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 …
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …
Automated ergonomic risk monitoring using body-mounted sensors and machine learning
Workers in various industries are often subject to challenging physical motions that may lead
to work-related musculoskeletal disorders (WMSDs). To prevent WMSDs, health and safety …
to work-related musculoskeletal disorders (WMSDs). To prevent WMSDs, health and safety …
Human activity recognition based on wearable sensor using hierarchical deep LSTM networks
LK Wang, RY Liu - Circuits, Systems, and Signal Processing, 2020 - Springer
In recent years, with the rapid development of artificial intelligence, human activity
recognition has become a research focus. The complex, dynamic and variable features of …
recognition has become a research focus. The complex, dynamic and variable features of …
Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models
Deep learning (DL) models are very useful for human activity recognition (HAR); these
methods present better accuracy for HAR when compared to traditional, among other …
methods present better accuracy for HAR when compared to traditional, among other …
[HTML][HTML] Activity recognition for iot devices using fuzzy spatio-temporal features as environmental sensor fusion
MÁL Medina, M Espinilla, C Paggeti… - Sensors (Basel …, 2019 - ncbi.nlm.nih.gov
The IoT describes a development field where new approaches and trends are in constant
change. In this scenario, new devices and sensors are offering higher precision in everyday …
change. In this scenario, new devices and sensors are offering higher precision in everyday …
A boundary consistency-aware multitask learning framework for joint activity segmentation and recognition with wearable sensors
With the development of industrial and sensing technology, sensor-based activity
recognition has become a promising technology for informatics applications. However, in a …
recognition has become a promising technology for informatics applications. However, in a …
Harmamba: Efficient wearable sensor human activity recognition based on bidirectional selective ssm
Wearable sensor human activity recognition (HAR) is a crucial area of research in activity
sensing. While transformer-based temporal deep learning models have been extensively …
sensing. While transformer-based temporal deep learning models have been extensively …
Dolars, a distributed on-line activity recognition system by means of heterogeneous sensors in real-life deployments—a case study in the smart lab of the university of …
Activity Recognition (AR) is an active research topic focused on detecting human actions
and behaviours in smart environments. In this work, we present the on-line activity …
and behaviours in smart environments. In this work, we present the on-line activity …
Activity recognition for iot devices using fuzzy spatio-temporal features as environmental sensor fusion
MA Lopez Medina, M Espinilla, C Paggeti… - Sensors, 2019 - mdpi.com
The IoT describes a development field where new approaches and trends are in constant
change. In this scenario, new devices and sensors are offering higher precision in everyday …
change. In this scenario, new devices and sensors are offering higher precision in everyday …