Deep learning in human activity recognition with wearable sensors: A review on advances
Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …
tracking, wellness monitoring, and human–computer interaction, that measure and improve …
A survey on deep learning for human activity recognition
Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …
home. In this study, we provide a comprehensive survey on recent advances and challenges …
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
There is a growing interest on using ambient and wearable sensors for human activity
recognition, fostered by several application domains and wider availability of sensing …
recognition, fostered by several application domains and wider availability of sensing …
Human activity recognition using wearable sensors by heterogeneous convolutional neural networks
Recent researches on sensor based human activity recognition (HAR) are mostly devoted to
designing various network architectures to enhance their feature representation capacity for …
designing various network architectures to enhance their feature representation capacity for …
A survey on unsupervised learning for wearable sensor-based activity recognition
Abstract Human Activity Recognition (HAR) is an essential task in various applications such
as pervasive healthcare, smart environment, and security and surveillance. The need to …
as pervasive healthcare, smart environment, and security and surveillance. The need to …
Time series change point detection with self-supervised contrastive predictive coding
Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …
trends and properties of time series data in order to describe the underlying behaviour of the …
Deep recurrent neural network for mobile human activity recognition with high throughput
In this paper, we propose a method of human activity recognition with high throughput from
raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate …
raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate …
Sensor-based datasets for human activity recognition–a systematic review of literature
The research area of ambient assisted living has led to the development of activity
recognition systems (ARS) based on human activity recognition (HAR). These systems …
recognition systems (ARS) based on human activity recognition (HAR). These systems …
KU-HAR: An open dataset for heterogeneous human activity recognition
Abstract In Artificial Intelligence, Human Activity Recognition (HAR) refers to the capability of
machines to identify various activities performed by the users. The knowledge acquired from …
machines to identify various activities performed by the users. The knowledge acquired from …
HARTH: a human activity recognition dataset for machine learning
Existing accelerometer-based human activity recognition (HAR) benchmark datasets that
were recorded during free living suffer from non-fixed sensor placement, the usage of only …
were recorded during free living suffer from non-fixed sensor placement, the usage of only …