Dual Residual Network for Accurate Human Activity Recognition

03/13/2019
by   Jun Long, et al.
0

Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction. Machine can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem, but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of the automatic learning, we propose a novel Dual Residual Network, named DRN. DRN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, yet still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of DRN model, we carried out extensive experiments and compared with conventional recognition methods (HC, CBH, CBS) and learning-based methods (AE, MLP, CNN, LSTM, Hybrid, ResNet). The benchmark datasets (OPPORTUNITY, UniMiB-SHAR) were adopted by our experiments. Results from our experiments show that our model is effective in recognizing human activities via wearable datasets. We discuss the influence of networks parameters on performance to provide insights about its optimization.

READ FULL TEXT
research
03/13/2019

Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Human Activity Recognition (HAR) using deep neural network has become a ...
research
03/31/2023

WSense: A Robust Feature Learning Module for Lightweight Human Activity Recognition

In recent times, various modules such as squeeze-and-excitation, and oth...
research
12/23/2022

Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) is one of the core research areas in mo...
research
11/07/2016

Action2Activity: Recognizing Complex Activities from Sensor Data

As compared to simple actions, activities are much more complex, but sem...
research
09/20/2018

Deep HMResNet Model for Human Activity-Aware Robotic Systems

Endowing the robotic systems with cognitive capabilities for recognizing...
research
09/20/2018

Human activity recognition based on time series analysis using U-Net

Traditional human activity recognition (HAR) based on time series adopts...
research
04/15/2020

Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables

Recognizing human activities from multi-channel time series data collect...

Please sign up or login with your details

Forgot password? Click here to reset