Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device

Amineh R. Elbow Gesture Recognition with an Array of Inductive Sensors and Machine Learning

This work presents a novel approach for elbow gesture recognition using an array of inductive sensors and a machine learning algorithm (MLA). This paper describes the design of the inductive sensor array integrated into a flexible and wearable sleeve. The sensor array consists of coils sewn onto the sleeve, which form an LC tank circuit along with the externally connected inductors and capacitors. Changes in the elbow position modulate the inductance of these coils, allowing the sensor array to capture a range of elbow movements. The signal processing and random forest MLA to recognize 10 different elbow gestures are described. Rigorous evaluation on 8 subjects and data augmentation, which leveraged the dataset to 1270 trials per gesture, enabled the system to achieve remarkable accuracy of 98.3% and 98.5% using 5-fold cross-validation and leave-one-subject-out cross-validation, respectively. The test performance was then assessed using data collected from five new subjects. The high classification accuracy of 94% demonstrates the generalizability of the designed system. The proposed solution addresses the limitations of existing elbow gesture recognition designs and offers a practical and effective approach for intuitive human-machine interaction.

Highly Inductive Coil Spring Strain/Compress Sensors Integrated with Shape Memory Alloy and Shape Memory Polymers-CNTs

Future wearable electronic gadgets offer great potential for using stretchable, strain-sensitive materials to instantly detect human motion and record physiological information. This paper presens a strain/compress sensor made from a Shape memory alloy (SMA) coil spring covered with silver pastes and the composite of carbon nanotubes and Shape memory polymer (SMP). The combination of the shape memory materials that expand or contract automatically by temperature improved the mechanics of the sensor. First, the proposed sensors showed an excellent ability to broad-range strain of 250% and compress of 50% with a relative inductance (∆L/L0 ) range from -35% to 50%, respectively. Durability during 1000 loading and unloading cycles at 200% strain is included. Secondly, by monitoring changes in resistance, inductance, and time, it is determined how many silver layers appropriate for transformation should be in order to improve the recovery time of the SMA coil spring. Moreover, the presence of CNTs in the composite-covered outer of sensors helps to reduce the influence of the relation between resistance and temperature in the range from 30 °C to 110 °C. Finally, a device is suggested for monitoring arm and triceps brachii muscle movements based on the stretchable area as a key parameter.

Low Back Pain-Behavior Correction by Providing Haptic Feedbacks: A Preliminary Investigation

The activities performed by nurses in their daily activities involve frequent forward bending and awkward back postures. These movements contribute to the prevalence and development of low back pain (LBP). In previous studies, it has been shown that modifying their posture by education and training in proper lifting techniques decreases the prevalence of LBP. However, this education and training needs to be implemented daily. Hence, implementing the use of a wearable device to monitor the back posture with haptic feedback would be of importance to prevent LBP. This paper proposes a wearable device to monitor the back posture of the user and provide feedback when the participant is performing a possible hurtful movement. In this study, a group of participants was asked to wear the device while performing three of the most common activities performed by nurses. The study was divided into three sessions: In the first session, the participants performed the activities without feedback (baseline). During the second session, the participants received feedback from the wearable device (training) while performing the three tasks. Finally, for the third session, the participants performed the three tasks again, but the haptic feedback was turned off (validation). We found an improvement in the posture of more than 40% for the pitch (lateral bending) and roll (forward/backward bending) axes and 7% for the yaw (twisting) axis when comparing to the results from session 1 and session 2. The comparison between session 1 and session 3 showed an overall improvement of more than 50% for the pitch (lateral bending) and roll (forward/backward bending) axes and more than 20% for the yaw axis. These results hinted at the impact of the haptic feedback on the participants to correct their posture.

Textile-Based Mechanical Sensors: A Review

Innovations related to textiles-based sensors have drawn great interest due to their outstanding merits of flexibility, comfort, low cost, and wearability. Textile-based sensors are often tied to certain parts of the human body to collect mechanical, physical, and chemical stimuli to identify and record human health and exercise. Until now, much research and review work has been carried out to summarize and promote the development of textile-based sensors. As a feature, we focus on textile-based mechanical sensors (TMSs), especially on their advantages and the way they achieve performance optimizations in this review. We first adopt a novel approach to introduce different kinds of TMSs by combining sensing mechanisms, textile structure, and novel fabricating strategies for implementing TMSs and focusing on critical performance criteria such as sensitivity, response range, response time, and stability. Next, we summarize their great advantages over other flexible sensors, and their potential applications in health monitoring, motion recognition, and human-machine interaction. Finally, we present the challenges and prospects to provide meaningful guidelines and directions for future research. The TMSs play an important role in promoting the development of the emerging Internet of Things, which can make health monitoring and everyday objects connect more smartly, conveniently, and comfortably efficiently in a wearable way in the coming years.