In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Gasparini, F., Grossi, A., Giltri, M., Nishinari, K., Bandini, S. (2023). Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages. SENSORS, 23(6), 1-28 [10.3390/s23063225].

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

Gasparini, Francesca
Primo
;
Grossi, Alessandra
Secondo
;
Giltri, Marta;Bandini, Stefania
Ultimo
2023

Abstract

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.
Articolo in rivista - Articolo scientifico
classification; EMG; GSR; physiological signals; PPG; signal processing; wearable sensors;
English
17-mar-2023
2023
23
6
1
28
3225
open
Gasparini, F., Grossi, A., Giltri, M., Nishinari, K., Bandini, S. (2023). Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages. SENSORS, 23(6), 1-28 [10.3390/s23063225].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/406695
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