We used Fisher linear discriminant analysis (LDA), static neural networks (NN), and focused time delay neural networks (TDNN) for gesture recognition. Gestures were collected in form of acceleration signals along three axes from six participants. A sports watch containing a 3-axis accelerometer, was worn by the users, who performed four gestures. Each gesture was performed for ten seconds, at the speed of one gesture per second. User-dependent and userindependent k-fold cross validations were carried out to measure the classifier performance. Using first and second order statistical descriptors of acceleration signals from validation datasets, LDA and NN classifiers were able to recognize the gestures at an average rate of 86% and 97% (user-dependent) and 89% and 85% (user-independent), respectively. TDNNs proved to be the best, achieving near perfect classification rates both for user-dependent and userindependent scenarios, while operating directly on the acceleration signals alleviating the need for explicit feature extraction.
We present a novel software tool called CDN (Collaborative Data Network) for large-scale sharing and querying of clinical documents modeled using HL7 v3 standard (e.g., Clinical Document Architecture (CDA), Continuity of Care Document (CCD)). Similar to the caBIG initiative, CDN aims to foster innovations in cancer treatment and diagnosis through large-scale, sharing of clinical data. We focus on cancer because it is the second leading cause of deaths in the US. CDN is based on the synergistic combination of peer-to-peer technology and the extensible markup language XML and XQuery. Using CDN, a user can pose both structured queries and keyword queries on the HL7 v3 documents hosted by data providers. CDN is unique in its design -it supports location oblivious queries in a large-scale, network wherein a user does not explicitly provide the location of the data for a query. A location service in CDN discovers data of interest in the network at query time. CDN uses standard cryptographic techniques to provide security to data providers and protect the privacy of patients. Using CDN, a user can pose clinical queries pertaining to cancer containing aggregations and joins across data hosted by multiple data providers. CDN is implemented with open-source software for web application development and XML query processing. We report the evaluation of CDN in a distributed environment (LAN) using a real dataset of discharge summaries available from the i2b2 project.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.