Research

18 pages, 7523 KiB

Open AccessArticle

Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation

by Alexander Jamieson, Laura Murray, Vladimir Stankovic, Lina Stankovic and Arjan Buis

Sensors 2023, 23(19), 8164; https://doi.org/10.3390/s23198164 - 29 Sep 2023

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Abstract

This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract ‘unique’ groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods—namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP)—we selected tSNE due to its performance and stable outputs. Cluster formation of activities via DBSCAN only occurred after the data were reduced to two dimensions via tSNE and contained only samples for a single individual. Additionally, through analysis of the t-SNE plots, appreciable clusters in walking-based activities were only apparent with ground walking and stair ambulation. Through a combination of density-based clustering and analysis of cluster distance and density, a novel algorithm inspired by the t-SNE plots, resulting in three proposed and validated hypotheses, was able to identify cluster formations that arose from ground walking and stair ambulation. Low dimensional clustering of activities has thus been found feasible when analyzing individual sets of data and can currently recognize stair and ground walking ambulation. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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11 pages, 17027 KiB

Open AccessCommunication

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques

by Giuseppina Monti and Luciano Tarricone

Sensors 2023, 23(18), 7748; https://doi.org/10.3390/s23187748 - 08 Sep 2023

Viewed by 636

Abstract

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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19 pages, 1671 KiB

Open AccessArticle

Improving Inertial Sensor-Based Activity Recognition in Neurological Populations

by Yunus Celik, M. Fatih Aslan, Kadir Sabanci, Sam Stuart, Wai Lok Woo and Alan Godfrey

Sensors 2022, 22(24), 9891; https://doi.org/10.3390/s22249891 - 15 Dec 2022

Cited by 4 | Viewed by 1860

Abstract

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson’s disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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