93,964 research outputs found
Olympic Games Event Recognition via Adaptive Convolutional Neural Networks
Automatic event recognition based on human action is both interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data which is being captured momentarily, the need of fast and precise access to the right information has become challenging task with considerable importance for multiple practical applications, e.g., image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. This research, part of my master’s thesis, develops an adaptive content-aware convolution neural network with the capability of analyzing, recognizing and interpreting the sport event in the Olympic games based on human action. 20 of the 33 sports scheduled for inclusion in the Olympic Games Tokyo 2020 will be included in the collected data set to evaluate the proposed method. This method combines convolutional neural network (CNN) and transfer learning (fine-tuning method) to potentially achieve best performance with high accuracy and precision of the event recognition.https://ecommons.udayton.edu/stander_posters/2929/thumbnail.jp
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Healthcare Event and Activity Logging.
The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean
Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features
The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available
CaloriNet: From silhouettes to calorie estimation in private environments
We propose a novel deep fusion architecture, CaloriNet, for the online
estimation of energy expenditure for free living monitoring in private
environments, where RGB data is discarded and replaced by silhouettes. Our
fused convolutional neural network architecture is trainable end-to-end, to
estimate calorie expenditure, using temporal foreground silhouettes alongside
accelerometer data. The network is trained and cross-validated on a publicly
available dataset, SPHERE_RGBD + Inertial_calorie. Results show
state-of-the-art minimum error on the estimation of energy expenditure
(calories per minute), outperforming alternative, standard and single-modal
techniques.Comment: 11 pages, 7 figure
Detecting Irregular Patterns in IoT Streaming Data for Fall Detection
Detecting patterns in real time streaming data has been an interesting and
challenging data analytics problem. With the proliferation of a variety of
sensor devices, real-time analytics of data from the Internet of Things (IoT)
to learn regular and irregular patterns has become an important machine
learning problem to enable predictive analytics for automated notification and
decision support. In this work, we address the problem of learning an irregular
human activity pattern, fall, from streaming IoT data from wearable sensors. We
present a deep neural network model for detecting fall based on accelerometer
data giving 98.75 percent accuracy using an online physical activity monitoring
dataset called "MobiAct", which was published by Vavoulas et al. The initial
model was developed using IBM Watson studio and then later transferred and
deployed on IBM Cloud with the streaming analytics service supported by IBM
Streams for monitoring real-time IoT data. We also present the systems
architecture of the real-time fall detection framework that we intend to use
with mbientlabs wearable health monitoring sensors for real time patient
monitoring at retirement homes or rehabilitation clinics.Comment: 7 page
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