The effectiveness of upper extremity rehabilitation in post-stroke patients significantly depends on patient motivation and adherence to therapeutic regimens. Rehabilitation-assistive technologies, including wearable sensors, have been adopted to facilitate intensive and repetitive exercises aimed at reducing hand dysfunction and enhancing quality of life. Building upon the previously introduced Przypominajka (reminder) system reported in this journal—a wearable sensory glove coupled with a mobile application providing exercise guidance and monitoring—we conducted a feasibility study to evaluate its effectiveness in supporting upper limb rehabilitation. Sixteen post-stroke patients with hemiparesis were equally randomized into experimental and control groups. Both groups performed upper limb exercises for 45 min daily for over two weeks. The experimental group utilized the sensor-equipped glove and tablet-based exercises, whereas the control group followed printed exercise instructions. Clinical improvements were measured using the Fugl–Meyer Assessment–Upper Extremity (FMA-UE), Functional Independence Measure (FIM), and MORE scales. The experimental group demonstrated a minimal clinically important difference (MCID) on the FMA-UE and reported greater overall improvement than the control group. This study confirms the feasibility and potential clinical benefit of supplementing post-stroke rehabilitation with sensor-augmented exercises provided by the previously described Przypominajka device.

Upper-limb paresis is one of the main complications after stroke. It is commonly associated with impaired wrist-extension function. Upper-limb paresis can place a tremendous burden on stroke survivors and their families. A novel soft-actuator device, the Balonikotron, was designed to assist in rehabilitation by utilizing a balloon mechanism to facilitate wrist-extension exercises. This pilot study aimed to observe the functional changes in the paralyzed upper limb and improvements in independent and cognitive functions following a 4-week regimen using the device, which incorporates a multimedia tablet application providing audiovisual feedback. The device features a cardboard construction with a hinge at wrist level and rails that guide hand movement as the balloon inflates, controlled by a microcontroller and a tablet-based application. It operates on the principle of moving the hand at the wrist by pushing the palm upwards through a surface actuated by a balloon. A model was developed to describe the relationship between the force exerted on the hand, the angle on hinge, the pressure within the balloon, and its volume. Experimental validation demonstrated a Pearson correlation of 0.936 between the model’s force predictions and measured forces, supporting its potential for real-time safety monitoring by automatically shutting down when force thresholds are exceeded. A pilot study was conducted with 12 post-stroke patients (six experimental, six control), who participated in a four-week wrist-extension training program. Clinical outcomes were assessed using the Fugl–Meyer Assessment for the Upper Extremity (FMA-UE), Modified Rankin Scale (mRS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), wrist Range of Motion (ROM), and Barthel Index (BI). Statistically significant results were obtained for the Barthel index (p < 0.05) and FMA-UE, indicating that the experimental use of the device significantly improved functional independence and self-care abilities. The results of our pilot study suggest that the Balonikotron device, which uses the principles of mirror therapy, may serve as a valuable adjunct to conventional rehabilitation for post-stroke patients with hemiparetic hands (BI p = 0.009, MMSE p = 0.151, mRS p = 0.640, FMA-UE p = 0.045, MOCA p = 0.187, ROM p = 0.109).

Soft Robotics experiments necessitate advanced measurement techniques to track robot pose and deformations accurately. Electromagnetic tracking systems provide substantial benefits over vision-based systems, especially in capturing both the position and orientation of a robot's surface without occlusion issues. However, these systems are typically designed for applications in controlled environments, with multiple guidelines stated by the producers, necessitating an evaluation of their effectiveness in different contexts. This paper presents a series of foundational experiments to assess the capabilities and limitations of such a system in Soft Robotics. We investigate the impact of distance from the base station, resistance to environmental interference, effects of metallic elements, and the accuracy of rotational measurements. The results demonstrate a significant influence of positional accuracy relative to the distance from the transmitter, with a tenfold decrease in accuracy observed. Additionally, the presence of metallic objects in close proximity (less than 20mm) to the probes adversely affects accuracy, as do large metallic elements such as construction materials. This effect is particularly pronounced when the sampling frequency of the system aligns with multiples of the electrical network frequency, resulting in more than a tenfold reduction in accuracy.

Robotic rehabilitation devices that utilize embedded systems, such as the Stretchbox - a device de-signed for hand rehabilitation through the actuation of rollers stretching a rubber band by BLDC motors-demand controllers that are safe, scalable, and understandable at a high level. Behavior trees satisfy these requirements and have found broad applications in robotics and some pioneering applications in the medical sector. Despite their widespread use, there has been an absence of behavior tree frameworks for micro controllers programmed with MicroPython, which is an attractive language for embedded systems and the chosen platform for the Stretchbox's high-level controller. This paper introduces a behavior tree implementation for MicroPython tailored as a high-level controller for the Stretchbox device. The implementation includes standard behavior tree nodes, specialized nodes, and classes designed for interfacing with micro controller peripherals and for Blue-tooth communication with the tablet-based user interface. This system provides a comprehensible and straightforward method for controlling the Stretchbox device for rehabilitation, establishing a well-defined hierarchy for event management. Furthermore, the solution is designed to be extensible, allowing for adding new therapeutic behaviors and integrating extra devices. As an open-source library, the implementation facilitates the adoption of behavior trees in other MicroPython- based projects. The detailed explanation of the approach in applying behavior trees to the Stretchbox serves as a practical demonstration of how behavior trees can be effectively used in controlling rehabilitation devices.

In this paper we describe the development of curricula for training educators for the use of robotics in adult education. The goal of this Erasmus+ project was to improve the teachers', trainers', and mentors' abilities in using robotics, with a special focus on persons with disabilities. The proposed approach was to provide materials both for teaching robotics to improve STEM skills as well as show robotics in larger context of their possible social impact, growing role of AI in society and ethical issues related to the use of robots. The project resulted in a database of educational robots and a series of workshops with a website based materials for teachers. The website and the materials were evaluated in a pilot study in Poland and the results are presented in this paper. The results show that the materials are useful for educators and the workshops are effective in improving the teachers' skills in using robotics in education.

This paper delineates an innovative approach to teaching the Denavit-Hartenberg (D-H) notation at the Lodz University of Technology's Institute of Automatic Control. Recognizing the challenges students face in mastering D-H notation, including ambiguous terminology, specialized indexing, and a lack of clarity in its application, a structured teaching methodology is introduced along with the motivation behind it. This method divides the D-H procedure into distinct, meaningful units, each represented with mnemonic acronyms, to facilitate easier understanding and retention. Our approach extends to the systematic coding of kinematic chains and is complemented by practical exercises tailored to enhance comprehension.

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.

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.

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.

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.

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.

This is data containing validation and training datasets (5-fold cross validation) for replicating the results presented in the paper "Sensing System for Plegic or Paretic Hands Self-Training Motivation" for using deep learning models for categorizing data (anomaly, category) while healthy participants were using Przypominajka device

We present an inexpensive, passive device supporting patient's hand rehabilitation while their hand is in a plegic state. The device's standalone capabilities allow patients to train their hand while in bed. A simple gamification scheme using flexion sensors and an accelerometer as input provides a way to remind patients to exercise and motivate them. WiFi connectivity to a cloud-based therapist interface allows monitoring of patient activity. The resulting device can support the hospital and house rehabilitation.

This paper presents a measurement system for after-stroke patients with disabled hands, based on an integrated vision system – Leap Motion. The device provides a simplified way to measure a set of hand parameters such as hand size, wrist, and fingers range of motions. Using the homogeneous-matrix-based notation, we derive medically relevant medical parameters. We provide a user interface for ease of data processing, experiments, and tracking patient progress. The system is evaluated for precision and measurement variation and compared to measurements based on goniometer and caliper. Evaluation of the system shows comparable results to caliper measurements for hand bone lengths while higher variation and large difference in wrist and finger flexion range of motion measurements compared to goniometer.

To comprehensively investigate the grasping behavior of the human hand during tomato picking, a new 16-channel data acquisition e-glove was developed and subsequently worn to randomly pick 60 tomatoes in a greenhouse. A 2-channel surface electromyography was used to record the electrical activity of the flexor pollicis longus and flexor digitorum superficialis in real time. Each fruit-picking process was divided into fruit-stem separation, fruit moving and release stages based on two peak pressures applied by the thumb distal region. The total grasping pressure at the moment of fruit-stem separation primarily depended on the intersection angle between the first and second stems of a fruit. Conversely, at the moment of fruit release, the total grasping pressure primarily depended on the fruit size (p < 0.05). The duration of the tomato-picking process ranged from 2.28 to 4.54 s. The duration of the separation between the first and second stems of a fruit ranged from 0.26 to 1.56 s, and the duration of the fruit release ranged from 0.34 to 2.02 s. The entry and departure sequences of 16 hand regions varied during picking. The frequency analysis results showed that the thumb distal region always entered first to contact the fruit surface for fruit-stem separation, while the ring finger distal region always departed first from the fruit surface for fruit release. At the moment of fruit-stem separation, the number of contact regions between the hand and the fruit significantly increased with the fruit size (p < 0.05), which was slightly different from that at the moment of fruit release. The distal regions of the thumb, middle finger and index finger always made the greatest pressure contribution to the picking of each fruit. The fruit surface pressure applied by the thumb and the other four fingers were moderately related to the mean absolute values of the electrical activity level of the flexor pollicis longus and the flexor digitorum superficialis, respectively.

We present the methodology and results of participatory design of a robot for presenting an epileptic seizure and a scenario of the educational workshop using this robot. Children with epilepsy encounter stigma and stereotypes and may receive inadequate aid when having an epileptic seizure. The goal of the larger project was to use the prototype device in a series of workshops for improving teachers' actions during an epileptic seizure and their attitudes towards epileptic students. In this paper, we show how various design goals for an educational robot were accomplished to fit the needs of all identified stakeholders, particularly people with epilepsy. We used a co-design (participatory design) approach through a series of meetings participated by members of the association Polish Association for People Suffering from Epilepsy, students and faculty members of the biomedical engineering and robotics departments, teachers, psychologists and medical specialists (epileptologist, neurologist). These meetings created an opportunity for a better understanding of the (functional and nonfunctional) requirements and resulting tradeoffs and led the participants to find appropriate solutions. Participation of people with epilepsy in the design process allowed them to deal with the potentially stereotyped representation of themselves. The prototype robot, therefore, combined goals of various stakeholders, such as an accurate presentation of an epileptic seizure, lightweight, ease of use and control, while preserving the dignity of people with epilepsy. As a result of the co-designing process, an inexpensive robot was created and used in a series of 10 pilot workshops with 217 participants, mainly teachers of primary and middle schools. Teachers improved their understanding of epilepsy and suggested further improvements to the system.

Herein, we present a pilot study on the adjustability for grasping force (AGF) of patients with autism. The AGF is the ability to grasp an object with an appropriate force. In our previous study, we developed a training and testing device for the AGF and a system to use it, called iWakka. Impairments in motor functioning, which until recently have been rarely a primary focus in autism spectrum disorder research, may be associated with key social communication deficits. Patients with autism are also suggested to exhibit a longer yaw displacement, indicating a larger head turning, than those without autism. A pilot study was conducted to explore the applicability of the above-mentioned device to autistic patients; eight participants with autism were involved in this study. Three participants could not use iWakka without assistance. The AGF of the four participants who could use iWakka by themselves was improved after training. Moreover, we noticed a reduction in the yaw head displacement of three participants. Because the head pose is an important indicator of the focus of attention of a person, this result suggested that their focus of attention was also improved. Therapists were interviewed for their opinions. Some activities of the participants changed after training with iWakka (e.g., they could sit and focus for longer than before). It was suggested that iWakka has the potential to improve the AGF and other abilities of patients with autism.