908 research outputs found
Event-based tracking of human hands
This paper proposes a novel method for human hands tracking using data from
an event camera. The event camera detects changes in brightness, measuring
motion, with low latency, no motion blur, low power consumption and high
dynamic range. Captured frames are analysed using lightweight algorithms
reporting 3D hand position data. The chosen pick-and-place scenario serves as
an example input for collaborative human-robot interactions and in obstacle
avoidance for human-robot safety applications. Events data are pre-processed
into intensity frames. The regions of interest (ROI) are defined through object
edge event activity, reducing noise. ROI features are extracted for use
in-depth perception. Event-based tracking of human hand demonstrated feasible,
in real time and at a low computational cost. The proposed ROI-finding method
reduces noise from intensity images, achieving up to 89% of data reduction in
relation to the original, while preserving the features. The depth estimation
error in relation to ground truth (measured with wearables), measured using
dynamic time warping and using a single event camera, is from 15 to 30
millimetres, depending on the plane it is measured. Tracking of human hands in
3D space using a single event camera data and lightweight algorithms to define
ROI features (hands tracking in space)
Addendum to Informatics for Health 2017: Advancing both science and practice
This article presents presentation and poster abstracts that were mistakenly omitted from the original publication
IoT Enabled Smart Activity Recognition using Machine Learning Methods
Internet of Things (IoT) enabled architecture-based devices are becoming accessible worldwide irrespective of the area. But functional settings depend on Internet facilities. In this context, the Healthcare industry took a step forward to automate Human Activity Recognition related concepts using IoT and Machine learning methods. This research used a Nodemcu ESP8266 device to track and communicate human activities acquired using ADXL345 accelerometer sensors. Three volunteers participated in this research, and data were acquired using two accelerometer sensors placed on the hand, wrist, and ankle. Data shared to the cloud- thingspeak.com. Acquired data were analyzed and trained with the Random Forest algorithm and tested with the data, achieving 100% accuracy. This model can be helpful in various applications like elderly patient monitoring, I.C.U., dementia, Alzheimer's, etc
Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review
Internet of Things (IoT) is an evolution of the Internet and has been gaining increased
attention from researchers in both academic and industrial environments. Successive technological
enhancements make the development of intelligent systems with a high capacity for communication
and data collection possible, providing several opportunities for numerous IoT applications,
particularly healthcare systems. Despite all the advantages, there are still several open issues
that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information
security, and privacy. IoT provides important characteristics to healthcare systems, such as availability,
mobility, and scalability, that o er an architectural basis for numerous high technological healthcare
applications, such as real-time patient monitoring, environmental and indoor quality monitoring,
and ubiquitous and pervasive information access that benefits health professionals and patients.
The constant scientific innovations make it possible to develop IoT devices through countless services
for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced
living environments (ELEs). This paper reviews the current state of the art on IoT architectures for
ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities,
open-source platforms, and operating systems. Furthermore, this document synthesizes the existing
body of knowledge and identifies common threads and gaps that open up new significant and
challenging future research directions.info:eu-repo/semantics/publishedVersio
Contextual awareness, messaging and communication in nomadic audio environments
Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1998.Includes bibliographical references (p. 119-122).Nitin Sawhney.M.S
Privacy-Protecting Techniques for Behavioral Data: A Survey
Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved
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