Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance. Although Feature Selection with Consensus (FeSC) implements a very robust architecture for feature selection and classification, results are still negatively affected by the limited size of the dataset. In the future, further investigations could determine to what extent size could cause a drop in the performance of FeSC in this and other gesture recognition applications.

Cisotto, G., Capuzzo, M., Guglielmi Anna, V., Zanella, A. (2020). Feature selection for gesture recognition in Internet-of-Things for healthcare. In Proceedings of the 2020 IEEE International Conference on Communications (ICC). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/ICC40277.2020.9149381].

Feature selection for gesture recognition in Internet-of-Things for healthcare

Cisotto Giulia
Primo
;
2020

Abstract

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance. Although Feature Selection with Consensus (FeSC) implements a very robust architecture for feature selection and classification, results are still negatively affected by the limited size of the dataset. In the future, further investigations could determine to what extent size could cause a drop in the performance of FeSC in this and other gesture recognition applications.
No
paper
consensus clustering; EEG; EMG; Feature selection; Internet-of-Things for Healthcare; nested cross-validation;
English
2020 IEEE International Conference on Communications, ICC 2020 - 7 June 2020 through 11 June 2020
978-172815089-5
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9149381
Cisotto, G., Capuzzo, M., Guglielmi Anna, V., Zanella, A. (2020). Feature selection for gesture recognition in Internet-of-Things for healthcare. In Proceedings of the 2020 IEEE International Conference on Communications (ICC). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/ICC40277.2020.9149381].
Cisotto, G; Capuzzo, M; Guglielmi Anna, V; Zanella, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/367502
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