedded Systems Proceedings Article

In: Mahmud, Mufti; Ieracitano, Cosimo; Kaiser, M. Shamim; Mammone, Nadia; Morabito, Francesco Carlo (Ed.): Applied Intelligence and Informatics, pp. 44–56, Springer Nature Switzerland, Cham, 2022, ISBN: 978-3-031-24801-6.

BibTeX

Aloi, G; Fortino, G; Gravina, R; Pace, P; Savaglio, C

Simulation-driven platform for Edge-based AAL systems Journal Article

In: IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 446-462, 2021.

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Wang, Jiaxin; Wang, Zhelong; Qiu, Sen; Xu, Jian; Zhao, Hongyu; Fortino, Giancarlo; Habib, Masood

A selection framework of sensor combination feature subset for human motion phase segmentation Journal Article

In: Information Fusion, vol. 70, pp. 1 - 11, 2021, ISSN: 1566-2535.

Abstract | Links | BibTeX

@article{WANG20211,

title = {A selection framework of sensor combination feature subset for human motion phase segmentation},

author = {Jiaxin Wang and Zhelong Wang and Sen Qiu and Jian Xu and Hongyu Zhao and Giancarlo Fortino and Masood Habib},

url = {http://www.sciencedirect.com/science/article/pii/S1566253520304346},

doi = {https://doi.org/10.1016/j.inffus.2020.12.009},

issn = {1566-2535},

year  = {2021},

date = {2021-01-01},

journal = {Information Fusion},

volume = {70},

pages = {1 - 11},

abstract = {Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors, it is difficult to artificially select the number and position of the sensors, especially when human motion phases are diverse. This paper proposes a selection framework for the sensor combination feature subset for motion phase segmentation, which combines feature selection algorithms with the subsequent classifiers, and determine the optimum combination of the sensor and the feature subset according to the performance of the trained model. Through the constraint and the sensor combination feature subset (SCFS), the filter method can select any number of sensors and control the size of the feature subset; the embedded method can select any number of sensors, but the size of the feature subset is determined by the classifier model. Experimental results show that the proposed framework can effectively select a specified number of sensors without human intervention, and the number of sensors has an impact on the recognition rate of the classifier within 1.5%. In addition, the filter method has good adaptability to a variety of classifiers, and the classifier prediction time can be controlled by setting the subset size of the feature; the embedded method can achieve a better phase segmentation effect than the filter method. For the application of motion phase segmentation, the proposed framework can reliably and quickly identify redundant sensors that provide effective support for reducing the complexity of the wearable sensor system and improving user comfort.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}