Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: a multi-stage approach

Abstract

Purpose Post-stroke survivors report that feedback helps to increase training motivation. A wearable system (M-MARK), comprising movement and muscle sensors and providing feedback when performing everyday tasks was developed. The objective reported here was to create an evidence-based set of upper-limb tasks for use with the system. Materials and methods Data from two focus groups with rehabilitation professionals, ten interviews with stroke survivors and a review of assessment tests were synthesized. In a two-stage process, suggested tasks were screened to exclude non-tasks and complex activities. Remaining tasks were screened for suitability and entered into a categorization matrix. Results Of 83 suggestions, eight non-tasks, and 42 complex activities were rejected. Of the remaining 33 tasks, 15 were rejected: five required fine motor control; eight were too complex to standardize; one because the role of hemiplegic hand was not defined and one involved water. The review of clinical assessment tests found no additional tasks. Eleven were ultimately selected for testing with M-Mark. Conclusions Using a task categorization matrix, a set of training tasks was systematically identified. There was strong agreement between data from the professionals, survivors and literature. The matrix populated by tasks has potential for wider use in upper-limb stroke rehabilitation. IMPLICATIONS FOR REHABILITATION Rehabilitation technologies that provide feedback on quantity and quality of movements can support independent home-based upper limb rehabilitation. Rehabilitation technology systems require a library of upper limb tasks at different levels for people with stroke and therapists to choose from. A user-defined and evidence-based set of upper limb tasks for use within a wearable sensor device system have been developed.