Objectivizing Measures of Post-Stroke Hand Rehabilitation through Multi-Disciplinary Scales.

There is a wide variety of tools and measures for rehabilitation outcomes in post-stroke patients with impairments in the upper limb and hand, such as paralysis, paresis, flaccidity, and spasticity. However, there is a lack of general recommendations for selecting the most appropriate scales, tests, and instruments to objectively evaluate therapy outcomes. Reviews on upper limb and hand measurements reveal that clinicians' choices of tools and methods are highly varied. Some clinicians and medical teams continue to employ non-standard and unverified metrics in their research and measurements. This review article aims to identify the key parameters, assessed by outcome measures and instruments, that play a crucial role in upper limb and hand rehabilitation for post-stroke patients, specifically focusing on the recovery of hand function. The review seeks to assist researchers and medical teams in selecting appropriate outcome measures when evaluating post-stroke patients. We analyze the measured factors and skills found in these outcome measures and highlight useful tools that diversify assessments and enhance result objectivity through graphical representation. The paper also describes trends and new possibilities in hand outcome measures. Clinicians frequently use proven devices, such as EMG, goniometers, and hand dynamometers. Still, there is a growing trend towards incorporating technologies, such as pose and position estimation, using artificial intelligence, or custom hand grip measurement devices. Researchers are increasingly adopting scales previously successful in orthopedic and surgical patients, recognizing their potential for objectivizing outcomes in neurological patients with post-stroke hand complications. The review included only adults over the age of 18. Outcome measures were tested for usefulness in the rehabilitation of stroke patients.

The Effect of Using a Rehabilitation Robot for Patients with Post-Coronavirus Disease (COVID-19) Fatigue Syndrome.

The aim of this study was to compare the effectiveness of traditional neurological rehabilitation and neurological rehabilitation combined with a rehabilitation robot for patients with post-COVID-19 fatigue syndrome. Eighty-six participants transferred from intensive care units due to post-viral fatigue after COVID-19 were randomly divided into two groups: the intervention group and the control group. The control group received standard neurological rehabilitation for 120 min a day, while the intervention group received the same neurological rehabilitation for 75 min a day, complemented by 45 min of exercises on the rehabilitation robot. The Berg scale, Tinetti scale, six-minute walking test, isokinetic muscle force test, hand grip strength, Barthel Index, and Functional Independence Measure were used to measure the outcomes. Both groups improved similarly during the rehabilitation. Between groups, a comparison of before/after changes revealed that the intervention group improved better in terms of Functional Independence Measure (p = 0.015) and mean extensor strength (p = 0.023). The use of EMG-driven robots in the rehabilitation of post-COVID-19 fatigue syndrome patients was shown to be effective.

Immersion Therapy with Head-Mounted Display for Rehabilitation of the Upper Limb after Stroke-Review.

Immersive virtual therapy technology is a new method that uses head-mounted displays for rehabilitation purposes. It offers a realistic experience that puts the user in a virtual reality. This new type of therapy is used in the rehabilitation of stroke patients. Many patients after this disease have complications related to the upper extremities that limit independence in their everyday life, which affects the functioning of society. Conventional neurological rehabilitation can be supplemented by the use of immersive virtual therapy. The system allows patients with upper limb dysfunction to perform a motor and task-oriented training in virtual reality that is individually tailored to their performance. The complete immersion therapy itself is researched and evaluated by medical teams to determine the suitability for rehabilitation of the upper limb after a stroke. The purpose of this article is to provide an overview of the latest research (2019-2022) on immersive virtual reality with head-mounted displays using in rehabilitation of the upper extremities of stroke patients.

Sensing System for Plegic or Paretic Hands Self-Training Motivation.

Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2-a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.

Hand Measurement System Based on Haptic and Vision Devices towards Post-Stroke Patients.

Diagnostics of a hand requires measurements of kinematics and joint limits. The standard tools for this purpose are manual devices such as goniometers which allow measuring only one joint simultaneously, making the diagnostics time-consuming. The paper presents a system for automatic measurement and computer presentation of essential parameters of a hand. Constructed software uses an integrated vision system, a haptic device for measurement, and has a web-based user interface. The system provides a simplified way to obtain hand parameters, such as hand size, wrist, and finger range of motions, using the homogeneous-matrix-based notation. The haptic device allows for active measurement of the wrist's range of motion and additional force measurement. A study was conducted to determine the accuracy and repeatability of measurements compared to the gold standard. The system functionality was confirmed on five healthy participants, with results showing comparable results to manual measurements regarding fingers' lengths. The study showed that the finger's basic kinematic structure could be measured by a vision system with a mean difference to caliper measurement of 4.5 mm and repeatability with the Standard Deviations up to 0.7 mm. Joint angle limits measurement achieved poorer results with a mean difference to goniometer of 23.6º. Force measurements taken by the haptic device showed the repeatability with a Standard Deviation of 0.7 N. The presented system allows for a unified measurement and a collection of important parameters of a human hand with therapist interface visualization and control with potential use for post-stroke patients' precise rehabilitation.