IMU Sensor-based Electronic Goniometric Glove (iSEG-Glove) for clinical finger movement analysis

Abstract

Arthritis remains a disabling and painful disease, and involvement of finger joints is a major cause of disability and loss of employment. Traditional arthritis measurements require labour intensive examination by clinical staff. These manual measurements are inaccurate and open to observer variation. This paper presents the development and testing of a next generation wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors, with bespoke controlling algorithms. Our main objective was to measure finger and thumb joint movement. These dynamic measurements will provide clinicians with a new and accurate way to measure loss of movement in patients with Rheumatoid Arthritis. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application, and require calibration for each glove wearer. Unlike these stateof-the-art data gloves, the Inertial Measurement Unit (IMU) glove uses a combination of novel stretchable substrate material and 9 degree of freedom (DOF) inertial sensors in conjunction with complex data analytics to detect joint movement. Our novel iSEG-Glove requires minimal calibration and is therefore particularly suited to the healthcare environment. Inaccuracies may arise for wearers who have varying degrees of movement in their finger joints, variance in hand size or deformities. The developed glove is fitted with sensors to overcome these issues. This glove will help quantify joint stiffness and monitor patient progression during the arthritis rehabilitation process.

Supervised Classification of Bradykinesia for Parkinson's Disease Diagnosis from Smartphone Videos

“…Furthermore, the use of ubiquitous technology means that the approach may be suitable in a home setting to monitor progression of Parkinson's disease. In addition, it might also be useful for monitoring other conditions in which there are changes in movement over time such as rheumatoid arthritis, in which common symptoms include decreased range of motion and joint stiffness [26], [27].…”

Interactive hand pose estimation using a stretch-sensing soft glove

“…IMUs consist of a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer. Gloves based on 15 [Fang et al 2017], 16 [Connolly et al 2018], or 18 [Lin et al 2018 IMUs have been suggested to recover hand pose. The work of von Marcard et al [2017] leverages 6 IMUs together with an offline optimization to recover full-body pose, and Huang et al [2018] use a bi-directional RNN to learn this mapping from synthetic data and reconstruct fullbody poses in real time.…”

Techniques and methods for monitoring the evolution of upper limb fine motor skills

“…Again, the score is measured through [53]. In the second case, with inertial sensors, it facilitates the measurement (of acceleration and orientation) in the 3 axes of each finger joint [54]. It is important to highlight that there are also sensors such as goniometers, dynamometers [55], potentiometers [56] and in some cases the electromyographic signals are measured to estimate the user's movement [57], [58].…”