Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
Human activity recognition systems are developed as part of a framework to enable
continuous monitoring of human behaviours in the area of ambient assisted living, sports …
continuous monitoring of human behaviours in the area of ambient assisted living, sports …
Deep, convolutional, and recurrent models for human activity recognition using wearables
Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep
learning to substitute for well-established analysis techniques that rely on hand-crafted …
learning to substitute for well-established analysis techniques that rely on hand-crafted …
InnoHAR: A deep neural network for complex human activity recognition
Human activity recognition (HAR) based on sensor networks is an important research
direction in the fields of pervasive computing and body area network. Existing researches …
direction in the fields of pervasive computing and body area network. Existing researches …
Ensembles of deep lstm learners for activity recognition using wearables
Recently, deep learning (DL) methods have been introduced very successfully into human
activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the …
activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the …
[HTML][HTML] Automated detection of autism spectrum disorder using a convolutional neural network
Z Sherkatghanad, M Akhondzadeh, S Salari… - Frontiers in …, 2020 - frontiersin.org
Background: Convolutional neural networks (CNN) have enabled significant progress in
speech recognition, image classification, automotive software engineering, and …
speech recognition, image classification, automotive software engineering, and …
[HTML][HTML] Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection
The detection and monitoring of emotions are important in various applications, eg, to
enable naturalistic and personalised human-robot interaction. Emotion detection often …
enable naturalistic and personalised human-robot interaction. Emotion detection often …
[HTML][HTML] Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review
R Francese, X Yang - Complex & Intelligent Systems, 2022 - Springer
The number of autism spectrum disorder individuals is dramatically increasing. For them, it is
difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which …
difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which …
Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks
D Arifoglu, A Bouchachia - Artificial intelligence in medicine, 2019 - Elsevier
In recent years, there is a rapid increase in the population of elderly people. However,
elderly people may suffer from the consequences of cognitive decline, which is a mental …
elderly people may suffer from the consequences of cognitive decline, which is a mental …
Sequential human activity recognition based on deep convolutional network and extreme learning machine using wearable sensors
Human activity recognition (HAR) problems have traditionally been solved by using
engineered features obtained by heuristic methods. These methods ignore the time …
engineered features obtained by heuristic methods. These methods ignore the time …
[HTML][HTML] pyphysio: A physiological signal processing library for data science approaches in physiology
The lack of open-source tools for physiological signal processing hinders the development
of standardized pipelines in physiology. Researchers usually must rely on commercial …
of standardized pipelines in physiology. Researchers usually must rely on commercial …