Bi-Modal Deep Neural Network for Gait Emotion Recognition

Bhatia, Yajurv

Bi-Modal Deep Neural Network for Gait Emotion Recognition

dc.contributor.advisor	Gavrilova, Marina
dc.contributor.author	Bhatia, Yajurv
dc.contributor.committeemember	Jacobson, Michael
dc.contributor.committeemember	Runions, Adam
dc.date	2023-02
dc.date.accessioned	2022-11-25T20:31:36Z
dc.date.available	2022-11-25T20:31:36Z
dc.date.issued	2022-11-23
dc.description.abstract	Emotion Recognition systems can be used for autonomous tasks such as video gaming experiences, medical diagnosis, adaptive education, and smart homes. Several biometric modalities, including face, hands, and voice have been successfully used for emotion recognition tasks. Gait Emotion Recognition (GER) is an emerging domain of research that is focused on identifying the emotional state of a person from gait biometric, which represents the person’s manner of walking. In comparison to the other modalities, gait provides a non-intrusive method to collect data remotely without an expert’s supervision. Moreover, unlike facial expression-based emotion recognition, it does not require high-resolution data for inference. Early works in GER produced limited feature sets and used classical machine learning methodologies to infer emotions, but could not achieve high performance. This thesis proposes powerful architectures based on deep-learning to accurately identify emotions from human gaits. The proposed Bi-Modal Deep Neural Network (BMDNN) architecture utilizes robust handcrafted features that are independent of dataset size and data distribution. The network is based on Long Short-Term Memory units and Multi-Layered Perceptrons to sequentially process raw gait sequences and facilitate feature fusion with the handcrafted features. Lastly, the proposed Bi-Modular Sequential Neural Network (BMSNN) has a low number of parameters and a low inference time, hence making it suitable for deployment in real world applications. The proposed methodologies were evaluated on the Edinburgh Locomotive MoCap Dataset and outperformed all recent state-of-the-art methods.	en_US
dc.identifier.citation	Bhatia, Y. (2022). Bi-Modal Deep Neural Network for Gait Emotion Recognition (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.uri	http://hdl.handle.net/1880/115541
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40498
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Deep Learning	en_US
dc.subject	Cognitive Systems	en_US
dc.subject	Long Short-Term Memory (LSTM)	en_US
dc.subject	Affective Computing	en_US
dc.subject	Emotion Recognition	en_US
dc.subject	Situation Awareness	en_US
dc.subject	Gait	en_US
dc.subject	Remote Visual Technology	en_US
dc.subject	Motion Capture Sensor	en_US
dc.subject	Human Motion	en_US
dc.subject	Handcrafted Features	en_US
dc.subject	Feature Fusion	en_US
dc.subject.classification	Education--Sciences	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Computer Science	en_US
dc.subject.classification	Robotics	en_US
dc.subject.classification	Psychology--Behavioral	en_US
dc.subject.classification	Psychology--Physiological	en_US
dc.title	Bi-Modal Deep Neural Network for Gait Emotion Recognition	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Computer Science	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US