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

Abstract

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.

Keywords: assistive devices; brain-computer interface; functional assessment; hand function impairments; monitoring devices; neurorehabilitation; sensor gloves.

Figure 1

Schematic of Brain Computer Interface (BCI) operation in the context of this review. Brain-generated signals are acquired through electroencephalography (EEG), electrocorticography (ECOG), magnetoencephalography (MEG), etc., and processed to extract key features related to hand movement (e.g., alpha modulation). These features can be used to trigger or synced a hand-worn device (e.g., assistive exoskeleton or sensor glove) to provide proprioceptive feedback to the subject.

Figure 2

PRISMA flowchart for the Hand-Worn Device Review for rehabilitation and functional assessment for individuals suffering from impaired hand function. This review conducted searches in four different databases to search for literature (i.e., research articles, patents, other reviews) that focus on hand-worn devices. From the original 537 references found, only 81 of those references remained after exclusion criteria (i.e., must be a device attached to the hand) were applied.

Figure 3

The Hand-Worn Device Review literature selection process is shown in this flow chart. From the original four databases, there were four separate searches done. Each search produced a number of sources that were narrowed down by using the exclusion process. Afterwards, duplicates and same author same device references were removed. This led to a total of 81 references in this paper.

Figure 4

Publication year for the references from the Hand-Worn Device Review show that the majority of the literature was written in the past decade between 2009 and 2022. There is an increase trend of interest in hand-worn devices, particularly after 2016. This projected trend can be used to predict the growing increase in hand-worn devices for rehabilitation and assessment for individuals with impaired hand function.

Figure 5

(A) The majority of the 81 references from the Hand-Worn Device Review are research articles (76%), while the second leading source is patents (15%). Only 5% of these references are literature reviews that focus on hand-worn devices for rehabilitation and assessment for individuals with impaired hand function. (B) The patient population distribution shows that 60% of the population who are targeted to wear these devices are stroke patients. While 19% of these references target any individual who has impaired hand function (given the term: “Not Specified” in the chart).

Figure 6

The distribution of the type of device found in all references from the Hand-Worn Device Review showed that sensor-based devices and assistive devices were the most researched or tested. Sensor-based devices make up almost half of the references, while assistive devices make up 38% of the references. FES devices only make up 8% of the 81 references. The smaller pie chart to the right shows the breakdown of all the device combinations from the 7% of the 81 references found in the Hand-Worn Device Review.

Figure 7

This pie chart shows how many different devices from the Hand-Worn Device Review used a particular sensor type. The most used type of sensor was flex or bend sensors that measure the amount the finger curls. The Inertial Measurement Unit (IMU) sensors were the second most used sensors. The least used sensor was the electromyography (EMG) sensors. The total amount of unique sensors devices was found to be 38. References that use the same device were only counted once in the pie chart.

Figure 8

This pie chart shows the relative numbers of articles found on different types of assistive devices in the Hand-Worn Device Review. The most common assistive instrumentation was the electrical actuators followed by the pneumatic actuators. These actuators are attached to the fingers to aid in extension and contraction. The total number of unique devices was 27. Articles that referred to the same device were only counted once.

Figure 9

PRISMA flowchart for the BCI Hand-Worn Device Sub-Review for rehabilitation and functional assessment for individuals suffering from impaired hand function. This review conducted four different searches for literature (i.e., research articles, clinical trials, control trials, other reviews) that focus on BCI hand-worn devices. From the original 54 references found, only 13 of those references remained after exclusion criteria (i.e., must be a device attached to the hand) were applied.

Figure 10

Publication year for the extracted research articles from the 13 BCI Hand-Worn Device Sub-Review, which shows that the majority of the literature was written between 2014 and 2020. Important to note that there was a decrease in interest after 2019.

Figure 11

This pie chart shows the number of unique devices used in the relative research articles extracted from the BCI Hand-Worn Device Sub-Review. Majority of the research articles used electrical actuators in the form of an exoskeleton or orthosis as the proprioceptive feedback in their BCI system. None of these systems included only a sensor-based device to provide accurate monitoring and confirmation of hand movement. There were, however, four systems that used electrical actuators with sensor-based devices. There was a total of 26 unique systems from the 32 research articles extracted from the 13 references. Articles that referred to the same device were only counted once.

Figure 12

This pie chart shows the number of times a particular brain activity monitoring device was used in the relative research articles from the BCI Hand-Worn Device Sub-Review. The majority of the research groups (i.e., 27 out of 32 articles) used EEG as the brain activity monitoring (BAM) device for their BCI system. EEG, electroencephalogram; ECOG, electrocorticography; MEG, magnetoencephalography; EOG, electro-oculography; fMRI, functional magnetic resonance imaging.

Figure 13

Venn diagram containing all of the devices from the Hand-Worn Device Review and the devices extracted from the BCI Hand-Worn Device Sub-Review. This diagram shows how much each device relates to the others. The size of the circle represents the amount of research and development being done in that particular area. Their relation to each other is shown by the amount of overlap between the circles. The BCI in this diagram stands for the BCI systems that are specifically documented with a hand-worn device. There were more BCI-Assistive systems than BCI-Sensor-based or -FES systems.