Rehabilitation of Traumatic Brain Injured Patients: Patient Mood Analysis from Multimodal Video

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Abstract

Rehabilitation after traumatic brain injury (TBI) is very critical as it is largely unpredictable depending upon the nature of the injury. Rehabilitation process and recovery time also varies, as it takes months and years, depending upon the assessment of treatment, mental and physical conditions and strategies. Due to non-cooperative behaviour of patients, and increase in negative emotional expressions it is very beneficial to evaluate these expressions in a contactless way, and perform a rehabilitation physiotherapy, cognitive or other behavioral activities when the patient is in a positive mood. In this paper we have analyzed the methods for facial features extraction for TBI patients to determine optimal time to have aforementioned rehabilitation process on the basis of positive and negative facial expressions. We have employed a deep learning architecture based on convolutional neural network and long short term memory on RGB and thermal data that were collected in challenging scenarios from real patients. It automatically identifies the patient's facial expressions, and inform experts or trainers that "it is the time" to start rehabilitation session.
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Detaljer

Rehabilitation after traumatic brain injury (TBI) is very critical as it is largely unpredictable depending upon the nature of the injury. Rehabilitation process and recovery time also varies, as it takes months and years, depending upon the assessment of treatment, mental and physical conditions and strategies. Due to non-cooperative behaviour of patients, and increase in negative emotional expressions it is very beneficial to evaluate these expressions in a contactless way, and perform a rehabilitation physiotherapy, cognitive or other behavioral activities when the patient is in a positive mood. In this paper we have analyzed the methods for facial features extraction for TBI patients to determine optimal time to have aforementioned rehabilitation process on the basis of positive and negative facial expressions. We have employed a deep learning architecture based on convolutional neural network and long short term memory on RGB and thermal data that were collected in challenging scenarios from real patients. It automatically identifies the patient's facial expressions, and inform experts or trainers that "it is the time" to start rehabilitation session.
OriginalsprogEngelsk
Titel2018 IEEE International Conference on Image Processing : ICIP 2018
ForlagIEEE
Publikationsdato7 okt. 2018
StatusUdgivet - 7 okt. 2018
PublikationsartForskning
Peer reviewJa
Begivenhed2018 IEEE International Conference on Image Processing - Megaron Athens International Conference Centre, Athens, Greece, Athens, Grækenland
Varighed: 7 okt. 201810 okt. 2018
https://2018.ieeeicip.org/default.asp

Konference

Konference2018 IEEE International Conference on Image Processing
LokationMegaron Athens International Conference Centre, Athens, Greece
LandGrækenland
ByAthens
Periode07/10/201810/10/2018
Internetadresse

Kort

ID: 275292379