Posts Tagged Hand
[Abstract + References] Research progress and development trend of flexible hand rehabilitation gloves
Posted by Kostas Pantremenos in Paretic Hand, REHABILITATION, Rehabilitation robotics on February 20, 2024
Abstract
The flexible hand rehabilitation glove is proposed to solve the problems of long rehabilitation training time for patients and high workload for doctors, and to make the treatment more effective. With the advances in robotics, robotic-assisted therapy has developed rapidly and has become an essential complement to conventional treatment. To understand flexible hand rehabilitation glove devices, with the different construction types of actuators and drive methods as the mainline, the corresponding study of these structures as the auxiliary lines. The characteristics and the current state of research have been discussed. A brief introduction to manufacturing actuators and rehabilitation systems is also given. Through the analysis of hand rehabilitation gloves, some current advantages and disadvantages are summarized, and future directions and functional diversification are envisaged. Certain feasible research suggestions have been proposed for future development regarding structure, functional diversity, and a combination of driving methods. These include that there will be more combinations of pneumatic and motor driven, combining the advantages of both methods to overcome the disadvantages of each. The structural design will be more in line with anatomy and ergonomics, make it more esthetically pleasing. More innovative controls methods will be adopted to achieve more complex rehabilitation functions.
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[WEB] Smart Glove Technology: Revolutionizing Stroke Recovery
Posted by Kostas Pantremenos in Assistive Technology, Paretic Hand on January 17, 2024
By Zara Nwosu
Stroke can have devastating effects on patients, leading to mobility, functionality, and communication challenges. A key part of stroke recovery is rehabilitation, particularly aimed at restoring the use of affected limbs and hands. Innovative technology is now contributing to this field, with a group of stroke survivors in B.C. set to test a new smart glove designed to aid their recovery.
Understanding the Smart Glove Technology
Developed by engineers and researchers at the University of British Columbia, the smart glove integrates highly sensitive sensors and pressure sensors into a comfortable fabric. These sensors work to track hand and finger movements during rehabilitation exercises, providing precise and fast data. This data can be wirelessly transmitted, enabling remote monitoring and analysis of exercise programs, and potentially transforming stroke recovery procedures.
According to data available on ScienceDaily, and MedicalXpress, the smart glove not only aids with movements but also provides feedback. This makes it a powerful tool for stroke recovery, as it helps patients develop better control over their hand movements, leading to increased mobility and functionality.
Applications Beyond Stroke Recovery
While the primary goal of the smart glove is to aid in stroke recovery, its potential applications extend beyond this. Given its ability to accurately track hand movements and interactions with objects, it can be utilized in the fields of augmented reality, robotics, and virtual reality.
Moreover, the smart glove has the potential to translate American Sign Language into written speech in real-time. This can significantly benefit individuals who are deaf or hard of hearing, opening up new avenues for communication.
The Future of Smart Glove Technology
As reported by MedRiva, and a product review on Amazon, the smart glove technology is in its development and testing phase. Clinical trials are ongoing to evaluate its effectiveness further, and results so far have been promising.
With the advancement of technology, recovery from stroke could become faster and more efficient. Smart glove technology could become a standard part of rehabilitation, helping stroke survivors regain control over their lives.
Conclusion
Stroke recovery is a challenging journey, but innovations like the smart glove are making it more manageable. As technology continues to evolve, we can hope for more such breakthroughs that can significantly improve the quality of life for stroke survivors and other individuals facing mobility challenges.
[WEB] Revolutionizing Stroke Recovery: The Smart Glove Aid
Posted by Kostas Pantremenos in Artificial intelligence, Assistive Technology, Paretic Hand on January 17, 2024
In recent years, technology has played a crucial role in transforming the healthcare sector. Among the exciting innovations, the ‘smart glove’ stands out as a groundbreaking tool designed to aid stroke survivors in their recovery process. Created by a team at Texavie, this innovative technology is set to revolutionize stroke rehabilitation by providing precise and personalized therapy for each individual.
The Concept Behind the Smart Glove
The smart glove is a technological marvel that incorporates a network of highly sensitive sensor yarns and pressure sensors woven into a comfortable, stretchy fabric. This innovative design enables the glove to track even the minutest hand and finger movements during rehabilitation exercises. It’s much like a wearable motion-capture camera, capturing movements with unparalleled precision and speed.
What sets the glove apart is its ability to wirelessly transmit this data, allowing for remote monitoring and analysis of exercise programs. This feature is especially crucial in times where social distancing is the norm, enabling therapists to track their patients’ progress remotely. It also allows patients to engage in rehabilitation exercises from the comfort of their homes, making the recovery process more flexible and less stressful.
The Role of Artificial Intelligence
Artificial Intelligence (AI) plays a pivotal role in the functionality of the smart glove. AI technology not only helps track movements but also analyses the data to provide personalized therapy for each individual. This aspect of individualization is vital in stroke rehabilitation, where each person’s recovery process is unique and requires a tailored approach.
AI-driven technology in the glove adapts the rehabilitation program to the individual’s recovery pace and progress. It can adjust the difficulty level of exercises, making them more challenging as the patient’s hand function and mobility improve. This adaptive capability ensures that the rehabilitation process is efficient and effective, promoting faster recovery.
Testimonials and Success Stories
Stroke survivors who have used this innovative smart glove technology have shared their success stories, providing real-life evidence of the glove’s effectiveness. Users have reported significant improvements in hand function and mobility, contributing to an overall better quality of life post-stroke.
The smart glove has not only helped stroke survivors regain their independence but also boosted their confidence. The ability to track progress in real-time has been highly motivating, encouraging patients to stay committed to their rehabilitation program.
Future Applications
While currently focused on aiding stroke rehabilitation, the smart glove’s potential applications extend beyond this. The technology’s precision and speed match the performance of costly motion-capture cameras, making it a potential tool for use in virtual reality, augmented reality, animation, and robotics. The smart glove technology could significantly impact these fields, making it a truly transformative invention.
In conclusion, the smart glove is a promising leap forward in stroke rehabilitation. It not only offers a personalized approach to recovery but also enables remote therapy, making the process more accessible and convenient for stroke survivors. With its potential to expand into other fields, the smart glove is indeed a game-changer in healthcare technology.
[BOOK Chapter] Dynamic Difficulty Adjustment (DDA) on a Serious Game Used for Hand Rehabilitation
Posted by Kostas Pantremenos in Books, Paretic Hand, Video Games/Exergames on January 14, 2024
Abstract
Serious games have been used for assisting people in physical rehabilitation for hands. People might have different degrees of mobility in their hands; consequently, it would be convenient that the game could be adapted according to the range-of-motion in performing hand movements. This study implemented a serious game for hand rehabilitation with two play modes. Mode one does not adjust the game difficulty; whereas mode two adjusts the game difficulty according to the player’s range-of-motion in performing flexion, extension, ulnar, and radial deviations. The game difficulty was adjusted using fuzzy logic to compute positions at which the rewards will be displayed at the game scene (easy, medium, and difficult positions to collect the rewards). Four participants played both modes. Two-tailed t-tests revealed that there were no significant differences between both modes in terms of rewards collected (p = 0.6621), play time (p = 0.8178), and “game engagement questionnaire” score (p = 0.1383).
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Introduction
Hands play a key role in daily activities. People use them to interact with the world. Nevertheless, due to accidents or medical conditions, people might lose mobility in their hands; consequently, they might require physical rehabilitation.
According to Walsh et al., (2002), “exercise forms a crucial part of a patient’s motor rehabilitation in terms of upper and lower limb function as well as prevention of muscle atrophy” (p. 2).
The main problem in the traditional rehabilitation method is the lack of motivation in patients; therefore, the performances of the rehabilitation exercises might become frustrated and boring. To cope with this issue, robots have been used to assist people during their motor rehabilitation exercises. For instance, a review on robots employed as assistive technologies for rehabilitation on upper limb can be found in (Narayan et al., 2021). In the same vein, robots have been employed for lower limb motor rehabilitation (Alvarez-Perez et al., 2020; Hussain et al., 2017, Hussain et al., 2021).
On the other hand, researchers (Lohse et al., 2013) have studied that video games can be used as a therapeutic tool in physical rehabilitation due to their motivational and engagement properties (e.g., optimal challenge, rewards and feedback provided to the players). As can be seen, these games are focused on assisting people in their rehabilitation processes. These types of games are called serious games. According to Zyda (2005), a serious game can be defined as “a mental contest, played with a computer in accordance with specific rules, that uses entertainment to further government or corporate training, education, health, public policy, and strategic communication objectives” (p. 26).
Figure 1.
Wrist joints
It is important to remark that serious games have been used to assist therapists in the rehabilitation processes of patients on emotional health aspects (e.g., anxiety and depression (Abd-Alrazaq et al., 2022; Barnes & Prescott, 2018; Dias et al., 2018), autism spectrum disorder (Silva et al., 2021; Tsikinas & Xinogalos, 2019), phobias: acrophobia (Sharmili & Kanagaraj, 2020), spider phobia (Lindner et al., 2020)) and motor rehabilitation (e.g. ankle rehabilitation (Hendrickx et al., 2021, Feng et al., 2018), finger rehabilitation (Rahman, 2017; Aguilar-Lazcano & Rechy-Ramirez, 2020), shoulder rehabilitation (Viglialoro et al., 2020; Steiner et al., 2020)), so that the patients could be engaged to the rehabilitation and therapy. Additionally, virtual reality has been used in serious games for upper limb rehabilitation. For instance, Wang et al., (2022) conducted a review on game-based virtual reality systems for upper limb rehabilitation on people that have suffered a stroke to assess the effectiveness of these systems. As a result, authors found that games based on virtual reality for upper limb rehabilitation are more effective than traditional rehabilitation on people suffering cerebral apoplexy.
In terms of wrist motor rehabilitation, the majority of these games are controlled using rehabilitation exercises for the wrist (e.g., flexion, extension, ulnar and radial deviations, pronation and supination of the wrist). The wrist has two main joints, radiocarpal joint and midcarpal joint, that are involved in these rehabilitation exercises (see Figure 1). The intensities of the movements depend on the range-of-motion (ROM) that the patients might have in their hands. According to the American Physical Therapy Association (2001), the range-of-motion “is the arc of motion that occurs at a joint or a series of joints”.
Key Terms in this Chapter
Engagement: it is the feeling of enjoying playing the game (i.e., the involvement in the game).
Fuzzy Logic: It was introduced by Lotfi Zadeh in 1965. Fuzzy logic could be employed to tackle data uncertainty through mainly three processes: fuzzification, fuzzy inference, and defuzzification.
Range-of-Motion: The number of degrees – arc – that a joint could achieve when it is moved from one position to another.
Serious Game: It is a video game played against a computer, where its main purpose is beyond the entertainment (i.e., it is designed to tackle health problems or train people mainly).
Dynamic Difficulty Adjustment (DDA): The game difficulty is modified continuously in the game in order to avoid boredom or frustration on the player.
Leap Motion Controller: It is a sensor based on infrared cameras and leds, which can identify X, Y, Z coordinates of the positions of the finger phalanges, palm, wrist and elbow.
Rehabilitation: A process that is needed in order to recover mobility. This could be done using body movements or exercises.
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Preface
Maki K. Habib
Chapter 1
Alessandro Massaro
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Chapter 2
The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System (pages 18-34)
Tomohiro Yamaguchi
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Chapter 3
Dynamic Difficulty Adjustment (DDA) on a Serious Game Used for Hand Rehabilitation (pages 35-66)
Juan Ignacio Vargas-Bustos, Ericka Janet Rechy-Ramirez
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Chapter 4
Evolution of Blockchain Technology: Principles, Research Trends and Challenges, Applications, and Future Directions (pages 67-104)
Oluwaleke Umar Yusuf, Maki K. Habib
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Chapter 5
Mind of a Portfolio Investor: Which Strategies Should I Use as a Basis for My Investment Decisions (pages 105-119)
Chabi Gupta
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Chapter 6
Modelling of Engineering Systems With Small Data: A Comparative Study (pages 120-136)
Morteza Mohammadzaheri, Mojtaba Ghodsi, Hamidreza Ziaiefar, Issam Bahadur, Musaab Zarog, Mohammadreza Emadi, Payam Soltani, Amirhosein Amouzadeh
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The Theory and Applications of the Software-Based PSK Method for Solving Intuitionistic Fuzzy Solid Transportation Problems (pages 137-186)
P. Senthil Kumar
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Chapter 8
Metrics for Project Management Methodologies Elicitation (pages 187-212)
Patricia R. Cristaldo, Daniela Lopez De Luise, Lucas La Pietra
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Chapter 9
An Overview of Security Issues in Cognitive Radio Ad Hoc Networks (pages 213-246)
Noman Islam, Muhammad Furqan Zia, Darakhshan Syed
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Chapter 10
Energy Harvesting Systems: A Detailed Comparison of Existing Models (pages 247-295)
Afnan Khaled Elhamshari, Maki K. Habib
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Chapter 11
Detecting Phishing URLs With Word Embedding and Deep Learning (pages 296-319)
Ali Selamat, Nguyet Quang Do, Ondrej Krejcar
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Chapter 12
Prospects of Deep Learning and Edge Intelligence in Agriculture: A Review (pages 320-341)
Ali Shaheen, Omar F. El-Gayar
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Chapter 13
Study on Healthcare Security System-Integrated Internet of Things (IoT) (pages 342-362)
S. A. Karthik, R. Hemalatha, R. Aruna, M. Deivakani, R. Vijaya Kumar Reddy, Sampath Boopathi
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[ARTICLE] Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke – Full Text
Posted by Kostas Pantremenos in Paretic Hand, REHABILITATION, Rehabilitation robotics on December 17, 2023
Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain–computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl–Meyer Assessment (FMA), and significant improvements (p < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations (p > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, p = 0.005; high-beta, c.c = 0.71, p = 0.004) and task/rest EEG power ratio changes (delta, c.c = −0.738, p = 0.003; low-beta, c.c = 0.67, p = 0.009; high-beta, c.c = 0.839, p = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.
1 Introduction
Stroke has been the leading cause of acquired disability in adults globally for decades (Mendis, 2013). Although the mortality rate declined with improved healthcare, approximately 80% of stroke victims still experience motor impairment, and more than 30% of patients suffer despite intensive rehabilitation (Lai et al., 2002; Young and Forster, 2007). It is worse for the chronic group with severe motor impairments in the upper limbs. On the one hand, effective interventions like constraint-induced movement therapy (CIMT) may not be applicable to those patients without enough residual active movement (Thrasher et al., 2008). On the other hand, motor recovery in chronic stroke is more challenging due to the decreasing plasticity of spontaneous recovery (Cassidy and Cramer, 2017). Since the upper limbs, especially the hands, play a significant role in daily activity, exploring novel rehabilitation therapies for hand motor recovery in this group is essential (Neumann, 2016). Robot-assisted therapy (RAT) and motor imagery (MI) have been introduced to enhance motor recovery for stroke patients through passive motion or mental practice. However, although these interventions benefit training without requiring patients’ residual ability, rehabilitation effectiveness is still limited by a lack of active engagement (Kwakkel et al., 2008; Ietswaart et al., 2011). Recent advances in brain–computer interface (BCI) technology offer a novel method that could extract the motor intention of patients executing MI to support active rehabilitation training. Related studies have shown promising results that MI-actuated BCI improves motor ability more than pure MI or sham BCI (Ramos-Murguialday et al., 2013; Ang et al., 2014; Pichiorri et al., 2015). However, this intervention still faces limitations in practical use (Mulder, 2007; Baniqued et al., 2021). First, BCI may not be easy for everyone due to the “BCI illiteracy” phenomenon or the limited training schedule in clinical environments (Blankertz et al., 2009; Horowitz et al., 2021). In addition, most stroke subjects show more difficulty executing MI tasks than healthy subjects because of brain impairment in motor-related areas (Mulder, 2007). Worse situations occur in severe patients because they can hardly perform effective MI or fall into fatigue quickly under effortful attempts. Recent studies found that action observation (AO) could also activate sensorimotor features, as in MI and motor execution tasks (Friesen et al., 2017; Hardwick et al., 2017). In addition, repeated AO could induce plasticity changes by activating the mirror neuron system (MNS) (Rizzolatti and Sinigaglia, 2010; Agosta et al., 2017). These inspired studies combined AO in the BCI system, where stronger event-related desynchronization (ERD) responses are found than in pure MI-BCI (Kondo et al., 2015; Ono et al., 2018; Nagai and Tanaka, 2019). However, most of these studies focused on healthy subjects, while related endeavors in the clinical rehabilitation of stroke subjects are still insufficient.
Another major concern in exploring novel interventions in chronic stroke is better evaluating the motor deficits and understanding the therapeutic-induced improvement during rehabilitation neurologically. On the one hand, the recovery in post-stroke motor rehabilitation is usually heterogeneous. Except for individual factors such as age, time since stroke, and related complications, a variety of neuro-clinical factors, such as the degree of brain lesion and neural status, would also affect the patient’s recovery (Riley et al., 2011; Chang et al., 2013; Feng et al., 2015; Kim and Winstein, 2017). On the other hand, chronic stroke recovery is more challenging with the decreasing plasticity of spontaneous recovery and depends more on intervention-induced plasticity (Cassidy and Cramer, 2017). The routinely used assessment of motor recovery is on clinical scales, which are semi-objective and limited in monitoring the underlying neural factors. Hence, recent studies have focused on finding neural biomarkers that could serve as an additional physiological approach to probe brain status and reflect the extent of post-stroke functional recovery (Kim and Winstein, 2017). Potential biomarkers have been found in physiological measuring tools such as Functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG) (Várkuti et al., 2013; Kim and Winstein, 2017).
Compared with these tools, electroencephalography (EEG) offers another economical and widely available choice, making it a more practical approach in clinical environments for rehabilitation (Gerloff et al., 2006; Ang and Guan, 2016). In addition, the EEG is easy to implement in EEG-based BCI interventions. However, most related investigations of EEG markers focused on acute or subacute-stage patients, and studies concerned with chronic patients are still lacking (Foreman and Claassen, 2012; Assenza et al., 2017; Trujillo et al., 2017; Bentes et al., 2018). Notably, EEG oscillations in different bands themselves play roles in reflecting the physiological and pathological status of the neural systems. For example, the increasing low-frequency power (delta and theta bands) and decreasing high-frequency power (alpha and beta bands) are believed to reflect the severity of acute neurological deficits (Rabiller et al., 2015; Assenza et al., 2017). Apart from reflecting the motor status, the EEG features may also promote an understanding of varied recovery resulting from additional factors during rehabilitation. For instance, a previous study found that patients under different interventions have different EEG indicators (Mane et al., 2019). We infer that patients with varying degrees of recovery may also differ in EEG features after experiencing different neural processes in training. Overall, how these EEG oscillations would act in chronic stroke and whether related EEG features could reflect therapeutic-induced improvement in effective interventions remains to be determined.
To fill this gap, the present study aimed to explore whether brain oscillations in different EEG bands can reflect the motor status and recovery induced by novel BCI therapy in chronic stroke. Specifically, an AO-BCI robotic hand training intervention was studied in a clinical environment, and the motor scales were assessed before and after the training. The correlations between EEG band power and motor scales both before and after the intervention were analyzed to study their feasibility in reflecting motor status by EEG band power in chronic stroke patients. In addition, we presented the difference in EEG variation during an intervention on patients with and without effective recovery [whether the minimal clinically important difference (MCID) was reached] (van der Lee et al., 2001; Wagner et al., 2008). Moreover, we examined which EEG rhythm variations correlate with motor function improvement and their potential as markers in reflecting therapeutic-induced neuroplasticity changes and guiding rehabilitation intervention in chronic stroke patients. […]
[Abstract + References] Development of a Finger Rehabilitation Device – Conference paper
Posted by Kostas Pantremenos in Paretic Hand on November 17, 2023
Abstract
This paper aims to develop a device for the rehabilitation of the fingers of the human hand. With the daily use of hands, they can suffer different types of injuries in accidents or injuries caused by different diseases. The rehabilitation of the human hand is fundamental for the recovery of its motor capacity. Hand rehabilitation procedures are generally performed by healthcare professionals who lack specialized equipment. The proposed device consists of an exoskeleton to be placed on the back of the human hand and can be used for a specific finger. This device must be capable of performing the flexion and extension movements of each finger joint independently or in a coupled manner.
References
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[Abstract] Preliminary Results from a Six-Week Home-Based Evaluation of a Rehabilitation Device for Hand and Wrist Therapy After Stroke
Posted by Kostas Pantremenos in Paretic Hand, REHABILITATION, Rehabilitation robotics, Tele/Home Rehabilitation on November 17, 2023
Abstract
While many robot-aided solutions have been proposed for the rehabilitation of the distal segment of the upper limb, very few take into account the synergy between the wrist and fingers to allow them to train simultaneously in a home environment. WiGlove is a passive robotic orthosis designed to address this need. This wearable, wireless device enables stroke survivors to perform flexion/extension exercises of both the wrist and fingers while performing ADL or playing therapeutic computer games. As a part of its user-centred design process, this paper presents a case study of a 6-week feasibility evaluation of the WiGlove conducted at a stroke survivor’s home without assistance from the therapists. The participant trained with the device for an average of 48 minutes a day and showed a noticeable reduction in the spasticity of the fingers and improved performance in the box and block test. He expressed satisfaction with its usability and suitability for the home environment. These results show overwhelmingly positive outcomes in terms of its acceptance, usability and effectiveness in offering home-based rehabilitation of the wrist and fingers.
[Abstract] Functional MRI Assessment of Brain Activity During Hand Rehabilitation with an MR-Compatible Soft Glove in Chronic Stroke Patients: A Preliminary Study
Posted by Kostas Pantremenos in Neuroplasticity, Paretic Hand, Radiology/Imaging technology, Rehabilitation robotics on November 17, 2023
Abstract:
Brain plasticity plays a significant role in functional recovery after stroke, but the specific benefits of hand rehabilitation robot therapy remain unclear. Evaluating the specific effects of hand rehabilitation robot therapy is crucial in understanding how it impacts brain activity and its relationship to rehabilitation outcomes. This study aimed to investigate the brain activity pattern during hand rehabilitation exercise using functional magnetic resonance imaging (fMRI), and to compare it before and after 3-week hand rehabilitation robot training. To evaluate it, an fMRI experimental environment was constructed to facilitate the same hand posture used in rehabilitation robot therapy. Two stroke survivors participated and the conjunction analysis results from fMRI scans showed that patient 1 exhibited a significant improvement in activation profile after hand rehabilitation robot training, indicative of improved motor function in the bilateral motor cortex. However, activation profile of patient 2 exhibited a slight decrease, potentially due to habituation to the rehabilitation task. Clinical results supported these findings, with patient 1 experiencing a greater increase in FMA score than patient 2. These results suggest that hand rehabilitation robot therapy can induce different brain activity patterns in stroke survivors, which may be linked to patient-specific training outcomes. Further studies with larger sample sizes are necessary to confirm these findings.
[Abstract] Understanding Characteristics of User Adherence to Optimize the Use of Home Hand Rehabilitation Technology
Posted by Kostas Pantremenos in Paretic Hand, Tele/Home Rehabilitation on November 17, 2023
Abstract:
Home-based rehabilitation can serve as an adjunct to in-clinic rehabilitation, encouraging users to engage in more practice. However, conventional home-based rehabilitation programs suffer from low adherence and high drop-out rates. Wearable movement sensors coupled with computer games can be more engaging, but have highly variable adherence rates. Here we examined characteristics of user adherence by analyzing unsupervised, wearable grip sensor-based home-hand rehabilitation data from 1,587 users. We defined three different classes of users based on activity level: low users (<2 days), medium users (2 – 9 days), and power users (> 9 days). The probability of using the device more than two days was positively correlated with first day game success (p = 0.91, p<. 001), and number of sessions played on the first day (p = 0.87, p<. 001) but negatively correlated with parameter exploration (total number of game adjustments / total number of sessions played) on the first day (p = – 0.31, p= 0.05). Compared to low users, power users on the first day had more game success (65.18 ± 25.76 %vs. 54.94 ± 30.31 %,p <. 001), parameter exploration (25.47 ± 22.78 % vs. 12.05 ± 20.56 %, p <. 001), and game sessions played (7.60 ± 6.59 sessions vs. 4.04 ± 3.56 sessions, p <. 001). These observations support the premise that initial game success which is modulated by strategically adjusting parameters when necessary is a key determinant of adherence to rehabilitation technology.
[Abstract + References] A new adaptive VR-based exergame for hand rehabilitation after stroke
Posted by Kostas Pantremenos in Paretic Hand, REHABILITATION, Video Games/Exergames, Virtual reality rehabilitation on October 18, 2023
Abstract
The aim of this work is to present an adaptive serious game based on virtual reality (VR) for functional rehabilitation of the hand after stroke. The game focuses on simulating the palmar grasping exercise commonly used in clinical settings. The system’s design follows a user-centered approach, involving close collaboration with functional rehabilitation specialists and stroke patients. It uses the Leap motion controller to enable patient interaction in the virtual environment, which was created using the Unity 3D game engine. The system relies on hand gestures involving opening and closing movements to interact with virtual objects. It incorporates parameters to objectively measure participants’ performance throughout the game session. These metrics are used to personalize the game’s difficulty to each patient’s motor skills. To do this, we implemented an approach that dynamically adjusts the difficulty of the exergame according to the patient’s performance during the game session. To achieve this, we used an unsupervised machine learning technique known as clustering, in particular using the K-means algorithm. By applying this technique, we were able to classify patients’ performance into distinct groups, enabling us to assess their skill level and adapt the difficulty of the game accordingly. To evaluate the system’s effectiveness and reliability, we conducted a subjective evaluation involving 11 stroke patients. The standardized System Usability Scale (SUS) questionnaire was used to assess the system’s ease of use, while the Intrinsic Motivation Inventory (IMI) was used to evaluate the participants’ subjective experience with the system. Evaluations showed that our proposed system is usable and acceptable on a C-level scale, with a good adjective score, and the patients perceived a high intrinsic motivation.
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TBI Rehabilitation 