Results 21 to 30 of about 28,372 (157)
Out-of-distribution in Human Activity Recognition
2022 Swedish Artificial Intelligence Society Workshop (SAIS), 2022 With the growing interest of the research community in making deep learning (DL) robust and reliable, detecting out-of-distribution (OOD) data has become critical. Detecting OOD inputs during test/prediction allows the model to account for discriminative features unknown to the model.
Roy,Debaditya +2 more
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On attention models for human activity recognition [PDF]
Proceedings of the 2018 ACM International Symposium on Wearable Computers, 2018 Most approaches that model time-series data in human activity recognition based on body-worn sensing (HAR) use a fixed size temporal context to represent different activities. This might, however, not be apt for sets of activities with individ- ually varying durations.
Vishvak S. Murahari, Thomas Plötz
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Time Analysis in Human Activity Recognition
Neural Processing Letters, 2021 Continuous human activity recognition from inertial signals is performed by splitting these temporal signals into time windows and identifying the activity in each window. Defining the appropriate window duration has been the target of several previous works.
Gil Martín, Manuel +3 more
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Daily Human Activity Recognition Using Non-Intrusive Sensors
Sensors, 2021 In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home.
Raúl Gómez Ramos +3 more
doaj +1 more source
CBARS: cluster based classification for activity recognition systems [PDF]
, 2012 Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition.
Gaber, Mohamed Medhat +11 more
core +1 more source
Mars: a personalised mobile activity recognition system [PDF]
, 2012 Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones.
Krishnaswamy, S. +14 more
core +1 more source
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTThis project presents an intelligent and automated safety monitoring system that integrates Human Action Recognition and Facial Expression Recognition to detect critical emergency situations such as falls and drowsiness in real time. The system continuously analyzes video or motion-based input data to understand both the physical activities and ...
Gabriele Schabacher, Sophie Spallinger
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Segmentation and recognition of continuous human activity [PDF]
Proceedings IEEE Workshop on Detection and Recognition of Events in Video, 2001 This paper presents a methodology for automatic segmentation and recognition of continuous human activity. We segment a continuous human activity into separate actions and correctly identify each action. The camera views the subject from the lateral view: there are no distinct breaks or pauses between the execution of different actions.
Anjum Ali, J. K. Aggarwal
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StreamAR: incremental and active learning with evolving sensory data for activity recognition [PDF]
, 2012 Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition.
Gaber, Mohamed Medhat +11 more
core +1 more source
Brain-inspired spiking neural networks for Wi-Fi based human activity recognition [PDF]
, 2021 Human activities can be recognized through reflections of wireless signals which solve the problem of privacy concerns and restriction of the application environment in vision-based recognition.
Wong, Yan Chiew +2 more
core +1 more source