Paper Info
Title
Speech-Based Activity Recognition for Trauma Resuscitation
Abstract
We present a speech-based approach to recognize team activities in the context of trauma resuscitation. We first analyzed the audio recordings of trauma resuscitations in terms of activity frequency, noise-level, and activity-related keyword frequency to determine the dataset characteristics. We next evaluated different audio-preprocessing parameters (spectral feature types and audio channels) to find the optimal configuration. We then introduced a novel neural network to recognize the trauma activities using a modified VGG network that extracts features from the audio input. The output of the modified VGG network is combined with the output of a network that takes keyword text as input, and the combination is used to generate activity labels. We compared our system with several baselines and performed a detailed analysis of the performance results for specific activities. Our results show that our proposed architecture that uses Mel-spectrum spectral coefficients features with a stereo channel and activity-specific frequent keywords achieve the highest accuracy and average F1-score.
Year
DOI
Venue
2020
10.1109/ICHI48887.2020.9374372
2020 IEEE International Conference on Healthcare Informatics (ICHI)
Keywords
DocType
Volume
activity recognition,keyword,audio classification,speech processing,trauma resuscitation
Conference
2020
ISSN
ISBN
Citations 
2575-2626
978-1-7281-5383-4
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jalal Abdulbaqi101.35
Yue Gu200.34
Zhichao Xu300.34
Chenyang Gao400.34
Ivan Marsic571691.96
Randall S. Burd612221.53