Serious game for locomotor rehabilitation of hemiparetic stroke patients

Effects of constraint-induced movement therapy on activity and participation after a stroke: Systematic review and meta-analysis

Introduction

Hemiparesis is the main sensorimotor deficit after stroke. It can result in limitations in Activities of Daily Living (ADL) and social participation. Hemiparesis can be treated with behavioral techniques of intensive use of the affected arm, such as constraint-induced movement therapy (CIMT), however, it remains unclear whether motor improvement can lead to increases in the domains of activity and participation.

Objective

Identify whether CIMT is superior to usual techniques to enhance activity and participation outcomes in stroke survivors.

Methods

A systematic review with meta-analysis was conducted, based on the PRISMA guidelines. Search databases were: PubMed, LILACS, Embase, SciELO, Cochrane Library, Scopus, Medline, and Web of Science, with no language restriction. Meta-analysis was performed with Review Manager (version 5.3), significance level p ≤ 0.05.

Results

A total of 21 articles were included for analysis. Superior effects were observed on motor function and performance in activities of daily living of individuals treated with CIMT. The outcomes measures utilized were: Fugl-Meyer Assessment (p = 0.00001); Wolf motor function test (p = 0.01); Modified Barthel Index (p = 0.00001); Motor Activity log (MAL) Amount of use (AOU) (p = 0.01); MAL Quality of movement (QOM) (p = 0.00001); Action Research Arm Test-ARAT (p = 0.00001); and FIM (p = 0.0007).

Conclusion

Our results show that CIMT results in more significant gains in the functional use of the upper limb in ADL and functional independence, demonstrating superior activity and participation results in stroke survivors when compared to conventional therapies.

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers’ knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with stroke survivors. The methodologies utilized professional medical experiences and real-life evidence data gathered during the duration of the iTrain project to create the scenarios for the game’s deep reinforcement caregiver behavior improvement model, as well as the design of game mechanics, game images and game characters, and gameplay implementation. Furthermore, the results of the game’s direct impact on caregivers (n = 25) and stroke survivors (n = 21) in Lithuania using the Geriatric Depression Scale (GDS) and user experience questionnaire (UEQ) are presented. Both surveys had favorable outcomes, showing the effectiveness of the approach. The GDS scale (score 10) revealed a low number of 28% of individuals depressed, and the UEQ received a very favorable grade of +0.8.