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

Abstract

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.

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

“…We selected ten commonly used grasping postures in ADLs, including the previously mentioned five postures: medium wrap, thumb-2-finger pinch, lateral pinch, tripod grip, and power sphere, and five additional postures: precision disk, lateral tripod, thumb-3-finger, light tool, and index finger extension [18][19][20]. These postures can be represented by the joint angles of the index finger while performing the corresponding grasp [20][21][22][23][24][25][26][27]. A human finger movement can be characterized by a minimum of 2 DoFs [28][29][30].…”