Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects
Abstract Human Activity Recognition (HAR) plays a significant role in the everyday life of
people because of its ability to learn extensive high-level information about human activity …
people because of its ability to learn extensive high-level information about human activity …
Emerging wearable interfaces and algorithms for hand gesture recognition: A survey
Hands are vital in a wide range of fundamental daily activities, and neurological diseases
that impede hand function can significantly affect quality of life. Wearable hand gesture …
that impede hand function can significantly affect quality of life. Wearable hand gesture …
[HTML][HTML] Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
A cerebrovascular accident or stroke is the second commonest cause of death in the world. If
it is not fatal, it can result in paralysis, sensory impairment and significant disability …
it is not fatal, it can result in paralysis, sensory impairment and significant disability …
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 …
A human-ai collaborative approach for clinical decision making on rehabilitation assessment
Advances in artificial intelligence (AI) have made it increasingly applicable to supplement
expert's decision-making in the form of a decision support system on various tasks. For …
expert's decision-making in the form of a decision support system on various tasks. For …
A novel deep learning Bi-GRU-I model for real-time human activity recognition using inertial sensors
L Tong, H Ma, Q Lin, J He, L Peng - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Wearable sensor based Human Activity Recognition (HAR) has been widely used these
years. This paper proposed a novel deep learning model for HAR using inertial sensors …
years. This paper proposed a novel deep learning model for HAR using inertial sensors …
Contrastive self-supervised learning for sensor-based human activity recognition
B Khaertdinov, E Ghaleb… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Deep Learning models, applied to a sensor-based Human Activity Recognition task, usually
require vast amounts of annotated time-series data to extract robust features. However …
require vast amounts of annotated time-series data to extract robust features. However …
Human activity recognition from wearable sensor data using self-attention
S Mahmud, M Tanjid Hasan Tonmoy… - ECAI 2020, 2020 - ebooks.iospress.nl
Abstract Human Activity Recognition from body-worn sensor data poses an inherent
challenge in capturing spatial and temporal dependencies of time-series signals. In this …
challenge in capturing spatial and temporal dependencies of time-series signals. In this …
MyoNet: A transfer-learning-based LRCN for lower limb movement recognition and knee joint angle prediction for remote monitoring of rehabilitation progress from …
The clinical assessment technology such as remote monitoring of rehabilitation progress for
lower limb related ailments rely on the automatic evaluation of movement performed along …
lower limb related ailments rely on the automatic evaluation of movement performed along …
A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation
Y Chen, D Zhang, HR Karimi, C Deng, W Yin - Neural Networks, 2022 - Elsevier
Blood pressure (BP) is known as an indicator of human health status, and regular
measurement is helpful for early detection of cardiovascular diseases. Traditional …
measurement is helpful for early detection of cardiovascular diseases. Traditional …