A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities

Abstract

Millions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks. Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients' movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient's movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

“…This model (29/63, 46% studies) measures awkward movements of the shoulder of the affected UE [ 15 ], involving complex movements of the shoulder girdle and shoulder joint. The most observed shoulder compensation was shoulder elevation (17/29, 59% studies), followed by shoulder abduction [ 27 , 28 , 64 , 70 , 71 ], shoulder girdle compensatory movement [ 29 - 31 , 64 , 72 ], shoulder forward (protraction) [ 26 , 32 ], and shoulder overflexion [ 22 , 33 , 73 ]. The most commonly used task was reaching task (18/29, 62% studies).…”

“…The most commonly used task was reaching task (18/29, 62% studies). Other tasks involved hand-to-mouth tasks [ 33 , 70 , 73 ], drinking tasks [ 66 , 72 ], elbow flexion-extension task [ 59 , 71 ], daily life activities [ 64 , 65 ], counterclockwise cyclic motions [ 31 ], FMA items [ 22 ], and GRASP [ 69 ]. In all, 12 studies were conducted using a robot-assisted device and 1 with an MR training system [ 26 ].…”

Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

“…Therefore, a hitting control term is developed and combined into the preliminary control law shown in (8) and (9) to suppress the TDE error and achieve desired impedance dynamics.…”

“…Generally, rehabilitation robots can be categorized into two types based on their interaction capacities. One is the end-effector-type rehabilitation robot [3]- [9], which has only a human-robot interaction point at the robot end-effector. Another is the exoskeleton-type rehabilitation robot [10]- [15], which can be worn on the human extremity with multiple connected points.…”

Technically assisted rehabilitation – approaches for the upper extremity

“…Self-motivated learning is supported by end-effector-based robot-assisted rehabilitation. Hennes et al [2] took advantage of smallest commercial inertial measurement units to record the patient's movement and support it by robot-assisted rehabilitation systems. Learning phases with a physiotherapist are used to record patient-specific data that provide tailored movements for the unsupervised phase in which the patient trains on his or her own.…”