A Pilot Study of Compensatory Strategies for Reach-to-Grasp-Pen in Patients with Stroke

Predicting Grip Aperture using Forearm Muscle Activation Data

The performance of activities of daily living (ADLs) is directly related to recovery of motor function after stroke. Because the recovery process occurs primarily in the home, there is a need for tools sensitive to this process that can be used in ambient settings. The goal of the current approach is to use surface electromyography (sEMG) acquired from wearable sensors to capture relevant ADL performance. Our specific focus is on detecting thumb-forefinger aperture. This aperture, which occurs during reach-to-grasp (RTG) movements, is an indicator of potential success of interacting with the environment. Our results suggest that sEMG data can be used to determine increasing thumb-forefinger aperture in a population of non-disabled individuals. We find a statistically significant effect of increased aperture on peak sEMG values (p < 0.001).

Design Optimization of a Soft Robotic Rehabilitation Glove Based on Finger Workspace Analysis

The finger workspace is crucial for performing various grasping tasks. Thus, various soft rehabilitation gloves have been developed to assist individuals with paralyzed hands in activities of daily living (ADLs) or rehabilitation training. However, most soft robotic glove designs are insufficient to assist with various hand postures because most of them use an underactuated mechanism for design simplicity. Therefore, this paper presents a methodology for optimizing the design of a high-degree-of-freedom soft robotic glove while not increasing the design complexity. We defined the required functional workspace of the index finger based on ten frequently used grasping postures in ADLs. The design optimization was achieved by simulating the proposed finger-robot model to obtain a comparable workspace to the functional workspace. In particular, the moment arm length for extension was optimized to facilitate the grasping of large objects (precision disk and power sphere), whereas a torque-amplifying routing design was implemented to aid the grasping of small objects (lateral pinch and thumb-two-finger pinch). The effectiveness of the optimized design was validated through testing with a stroke survivor and comparing the assistive workspace. The observed workspace demonstrated that the optimized glove design could assist with nine out of the ten targeted grasping posture functional workspaces. Furthermore, the assessment of the grasping speed and force highlighted the glove's usability for various rehabilitation activities. We also present and discuss a generalized methodology to optimize the design parameters of a soft robotic glove that uses an underactuated mechanism to assist the targeted workspace. Overall, the proposed design optimization methodology serves as a tool for developing advanced hand rehabilitation robots, as it offers insight regarding the importance of routing optimization in terms of the workspace.

A Pilot Study of Compensatory Strategies for Reach-to-Grasp-Pen in Patients with Stroke

Coordinated reaching and grasping movements may be impaired in patients with poststroke hemiplegia. Patients frequently adopt compensatory strategies, which require investigation. This pilot study used kinematic parameters to examine compensatory strategies by assessing the reach-to-grasp-pen movements in patients with stroke and unaffected participants. Twelve patients with stroke with mild impairment (45.16 ± 12.62 years, 2.41 ± 1.97 months), twelve with moderate impairment (50.41 ± 12.92 years, 3.83 ± 3.58 months), and ten healthy individuals (20.6 ± 0.69 years) performed a reach-to-grasp-pen task. Kinematics parameters of upper limb and fingers, such as movement time, number of movement units, index of curvature, spectral arc length, trunk forward transition, trunk lateral transition, elbow extension, shoulder flexion, shoulder abduction, trunk rotation, arm-plane angle, the joint angles of interphalangeal joints of the thumb, index, middle, ring, and little fingers were examined in the study. These parameters were evaluated with two Microsoft Azure Kinect and Leap Motion, which belong to markerless motion capture systems. Patients with stroke showed longer reaching movement time, less smooth movement trajectories, and more trunk rotation (P < 0.05). In patients with stroke, the metacarpophalangeal joint (MCP) and proximal interphalangeal joint (PIP) of the thumb were flexed in the starting position; the MCP and PIP joints of the index finger in the stroke group were more extended during pen grasp; the range of motion of the MCP of the middle finger and the PIP joints of the middle, ring, and little fingers became greater, suggesting a larger peak aperture (P < 0.05). The more significant extension was observed in the index finger at the end of the grasp, suggesting inadequate flexion (P < 0.05). In clinical practice, the reach-to-grasp-pen task using markless sensing technology can effectively distinguish patients with stroke from healthy individuals and evaluate the recovery and compensation strategies of upper limb and hand functions. It can potentially become an evaluation tool in hospital and community scenes. Accurate identification of abnormal trunk, arm, and finger strategies is crucial for therapists to develop targeted upper limb treatment methods and evaluate treatment effects.