Gesture recognition, in particular, hand gesture recognition, has received great attention in the field of human-computer interaction. Although several hand gesture recognition designs have been proposed in the past, they still suffer many limitations in practice. Hence, we present a wearable device, called GloveSense, which is made on a regular glove using conductive threads. The system consists of ten coils, with each pair of coils sewn on both sides of each finger and connected in series. To achieve high sensitivity, a data acquisition system is employed in which a tank circuit made of each sewn coil is measured. Since the utilized commercial data acquisition board has limited channels, a switching mechanism is employed to measure some of the extra coils. The utilized switching mechanism reduces the cross-talk effect between the readings. The GloveSense system is tested for recognition of numbers 1 to 10 in American Sign Language. A dataset of 1400 responses is employed along with a machine learning algorithm for gesture recognition. The proposed system demonstrates a high accuracy of 99% and a short recognition time of less than 1 second.

Currently, there is a rapidly growing interest and demand for wearable textile sensors that can monitor human motions in a naturalistic environment. Some potential applications for this technology include research on measuring the motor skill performance of patients with motor disabilities such as autism spectrum disorder, Parkinson’s disease, cerebral palsy, and stroke and evaluating the efficacy of applied treatments. Among wearable sensors, inductive sensors that are made from highly conductive threads are attractive due to their easy development process, high reliability, and low cost. In this study, we analyzed and compared the performance of three inductive wearable sensor configurations—(1) single planar rectangular coil, (2) two separated coils connected in series, and (3) two helical coils connected in series—in terms of the change in the resonant frequency of the tank circuit they comprised as a result of the change in elbow joint angle through simulations. Three parameters of length, width, and the number of turns were considered to calculate sensor sensitivity to the joint angle. The coil with the highest sensitivity was then fabricated and measured, and its performance was compared with the simulation results. The proposed methodology can be extended to sensing other joints in the body such as the shoulders, fingers, and knees.

The demand for biomedical devices that can collect high-fidelity data from patients in various environments, inside and outside medical clinics, and can provide high-precision diagnosis and long-term monitoring, is high in the healthcare industry. Among different sensing technologies, this study specifically focuses on providing a review of inductive sensing-based devices used in healthcare. Although the concept of inductive sensing has been used in other fields aside from healthcare, we believe that there is a high potential for a wide range of use of this sensing method across various biomedical devices due to the low cost, flexibility in the sensor design, mature fabrication technologies, and high sensitivity. This review first summarizes the mechanisms of inductive sensing-based devices and design considerations. Then, three main groups of applications that are used in healthcare and rely mainly on inductive sensing for their diagnosis and treatments are presented, including motion tracking and continuous monitoring systems, bio-signal detection systems, and imaging biological tissues. We conclude with a summary of the current status of inductive sensing devices used in healthcare, highlighting the promising capabilities and barriers to overcome.

Gesture recognition is a field of study within human-computer interaction technology and is used in an increasing number of applications. So far, the systems designed for gesture recognition have been able to sense multiple static and dynamic gestures. However, they suffer from some limitations. This study introduces a novel sensing pad system consisting of an array of inductive sensors which can recognize and differentiate specific static hand gestures through machine learning algorithms. It is designed to be a non-contact apparatus where the gestures made by a user can be perceived by the system. It uses five coils, one for each finger, and can sense the fingers that are unfolded while making a particular gesture. Ten volunteer users participated in this study. Ten gestures, numbers 1-10 of the American Sign Language are chosen to be tested upon, 10 times each for every user. The responses from the sensing coils were measured via a data acquisition board and sent to the PC for processing. A total of 1000 responses were recorded and processed using machine learning algorithms which provided an accuracy of 98.7% using 5-fold cross-validation (5F-CV) and 97.3% using leave-one-subject-out CV (LOSO-CV) proving that the system can successfully distinguish hand gestures instantly.