Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

10/31/2021
by   Shibo Zhang, et al.
0

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning–based HAR.

READ FULL TEXT

page 2

page 5

06/05/2020

Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices

Recently, deep learning has represented an important research trend in h...
01/05/2021

Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark

Recognizing human activity plays a significant role in the advancements ...
10/13/2021

Tutorial on Deep Learning for Human Activity Recognition

Activity recognition systems that are capable of estimating human activi...
02/03/2021

AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices

Human Activity Recognition (HAR) is one of the key applications of healt...
10/10/2017

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communi...
02/15/2022

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

In wearable-based human activity recognition (HAR) research, one of the ...