Comparing clothing-mounted sensors with wearable sensors for movement analysis and activity classification

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing

Comparing loose clothing-mounted sensors with body-mounted sensors in the analysis of walking

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

Long-term activity monitoring using wearable sensors mounted in loose clothing

Using multiple Inertial Measurement Units (IMU) in movement analysis will not only be useful in increasing the classification accuracy of the movement data, but also reduce the computational complexity in classification algorithms as there is no need to process an increased number of features generated from a single sensor. However, wearing sensor devices every day on the same place with the same orientation is a key requirement for the data analysis purpose. To facilitate this, sensor devices can be mounted into clothing, as it is an ideal platform to cater these miniature devices. There are research studies conducted with sensors mounted into clothing such as smart garments, tight-fitting clothing and loose clothing (everyday wear clothing). Data validations are available between tight-fitting clothing-mounted sensor data and body-mounted sensor data, focusing mainly on limited set of activities or sensors. The main focus of this research was to investigate the possibility of using loose clothing-mounted sensors in monitoring human movement patterns in a home based healthcare monitoring system, while validating how the loose clothing-mounted sensor data correlate with body-mounted sensor data with respect to different activities. In order to quantify and understand human movements in this research, time synchronised wearable sensors were mounted into loose clothing. This whole research was based on three datasets and they were used to conduct four sub analyses based on different types of human movement patterns to achieve the main goal. First analysis was based on data collected from Actigraph sensors from both body and clothing and the sensors were near waist, thigh and ankle/ lower-shank. This study validated the data between clothing and body mounted sensor data across various static and dynamic activities with respect to each sensor pairs. These validations were based on correlation coefficient values with respect to the accelerometer data pairs for different activities i.e. ‘standing’, ‘sitting’, ‘sitting on a bus’, ‘walking’ and ‘running’. Promising correlations were observed (especially with static activities) with this dataset and the second dataset was collected from body and clothingmounted lightweight IMU sensors. These data were analysed based on correlation coefficient values with respect to the inclination angle changes over ‘gait’ cycles. In addition to the correlation coefficient values, the data were analysed using different types of plots such as phase portraits and 3D plots. From these plots, it was noted that important features such as Mid-Stance (MS), Initial Contact (IC) and Toe Off (TO) points can be recognised by clothing data and they can be used to analyse ‘walking’ data in detail. Moreover, the third semi-natural dataset was collected from clothing-mounted sensors to check whether they can be used to implement posture and activity classifiers. These classifiers that were based on both Machine Learning (ML) and Deep Learning (DL) approaches with relevant selected features, also showed reasonably high classification accuracies. By taking into account all these promising observations, this thesis can be concluded that loose clothing-mounted sensor data can be used productively in movement analysis

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

Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a ‘sensor string’ and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28 - 48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5-8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features

Jayasinghe_etal_2023

Data and codes for the paper titled Inertial Measurement Data From Loose Clothing Worn on the Lower Body During Everyday Activities with an updated folder name

Classification of static postures with wearable sensors mounted on loose clothing

Inertial Measurement Units (IMUs) are a potential way to monitor the mobility of people outside clinical or laboratory settings at an acceptable cost. To increase accuracy, multiple IMUs can be used. By embedding multiple sensors into everyday clothing, it is possible to simplify having to put on individual sensors, ensuring sensors are correctly located and oriented. This research demonstrates how clothing-mounted IMU readings can be used to identify 4 common postures: standing, sitting, lying down and sitting on the floor. Data were collected from 5 healthy adults, with each providing 1–4 days of data with approximately 5 h each day. Each day, participants performed a fixed set of activities that were video-recorded to provide a ground truth. This is an analysis of accelerometry data from 3 sensors incorporated into right trouser-leg at the waist, thigh and ankle. Data were classified as static/ dynamic activities using a K-nearest neighbour (KNN) algorithm. For static activities, the inclination angles of the three sensors were estimated and used to train a second KNN classifier. For this highly-selected dataset (60000–70000 data points/posture), the static postures were classified with 100% accuracy, illustrating the potential for clothing-mounted sensors to be used in posture classification