A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems

Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients

BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.

Reinforcement Learning May Demystify the Limited Human Motor Learning Efficacy Due to Visual-Proprioceptive Mismatch

Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment. By applying a reinforcement learning algorithm to the motor learning of elbow joint flexion task, simulation results successfully explained how visual-proprioceptive mismatch limits motor learning outcomes in terms of motor control accuracy and task completion speed. The larger the perceived angular offset between the two sensory modalities, the lower the motor control accuracy. Also, the more similar the peak reward amplitude of the two sensory modalities, the lower the motor control accuracy. In addition, simulation results suggest that insufficient exploration rate limits task completion speed, and excessive exploration rate limits motor control accuracy. Such a speed-accuracy trade-off shows that a moderate exploration rate could serve as another important factor in motor learning.

Development of a Motion-Based Video Game for Postural Training: A Feasibility Study on Older Adults With Adult Degenerative Scoliosis

Forward sagittal alignment affects physical performance, is associated with pain and impacts the health-related quality of life of the elderly. Interventions that help seniors to improve sagittal balance are needed to inhibit the progression of pain and disability. A motion-sensing video game (active game) is developed in this study to monitor sitting and standing postures in real-time and facilitate the postural learning process by using optical sensors to measure body movement and a video game to provide visual feedback. Ten female subjects (mean age: 60.0 ± 5.2 years old; mean BMI: 21.4 ± 1.9) with adult degenerative scoliosis (mean major Cobb's angle: 38.1° ± 22.7°) participate in a 6-week postural training programme with three one-hour postural training sessions a week. Eleven body alignment measurements of their perceived "ideal" sitting and standing postures are obtained before and after each training session to evaluate the effectiveness of postural learning with the game. The participants learn to sit and stand with increased sagittal alignment with a raised chest and more retracted head position. The forward shift of their head and upper body is significantly reduced after each training session. Although this immediate effect only partially sustained after the 6-week program, the participants learned to adjust their shoulder and pelvis level for a better lateral alignment in standing. The proposed postural training system, which is presented as a gameplay with real-time visual feedback, can effectively help players to improve their postures. This pilot feasibility study explores the development and initial assessment of a motion-based video game designed for postural training in older adults with adult degenerative scoliosis, and demonstrates the usability and benefits of active gameplay in motor training.

Using Virtual Reality in a Rehabilitation Program for Patients With Breast Cancer: Phenomenological Study

Background

Surgery is an essential treatment for early-stage breast cancer. However, various side effects of breast cancer surgery, such as arm dysfunction and lymphedema, remain causes for concern. Rehabilitation exercises to prevent such side effects should be initiated within 24 hours after surgery. Virtual reality (VR) can assist the process of rehabilitation; however, the feasibility of applying VR for rehabilitation must be explored, in addition to experiences of this application.

Objective

This study explored patients' attitudes toward and experiences of using VR for their rehabilitation to determine the feasibility of such VR use and to identify potential barriers.

Methods

A phenomenological qualitative study was conducted from September to December 2021. A total of 18 patients with breast cancer who had undergone surgical treatment were interviewed using open-ended questions. The Colaizzi 7-step procedure for phenomenological analysis was used for data analysis. To ensure high study reliability, this study followed previously reported quality criteria for trustworthiness.

Results

Three themes were identified: (1) VR was powerful in facilitating rehabilitation, (2) early and repetitive upper limb movements were an advantage of VR rehabilitation, and (3) extensive VR use had challenges to be overcome. Most of the interviewed patients reported positive experiences of using VR for rehabilitation. Specifically, VR helped these patients identify appropriate motion and angle limits while exercising; in other words, knowledge gained through VR can play a key role in the rehabilitation process. In addition, the patients reported that the use of VR provided them company, similar to when a physiotherapist is present. Finally, the gamified nature of the VR system seemed to make VR-based rehabilitation more engaging than traditional rehabilitation, particularly with respect to early rehabilitation; however, the high cost of VR equipment made VR-based rehabilitation difficult to implement at home.

Conclusions

The interviewed patients with breast cancer had positive experiences in using VR for rehabilitation. The high cost of both VR equipment and software development presents a challenge for applying VR-based rehabilitation.

sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.

Dose Response to Upper Extremity Stroke Rehabilitation Varies by Individual: Early Indicators of Treatment Response

BACKGROUND

Dose response has remained a priority area in motor rehabilitation research for decades, prompting several large randomized trials and meta-analyses. These between-subjects comparisons have revealed equivocal relationships between the duration of motor practice and rehabilitation response. Prior reliance on time-consuming clinical assessments made it infeasible to capture within-subjects dose response, as tracking the dose-response trajectory of an individual requires dozens of repeated administrations.

METHODS

This secondary observational cohort analysis of existing data from the gaming arms of the VIGoROUS multisite trial (Video Game Rehabilitation for Outpatient Stroke) describes the rehabilitation dose response of 80 participants with mild-moderate chronic stroke. The 3-dimensional joint position data were captured via the Kinect v2 optical sensor as participants completed a prescribed 15 hours of in-home unsupervised game-based motor practice. Kinematic dose response trajectories were fitted from hundreds to thousands of in-game repetitions for 4 separate upper extremity movements for each participant.

RESULTS

Of 75 participants with sufficient data for dose-response analysis, 85% showed improved motor capacity for at least 1 movement. Dose response was bimodal; 42% required <5 hours of motor practice before reaching a plateau in movement kinematics, whereas 55% required >10 and 34% required >30 hours. We could predict with 93% accuracy whether or not an individual would ultimately respond to game-based motor practice within 5 hours of gameplay.

CONCLUSIONS

Dose response varies considerably between individuals. About half of chronic stroke patients benefit from higher doses of motor practice than the current standard of care. Individualized dose-response data from motion capture rehabilitation gaming can guide clinical decision-making early on in treatment.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02631850.

Cognitive telerehabilitation: an expert consensus paper on current evidence and future perspective

The progressive improvement of the living conditions and medical care of the population in industrialized countries has led to improvement in healthcare interventions, including rehabilitation. From this perspective, Telerehabilitation (TR) plays an important role. TR consists of the application of telemedicine to rehabilitation to offer remote rehabilitation services to the population unable to reach healthcare. TR integrates therapy-recovery-assistance, with continuity of treatments, aimed at neurological and psychological recovery, involving the patient in a family environment, with an active role also of the caregivers. This leads to reduced healthcare costs and improves the continuity of specialist care, as well as showing efficacy for the treatment of cognitive disorders, and leading to advantages for patients and their families, such as avoiding travel, reducing associated costs, improving the frequency, continuity, and comfort of performing the rehabilitation in its own spaces, times and arrangements. The aim of this consensus paper is to investigate the current evidence on the use and effectiveness of TR in the cognitive field, trying to also suggest some recommendations and future perspectives. To the best of our knowledge, this is the first consensus paper among multiple expert researchers that comprehensively examines TR in different neurological diseases. Our results supported the efficacy and feasibility of TR with good adherence and no adverse events among patients. Our consensus summarizes the current evidence for the application of cognitive TR in neurological populations, highlighting the potential of this tool, but also the limitations that need to be explored further.

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Experience of using a virtual reality rehabilitation management platform for breast cancer patients: a qualitative study

PURPOSES

Postoperative rehabilitation of upper limb function is crucial for breast cancer. Therefore, we developed a rehabilitation management platform using virtual reality to improve rehabilitation compliance and effect. The purpose of this research was to understand the user usability experience of breast cancer patients about the postoperative rehabilitation management of upper limb function using virtual reality.

METHODS

A qualitative descriptive research was designed. We used a maximum difference purpose sampling method. According to the inclusion and exclusion criteria, a 3-armor hospital in Changchun was selected for the recruitment. A one-on-one semi-structured interviews were conducted with patients after breast cancer operation. The Colaizzi seven-step analysis method was used to classify data under summarized themes.

RESULTS

Twenty patients participated in this semi-structured interview. User experience could be summarized into four themes as follows: 1) experience and feeling after using the virtual reality rehabilitation management platform; 2) factors influencing the use of the virtual reality rehabilitation management platform; 3) willingness to recommend the virtual reality rehabilitation management platform to peers; and 4) suggestions to improve the virtual reality rehabilitation management platform.

CONCLUSIONS

Breast cancer patients who used the rehabilitation management platform had a good experience, and their recognition and satisfaction were high. The use of the platform is influenced by many factors, and most patients are willing to recommend this platform to their peers. Future studies should be conducted according to patients' feedback and suggestions on how to further optimize and improve the platform.

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

The analysis of functional upper extremity (UE) movement kinematics has implications across domains such as rehabilitation and evaluating job-related skills. Using movement kinematics to quantify movement quality and skill is a promising area of research but is currently not being used widely due to issues associated with cost and the need for further methodological validation. Recent developments by computationally-oriented research communities have resulted in potentially useful methods for evaluating UE function that may make kinematic analyses easier to perform, generally more accessible, and provide more objective information about movement quality, the importance of which has been highlighted during the COVID-19 pandemic. This narrative review provides an interdisciplinary perspective on the current state of computer-assisted methods for analyzing UE kinematics with a specific focus on how to make kinematic analyses more accessible to domain experts. We find that a variety of methods exist to more easily measure and segment functional UE movement, with a subset of those methods being validated for specific applications. Future directions include developing more robust methods for measurement and segmentation, validating these methods in conjunction with proposed kinematic outcome measures, and studying how to integrate kinematic analyses into domain expert workflows in a way that improves outcomes.

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

Brain Network Organization Following Post-Stroke Neurorehabilitation

Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.

A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task

Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in [Formula: see text] cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.

Video game rehabilitation for outpatient stroke (VIGoROUS): A multi-site randomized controlled trial of in-home, self-managed, upper-extremity therapy

BACKGROUND

Integrating behavioral intervention into motor rehabilitation is essential for improving paretic arm use in daily life. Demands on therapist time limit adoption of behavioral programs like Constraint-Induced Movement (CI) therapy, however. Self-managed motor practice could free therapist time for behavioral intervention, but there remains insufficient evidence of efficacy for a self-management approach.

METHODS

This completed, parallel, five-site, pragmatic, single-blind trial established the comparative effectiveness of using in-home gaming self-management as a vehicle to redirect valuable therapist time towards behavioral intervention. Community-dwelling adults with post-stroke (>6 months) mild/moderate upper extremity hemiparesis were randomized to receive one of 4 different interventions over a 3-week period: 5 h of behaviorally-focused intervention plus gaming self-management (Self-Gaming), the same with additional behaviorally-focused telerehabilitation (Tele-Gaming), 5 h of Traditional motor-focused rehabilitation, or 35 h of CI therapy. Primary outcomes assessed everyday arm use (Motor Activity Log Quality of Movement, MAL) and motor speed/function (Wolf Motor Function Test, WMFT) immediately before treatment, immediately after treatment, and 6 months later. Intent-to-treat analyses were implemented with linear mixed-effects models on data gathered from March 15, 2016 to November 21, 2019. ClinicalTrials.gov, NCT02631850.

RESULTS

Of 193 enrolled participants, 167 began treatment and were analyzed, 150 (90%) completed treatment, and 115 (69%) completed follow-up. Tele-Gaming and Self-Gaming produced clinically meaningful MAL gains that were 1·0 points (95% CI 0·8 to 1·3) and 0·8 points (95% CI 0·5 to 1·0) larger than Traditional care, respectively. Self-Gaming was less effective than CI therapy (-0·4 points, 95% CI -0·6 to -0·2), whereas Tele-Gaming was not (-0·2 points, 95% CI -0·4 to 0·1). Six-month retention of MAL gains across all groups was 57%. All had similar clinically-meaningful WMFT gains; six-month retention of WMFT gains was 92%.

INTERPRETATION

Self-managed motor-gaming with behavioral telehealth visits has outcomes similar to in-clinic CI therapy. It addresses most access barriers, requiring just one-fifth as much therapist time that is redirected towards behavioral interventions that enhance the paretic arm's involvement in daily life.

FUNDING

PCORI, NIH.

A Vector Quantization-Based Spike Compression Approach Dedicated to Multichannel Neural Recording Microsystems

Implantable high-density multichannel neural recording microsystems provide simultaneous recording of brain activities. Wireless transmission of the entire recorded data causes high bandwidth usage, which is not tolerable for implantable applications. As a result, a hardware-friendly compression module is required to reduce the amount of data before it is transmitted. This paper presents a novel compression approach that utilizes a spike extractor and a vector quantization (VQ)-based spike compressor. In this approach, extracted spikes are vector quantized using an unsupervised learning process providing a high spike compression ratio (CR) of 10-80. A combination of extracting and compressing neural spikes results in a significant data reduction as well as preserving the spike waveshapes. The compression performance of the proposed approach was evaluated under variant conditions. We also developed new architectures such that the hardware blocks of our approach can be implemented more efficiently. The compression module was implemented in a 180-nm standard CMOS process achieving a SNDR of 14.49[Formula: see text]dB and a classification accuracy (CA) of 99.62% at a CR of 20, while consuming 4[Formula: see text][Formula: see text]W power and 0.16[Formula: see text]mm² chip area per channel.

Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

Patients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.

Effect of exercise mode on physical function and quality of life in breast cancer-related lymphedema: a randomized trial

PURPOSE

This study aimed to compare the effects of virtual reality (VR) training and resistance exercises training on lymphedema symptom severity as well as physical functioning and QoL in women with breast cancer-related lymphedema (BCRL).

METHODS

In a single blinded randomized trial, women diagnosed with unilateral BCRL were randomly divided into two groups: the Xbox Kinect group received VR Kinect-based games (n = 30) and resistance exercise group received resistance training (n = 30). In addition, both groups received complex decongestive physiotherapy (manual lymphatic drainage, compression bandages, skin care, and exercises). The intervention was conducted five sessions per week for 8 weeks. The outcome measures included excessive limb volume, visual analogue scale (VAS), the Disability of the Arm, Shoulder, and Hand (DASH) questionnaire, shoulder range of motion (ROM), shoulder muscles strength, hand grip strength, and Study Short-Form (SF-36). The outcomes were evaluated pre and post intervention (week 8).

RESULTS

Statistical significant differences were recorded in VAS (pain intensity), DASH, shoulder ROM (p < 0.001), bodily pain (p = 0.002), general health (p < 0.001), and vitality (p = 0.006) in favor of the Xbox Kinect group. However, there were statistically significant differences in shoulder flexion strength (p = 0.002), external rotation strength (p = 0.004), and abduction strength and handgrip strength (p < 0.001) in favor of the resistance exercise group.

CONCLUSIONS

The VR training was superior to resistance exercises training in BCRL management. The empirical findings support the VR as a new effective and encouraging intervention modality which can assist in improving physical functioning and quality of life in women with BCRL.

TRIAL REGISTRATION

This study is retrospectively registered at ClinicalTrials.gov (ID: NCT04724356).

Virtual Reality as a Promising Tool Supporting Oncological Treatment in Breast Cancer

Breast cancer (BC) treatment is associated with many physical and psychological symptoms. Psychological distress or physical dysfunction are one of the most common side effects of oncological treatment. Functional dysfunction and pain-related evasion of movement may increase disability in BC. Virtual reality (VR) can offer BC women a safe environment within which to carry out various rehabilitation interventions to patient support during medical procedures. The aim of this systematic review was to conduct an overview of the clinical studies that used VR therapy in BC. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines method: the initial search identified a total of 144 records, and 11 articles met the review criteria and were selected for the analysis. The results showed that VR seems to be a promising tool supporting oncological treatment in BC patients. VR can have a positive effect on mental and physical functions, such as relieving anxiety during oncotherapy, diminution pain syndrome, and increasing the range of motion and performance in daily activities.

Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain-Computer Interfaces

Motor imagery (MI) based brain-computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corresponding features obtained from the traditional CSP algorithm. In this paper, we designed a novel CSP feature selection framework that combines the filter method and the wrapper method. We first evaluated the importance of every CSP feature by the infinite latent feature selection method. Meanwhile, we calculated Wasserstein distance between feature distributions of the same feature under different tasks. Then, we redefined the importance of every CSP feature based on two indicators mentioned above, which eliminates half of CSP features to create a new CSP feature subspace according to the new importance indicator. At last, we designed the improved binary gravitational search algorithm (IBGSA) by rebuilding its transfer function and applied IBGSA on the new CSP feature subspace to find the optimal feature set. To validate the proposed method, we conducted experiments on three public BCI datasets and performed a numerical analysis of the proposed algorithm for MI classification. The accuracies were comparable to those reported in related studies and the presented model outperformed other methods in literature on the same underlying data.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

Detecting Connected Consciousness During Propofol-Induced Anesthesia Using EEG Based Brain Decoding

Connected consciousness refers to the state when external stimuli can enter into the stream of our consciousness experience. Emerging evidence suggests that although patients may not respond behaviorally to external stimuli during anesthesia, they may be aware of their surroundings. In this work, we investigated whether EEG based brain decoding could be used for detecting connected consciousness in the absence of behavioral responses during propofol infusion. A total of 14 subjects participated in our experiment. Subjects were asked to discriminate two types of auditory stimuli with a finger press during an ultraslow propofol infusion. We trained an EEG based brain decoding model using data collected in the awakened state using the same auditory stimuli and tested the model on data collected during the propofol infusion. The model provided a correct classification rate (CCR) of [Formula: see text]% when subjects were able to respond to the stimuli during the propofol infusion. The CCR dropped to [Formula: see text]% when subjects ceased responding and further decreased to [Formula: see text]% when we increased the propofol concentration by another 0.2 [Formula: see text]g/ml. After terminating the propofol infusion, we observed that the CCR rebounded to [Formula: see text]% before the subjects regained consciousness. With the classification results, we provided evidence that loss of consciousness is a gradual process and may progress from full consciousness to connected consciousness and then to disconnected consciousness.

Detection of the Intention of Direction Changes During Gait Through EEG Signals

Brain-Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

The use of videogames and motion capture systems in rehabilitation contributes to the recovery of the patient. This systematic review aimed to explore the works related to these technologies. The PRISMA method (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was used to search the databases Scopus, PubMed, IEEE Xplore, and Web of Science, taking into consideration four aspects: physical rehabilitation, the use of videogames, motion capture technologies, and upper limb rehabilitation. The literature selection was limited to open access works published between 2015 and 2020, obtaining 19 articles that met the inclusion criteria. The works reported the use of inertial measurement units (37%), a Kinect sensor (48%), and other technologies (15%). It was identified that 26% used commercial products, while 74% were developed independently. Another finding was that 47% of the works focus on post-stroke motor recovery. Finally, diverse studies sought to support physical rehabilitation using motion capture systems incorporating inertial units, which offer precision and accessibility at a low cost. There is a clear need to continue generating proposals that confront the challenges of rehabilitation with technologies which offer precision and healthcare coverage, and which, additionally, integrate elements that foster the patient's motivation and participation.

Palliative care and infection management at end of life in nursing homes: A descriptive survey

BACKGROUND

Infections are common occurrences at end of life that are associated with high rates of morbidity and mortality among frail elderly individuals. The problem of infections in nursing homes has led to a subsequent overuse and misuse of antibiotics in this already-frail population. Improving palliative care in nursing homes has been proposed as a key strategy to reduce the use of antibiotics.

AIM

The aim of this study was to describe the current status of how nursing homes integrates palliative care and infection management at end of life across the nation.

DESIGN

This is a cross-sectional survey of nationally representative US nursing homes.

SETTING/PARTICIPANTS

Between November 2017 and October 2018, a survey was conducted with a nationally representative random sample of nursing homes and 892 surveys were completed (49% response rate). The weighted study sample represented 15,381 nursing homes across the nation.

RESULTS

Most nursing homes engaged in care plan documentation on what is important to residents (90.43%) and discussed spiritual needs of terminally ill residents (89.50%). In the event of aspiration pneumonia in terminally ill residents, 59.43% of nursing homes responded that resident would be transferred to the hospital. In suspected urinary tract infection among terminally ill residents, 66.62% of nursing homes responded that the resident will be treated with antibiotics.

CONCLUSION

The study found wide variations in nursing home palliative care practices, particularly for timing of end-of-life care discussions, and suboptimal care reported for antibiotic usage. Further education for nursing home staff on appropriate antibiotic usage and best practices to integrate infection management in palliative care at the end of life is needed.

Is Xbox 360 Kinect-based virtual reality training as effective as standard physiotherapy in patients undergoing breast cancer surgery?

PURPOSE

Breast cancer surgery may be associated with pain and physical symptoms in the upper limbs. Functional impairment and pain-related avoidance of movement may further increase disability level. This study aimed to investigate the potential effects of early postoperative virtual reality (VR) therapy on pain, range of motion (ROM), muscle strength, functionality, and fear of movement.

METHODS

Forty women with breast cancer who had undergone unilateral mastectomy with axillary lymph node dissection and who were receiving adjuvant therapy were included in the study and randomly assigned to two groups: the Kinect-based rehabilitation group (KBRG) and the standardized physical therapy group (SPTG). The KBRG (n = 20) received VR therapy using Xbox Kinect-based games and the SPTG (n = 20) received standard physiotherapy. Study subjects were assessed at baseline and after the 6-week treatment. Outcome measures were pain (visual analogue scale), grip strength (dynamometer), functionality (disabilities of the arm shoulder and hand questionnaire), muscle strength (handheld dynamometer), ROM (digital goniometer), and fear of movement (Tampa kinesiophobia scale (TKS)).

RESULTS

Both groups detected significant changes in pain, ROM, muscle strength, grip strength, functionality, and TKS scores after the treatment (p < 0.01). Fear of movement was significantly improved in the KBRG and the SPTG displayed more improvement in functionality (p < 0.05). There were no differences in ROM, muscle strength, grip strength, and pain between the groups after the treatment (p > 0.05).

CONCLUSION

Kinect-based VR therapy resulted in significant outcomes that were comparable to those obtained under standard physiotherapy in the early postoperative phase in patients who had undergone breast cancer surgery.

TRIAL REGISTRATION

This study is registered at ClinicalTrials.gov ( ClinicalTrials.gov identifier: NCT03618433).