Next Article in Journal
Testing a Model of Human Spatial Navigation Attitudes towards Global Navigation Satellite Systems
Previous Article in Journal
Screen-Printed Carbon Electrodes with Macroporous Copper Film for Enhanced Amperometric Sensing of Saccharides
Article

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

National Research Council of Italy, IMM—Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Christophoros Nikou
Sensors 2022, 22(9), 3468; https://doi.org/10.3390/s22093468
Received: 30 March 2022 / Revised: 27 April 2022 / Accepted: 28 April 2022 / Published: 2 May 2022
(This article belongs to the Section Intelligent Sensors)
Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aimed to predict changes in one’s behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a temporal convolutional network, and the behavioral state was predicted through bidirectional long short-term memory auto-encoder, operating jointly. From the comparison with the state of the art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds. View Full-Text
Keywords: behavioral change prediction; learned features; deep feature learning; handcrafted features; bidirectional long-short term memory; autoencoders; temporal convolutional neural network; clinical decision support system; multisensory stimulation therapy; physiological signals behavioral change prediction; learned features; deep feature learning; handcrafted features; bidirectional long-short term memory; autoencoders; temporal convolutional neural network; clinical decision support system; multisensory stimulation therapy; physiological signals
Show Figures

Figure 1

MDPI and ACS Style

Diraco, G.; Siciliano, P.; Leone, A. Behavioral Change Prediction from Physiological Signals Using Deep Learned Features. Sensors 2022, 22, 3468. https://doi.org/10.3390/s22093468

AMA Style

Diraco G, Siciliano P, Leone A. Behavioral Change Prediction from Physiological Signals Using Deep Learned Features. Sensors. 2022; 22(9):3468. https://doi.org/10.3390/s22093468

Chicago/Turabian Style

Diraco, Giovanni, Pietro Siciliano, and Alessandro Leone. 2022. "Behavioral Change Prediction from Physiological Signals Using Deep Learned Features" Sensors 22, no. 9: 3468. https://doi.org/10.3390/s22093468

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop