Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Abstract

A person’s walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg.

A Probabilistic Model of Human Activity Recognition with Loose Clothing

“…Moreover, the study focused on activities that are relatively easy to distinguish (walking, running and sitting). Recently, Jayasinghe et al [ 11 ] extended this work to focus on the analysis of gait-related movements (e.g., standing, sitting, sit-to-stand, stand-to-sit, leg raises and walking back and forth) using sensorized trousers with similar findings reported. Jayasinghe et al [ 17 ] recently further explored the practicability of clothing-attached sensors to classify static postures using K-nearest neighbors (KNN) and achieved a promising result.…”

“…Some studies [ 13 , 14 , 15 , 16 ] focus on how to embedded the sensor into clothes using different e-textile technologies. Others [ 9 , 10 , 11 , 17 ] apply machine learning algorithms to sensor readings. However, they have not compared the performance of AR of body-attached and clothing-attached sensors with human movement, and they lack probabilistic models to understand the performance of AR of sensors with two different attachments.…”

“…This could be due to some possible factors (e.g., uncertainty in the fabric itself, movement frequency, etc.). (ii) Jayasinghe et al [ 10 ] discovered that the movement of clothing can be useful to describe human daily activities (walking, running, sitting and riding a bus) and gait analysis [ 11 ]. However, this phenomenon so far lacks a satisfactory theoretical explanation or model.…”

Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals

“…Further limitations were that during maximal breathing, it was likely that the shirt moved in relation to the skin surface [ 45 ]. However, this would generally also occur with a smart shirt, and thus, represent a typical systematic error that seems difficult to avoid without irritating the participant.…”

Inertial measurement data from loose clothing worn on the lower body during everyday activities

“…To calculate the sensor-to-vertical angles for dynamic activities, rotation matrices were used. First, the data from each sensor was used to estimate quaternions using Madgwick’s algorithm 53 ( https://github.com/xioTechnologies/NGIMU-Software-Public , accessed on 21 September 2021) and the sensor-to vertical angles were estimated by calculating the angle between the forward pointing vector and the gravity vector (as described in 54 ). The angles based on the lower-body sensors for walking, climbing up stairs and down stairs are shown in Fig.…”