Human action recognition from various data modalities: A review
Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …
each action. It has a wide range of applications, and therefore has been attracting increasing …
Human activity recognition using inertial, physiological and environmental sensors: A comprehensive survey
In the last decade, Human Activity Recognition (HAR) has become a vibrant research area,
especially due to the spread of electronic devices such as smartphones, smartwatches and …
especially due to the spread of electronic devices such as smartphones, smartwatches and …
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
Human activity recognition (HAR) has become a significant area of research in human
behavior analysis, human–computer interaction, and pervasive computing. Recently, deep …
behavior analysis, human–computer interaction, and pervasive computing. Recently, deep …
Deep learning for sensor-based activity recognition: A survey
Sensor-based activity recognition seeks the profound high-level knowledge about human
activities from multitudes of low-level sensor readings. Conventional pattern recognition …
activities from multitudes of low-level sensor readings. Conventional pattern recognition …
A survey on wearable sensor modality centred human activity recognition in health care
Increased life expectancy coupled with declining birth rates is leading to an aging
population structure. Aging-caused changes, such as physical or cognitive decline, could …
population structure. Aging-caused changes, such as physical or cognitive decline, could …
[HTML][HTML] Human activity recognition based on residual network and BiLSTM
Y Li, L Wang - Sensors, 2022 - mdpi.com
Due to the wide application of human activity recognition (HAR) in sports and health, a large
number of HAR models based on deep learning have been proposed. However, many …
number of HAR models based on deep learning have been proposed. However, many …
Recent trends in machine learning for human activity recognition—A survey
S Ramasamy Ramamurthy… - … Reviews: Data Mining and …, 2018 - Wiley Online Library
There has been an upsurge recently in investigating machine learning techniques for activity
recognition (AR) problems as they have been very effective in extracting and learning …
recognition (AR) problems as they have been very effective in extracting and learning …
PP-Net: A deep learning framework for PPG-based blood pressure and heart rate estimation
This paper presents a deep learning model'PP-Net'which is the first of its kind, having the
capability to estimate the physiological parameters: Diastolic blood pressure (DBP), Systolic …
capability to estimate the physiological parameters: Diastolic blood pressure (DBP), Systolic …
Deep ConvLSTM with self-attention for human activity decoding using wearable sensors
Decoding human activity accurately from wearable sensors can aid in applications related to
healthcare and context awareness. The present approaches in this domain use recurrent …
healthcare and context awareness. The present approaches in this domain use recurrent …
Rehab-net: Deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation
In this paper, we present a deep learning framework “Rehab-Net” for effectively classifying
three upper limb movements of the human arm, involving extension, flexion, and rotation of …
three upper limb movements of the human arm, involving extension, flexion, and rotation of …