An instrumented glove for monitoring hand function

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Introduction

Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.

Methods

Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.

Results

A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.

Discussion

We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.

Development of Joint Activity Angle Measurement and Cloud Data Storage System

In this study, we developed a range of motion sensing system (ROMSS) to simulate the function of the elbow joint, with errors less than 0.76 degrees and 0.87 degrees in static and dynamic verification by the swinging and angle recognition modules, respectively. In the simulation process, the ɣ correlation coefficient of the Pearson difference between the ROMSS and the universal goniometer was 0.90, the standard deviations of the general goniometer measurements were between ±2 degrees and ±2.6 degrees, and the standard deviations between the ROMSS measurements were between ±0.5 degrees and ±1.6 degrees. With the ROMSS, a cloud database was also established; the data measured by the sensor could be uploaded to the cloud database in real-time to provide timely patient information for healthcare professionals. We also developed a mobile app for smartphones to enable patients and healthcare providers to easily trace the data in real-time. Historical data sets with joint activity angles could be retrieved to observe the progress or effectiveness of disease recovery so the quality of care could be properly assessed and maintained.

A Modular Hybrid Exoskeletal-Soft Glove for High Degree of Freedom Monitoring Capability

In this paper, we present a hybrid exoskeletal-soft glove for the application of on-axis angle sensors that can be placed close to the center of rotation of the digit joints. 3D printed exoskeletal digit segments that run medially on most digits connect to low friction bearings. Exoskeletal segments and bearings provide rigid fixation points for a variety of traditional angle sensors, while a combination of textile and rigid structure fixate exoskeletal digits to the digits and hand. Exoskeletal digits are designed modularly so that only required digits are used and to reduce difficulty in donning and doffing. On-axis measurement may prove useful in control or assessment tasks in rehabilitation. The articulation of the digits while wearing the glove is demonstrated, albeit without sensors, showing little restriction at an early stage of the design process. Exoskeletal metacarpophalangeal joints of the 3rd and 4th digits require more work as the flexion/extension joint axis is inaccessible and moves when he digits are articulated. The proposed device must be customized for an individual and will facilitate an alternative approach to existing hand posture monitoring techniques.