Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor

02/28/2022
by   Muhammad Zeeshan Arshad, et al.
0

Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to explore a way of improving the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data was gathered from elderly subjects equipped with three IMU sensors while they walked. The input was taken only from the waist sensor was used to train 16 deep-learning models including CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. Fairly high accuracy of 99.73 tolerance window of ±6TS (±6ms) and ±1TS (±1ms) respectively. Advancing from the previous studies exploring gait event detection, the model showed a great improvement in terms of its prediction error having an MAE of 6.239ms and 5.24ms for HS and TO events respectively at the tolerance window of ±1TS. The results showed that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon.

READ FULL TEXT
research
07/09/2019

Deep Learning Techniques for Improving Digital Gait Segmentation

Wearable technology for the automatic detection of gait events has recen...
research
10/15/2021

Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN

Frailty is a common and critical condition in elderly adults, which may ...
research
12/17/2020

Treadmill Assisted Gait Spoofing (TAGS): An Emerging Threat to wearable Sensor-based Gait Authentication

In this work, we examine the impact of Treadmill Assisted Gait Spoofing ...
research
11/11/2022

A Gait Triaging Toolkit for Overlapping Acoustic Events in Indoor Environments

Gait has been used in clinical and healthcare applications to assess the...
research
05/02/2022

Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model

Gait phase detection with convolution neural network provides accurate c...
research
06/04/2019

Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks

The aim of this study is developing an automatic system for detection of...
research
11/16/2021

SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks

We introduce a novel neural network architecture – Spectral ENcoder for ...

Please sign up or login with your details

Forgot password? Click here to reset