2,998 research outputs found
Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients
This is the Accepted Manuscript version of the following article: I. Mporas, D. Triantafyllopoulos, V. Megalooikonomou, “Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients”, Journal of Medical Systems, Vol. 40(45), December 2015. The final published versions is available at: https://link.springer.com/article/10.1007%2Fs10916-015-0403-3 © Springer Science+Business Media New York 2015.New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient’s physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.Peer reviewedFinal Accepted Versio
A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System
This paper presents a novel multimodal human activity recognition system. It
uses a two-stream decision level fusion of vision and inertial sensors. In the
first stream, raw RGB frames are passed to a part affinity field-based pose
estimation network to detect the keypoints of the user. These keypoints are
then pre-processed and inputted in a sliding window fashion to a specially
designed convolutional neural network for the spatial feature extraction
followed by regularized LSTMs to calculate the temporal features. The outputs
of LSTM networks are then inputted to fully connected layers for
classification. In the second stream, data obtained from inertial sensors are
pre-processed and inputted to regularized LSTMs for the feature extraction
followed by fully connected layers for the classification. At this stage, the
SoftMax scores of two streams are then fused using the decision level fusion
which gives the final prediction. Extensive experiments are conducted to
evaluate the performance. Four multimodal standard benchmark datasets (UP-Fall
detection, UTD-MHAD, Berkeley-MHAD, and C-MHAD) are used for experimentations.
The accuracies obtained by the proposed system are 96.9 %, 97.6 %, 98.7 %, and
95.9 % respectively on the UP-Fall Detection, UTDMHAD, Berkeley-MHAD, and
C-MHAD datasets. These results are far superior than the current
state-of-the-art methods
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
For people with chronic pain, the assessment of protective behavior during
physical functioning is essential to understand their subjective pain-related
experiences (e.g., fear and anxiety toward pain and injury) and how they deal
with such experiences (avoidance or reliance on specific body joints), with the
ultimate goal of guiding intervention. Advances in deep learning (DL) can
enable the development of such intervention. Using the EmoPain MoCap dataset,
we investigate how attention-based DL architectures can be used to improve the
detection of protective behavior by capturing the most informative temporal and
body configurational cues characterizing specific movements and the strategies
used to perform them. We propose an end-to-end deep learning architecture named
BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts
that are more informative to the detection of protective behavior. The approach
addresses the variety of ways people execute a movement (including healthy
people) independently of the type of movement analyzed. Through extensive
comparison experiments with other state-of-the-art machine learning techniques
used with motion capture data, we show statistically significant improvements
achieved by using these attention mechanisms. In addition, the BANet
architecture requires a much lower number of parameters than the state of the
art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201
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