BackgroundThis paper describes the design and preliminary testing of FINGER (Finger Individuating Grasp Exercise Robot), a device for assisting in finger rehabilitation after neurologic injury. We developed FINGER to assist stroke patients in moving their fingers individually in a naturalistic curling motion while playing a game similar to Guitar Hero®a. The goal was to make FINGER capable of assisting with motions where precise timing is important.MethodsFINGER consists of a pair of stacked single degree-of-freedom 8-bar mechanisms, one for the index and one for the middle finger. Each 8-bar mechanism was designed to control the angle and position of the proximal phalanx and the position of the middle phalanx. Target positions for the mechanism optimization were determined from trajectory data collected from 7 healthy subjects using color-based motion capture. The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths. For initial evaluation, we asked individuals with a stroke (n = 16) and without impairment (n = 4) to play a game similar to Guitar Hero® while connected to FINGER.ResultsPrecision design, low friction bearings, and separate high speed linear actuators allowed FINGER to individually actuate the fingers with a high bandwidth of control (−3 dB at approximately 8 Hz). During the tests, we were able to modulate the subject’s success rate at the game by automatically adjusting the controller gains of FINGER. We also used FINGER to measure subjects’ effort and finger individuation while playing the game.ConclusionsTest results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.

Background Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning. Objective To determine the therapeutic effects of high and low levels of robotic assistance during finger training. Methods We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32+/−18 and upper extremity Fugl-Meyer score 46+/−12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero three hours/week for three weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8,000 movements during nine training sessions. Results Both groups improved significantly at the one-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength) – particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training. Conclusions Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Further, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model. ClinicalTrials.gov (NCT02048826)

Multi-finger capacity may be an important target for rehabilitative hand training.

Nonobtrusive options for monitoring the wrist and hand movement are needed for stroke rehabilitation and other applications. This paper describes the "manumeter," a device that logs total angular distance travelled by wrist and finger joints using a magnetic ring worn on the index finger and two triaxial magnetometers mounted in a watch-like unit. We describe an approach to estimate the wrist and finger joint angles using a radial basis function network that maps differential magnetometer readings to joint angles. We tested this approach by comparing manumeter estimates of total angular excursion with those from a passive goniometric exoskeleton worn simultaneously as seven participants completed a set of 12 manual tasks at low-, medium-, and high-intensity conditions on a first testing day, 1-2 days later, and 6-8 days later, using only the original calibration from the first testing day. Manumeter estimates scaled proportionally to the intensity of hand activity. Estimates of angular excursion made with the manumeter were 92.5% ± 28.4 (SD), 98.3% ± 23.3, and 94.7% ± 19.3 of the goniometric exoskeleton across the three testing days, respectively. Magnetic sensing of wrist and finger movement is nonobtrusive and can quantify the amount of use of the hand across days.

Wearable sensing is a new tool for quantifying upper extremity (UE) rehabilitation after stroke. However, it is unclear whether it provides information beyond what is available through standard clinical assessments. To investigate this question, people with a chronic stroke (n=9) wore accelerometers on both wrists for 9 hours on a single day during their daily activities. We used principal components analysis (PCA) to characterize how novel kinematic measures of jerk and acceleration asymmetry, along with conventional measures of limb use asymmetry and clinical function, explained the behavioral variance of UE recovery across participants. The first PC explained 55% of the variance and described a strong correlation between standard clinical assessments and limb use asymmetry, as has been observed previously. The second PC explained a further 31% of the variance and described a strong correlation between bimanual magnitude and jerk asymmetry. Because of the nature of PCA, this second PC is mathematically orthogonal to the first and thus uncorrelated with the clinical assessments. Therefore, kinematic metrics obtainable from bimanual accelerometry, including bimanual jerk asymmetry, encoded additional information about UE recovery. One interpretation is that the first PC relates to "functional status" and the second to "movement quality". We also describe a new graphical format for presenting bimanual wrist accelerometry data that facilitates identification of asymmetries.