Search Results (49)

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

(1) Background: Biofeedback and neurofeedback are gaining attention as non-invasive rehabilitation strategies in Parkinson’s disease (PD) treatment, aiming to modulate motor and non-motor symptoms through the self-regulation of physiological signals. (2) Objective: This review explores the application of biofeedback techniques, electromyographic (EMG) biofeedback, heart rate variability (HRV) biofeedback, and electroencephalographic (EEG) neurofeedback in PD rehabilitation, analyzing their impacts on motor control, autonomic function, and cognitive performance. (3) Methods: This review critically examined 15 studies investigating the efficacy of electromyographic (EMG), heart rate variability (HRV), and electroencephalographic (EEG) feedback interventions in PD. Studies were selected through a systematic search of peer-reviewed literature and analyzed in terms of design, sample characteristics, feedback modality, outcomes, and clinical feasibility. (4) Results: EMG biofeedback demonstrated improvements in muscle activation, gait, postural stability, and dysphagia management. HRV biofeedback showed positive effects on autonomic regulation, emotional control, and cardiovascular stability. EEG neurofeedback targeted abnormal cortical oscillations, such as beta-band overactivity and reduced frontal theta, and was associated with improvements in motor initiation, executive functioning, and cognitive flexibility. However, the reviewed studies were heterogeneous in design and outcome measures, limiting generalizability. Subgroup trends suggested modality-specific benefits across motor, autonomic, and cognitive domains. (5) Conclusions: While EMG and HRV systems are more accessible for clinical or home-based use, EEG neurofeedback remains technically demanding. Standardization of protocols and further randomized controlled trials are needed. Future directions include AI-driven personalization, wearable technologies, and multimodal integration to enhance accessibility and long-term adherence. Biofeedback presents a promising adjunct to conventional PD therapies, supporting personalized, patient-centered rehabilitation models. Full article

This study explores the application of wireless wearable devices within the emerging domain of biomechanical feedback systems for sports and rehabilitation. A critical aspect of these systems is the need for real-time operation, where wearable devices must execute multiple processes concurrently while ensuring specific tasks are performed within precise time constraints. To address this challenge, we developed a specialized, lightweight periodic scheduler for microcontrollers. Extensive testing under various conditions demonstrated that sensor sampling frequencies of 650 Hz and 1750 Hz are achievable when utilizing one and 26 sensor samples per packet, respectively. Receiver delays were observed to be a few milliseconds or more, depending on the application scenario. These findings offer practical guidelines for developers and practitioners working with real-time biomechanical feedback systems. By optimizing sensor sampling frequencies and packet configurations, our approach enables more responsive and accurate feedback for athletes and patients, improving the reliability of motion analysis, rehabilitation monitoring, and training assessments. Additionally, we outline the limitations of such systems in terms of transmission delays and jitter, providing insights into their feasibility for different real-world applications. Full article

Reliable acoustic coupling in a non-handheld mode and reducing the form factor of electronics are specific challenges in making ultrasound wearable. Applications relying on a large field of view (such as tracking of large muscles) induce a need for a large element count to achieve high image quality. In our work, we developed a 256-element linear array for imaging of abdominal muscles with four integrated custom-developed 8:32 multiplexer Integrated Circuits (ICs), allowing the array to be driven by our compact 32 ch electronics. The system is optimized for flexible use in R&D applications and allows adjustable transmit voltages (up to +/−100 V), arbitrary delay patterns, and 12-bit analog-to-digital conversion (ADC) with up to 50 MSPS and wireless (21.6 MBit/s) or USB link. Image metrics (SLL, FWHM) were very similar to a fully populated array driven with a 256 ch system. The contrast allowed imaging of lesions down to 7 cm in the phantom. In a first in-vivo study, we demonstrated reliable acoustic contact even during exercise and were able to visualize deep abdominal muscles such as the TrA. In combination with a muscle tracking algorithm, the change of thickness of the TrA during SSE could be monitored, demonstrating the potential of the approach as biofeedback for physiotherapy training. Full article

Background: Interdialytic weight gain (IDWG), defined as the accumulation of salt and water intake between dialysis sessions, is a critical parameter of fluid management and a marker of adherence to dietary and fluid restrictions in hemodialysis patients. Excessive IDWG has been strongly associated with increased cardiovascular risk, including left ventricular hypertrophy, cardiac dysfunction, and cerebrovascular complications. Additionally, it necessitates more aggressive ultrafiltration, potentially compromising hemodynamic stability, impairing quality of life, and escalating healthcare costs. Despite international guidelines recommending an IDWG target of <4–4.5% of body weight, many patients struggle to achieve this due to barriers in adhering to dietary and fluid restrictions. This review explores the current state-of-the-art strategies to mitigate IDWG and evaluates emerging diagnostic and therapeutic perspectives to improve fluid management in dialysis patients. Methods: A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science to identify studies on IDWG in hemodialysis. Keywords and MeSH terms were used to retrieve peer-reviewed articles, observational studies, RCTs, meta-analyses, and systematic reviews. Non-English articles, case reports, and conference abstracts were excluded. Study selection followed PRISMA guidelines, with independent screening of titles, abstracts, and full texts. Data extraction focused on IDWG definitions, risk factors, clinical outcomes, and management strategies. Due to study heterogeneity, a narrative synthesis was performed. Relevant data were synthesized thematically to evaluate both established strategies and emerging perspectives. Results: The current literature identifies three principal strategies for IDWG control: cognitive–behavioral interventions, dietary sodium restriction, and dialysis prescription adjustments. While educational programs and behavioral counseling improve adherence, their long-term effectiveness remains constrained by patient compliance and logistical challenges. Similarly, low-sodium diets, despite reducing thirst, face barriers to adherence and potential nutritional concerns. Adjustments in dialysate sodium concentration have yielded conflicting results, with concerns regarding hemodynamic instability and intradialytic hypotension. Given these limitations, alternative approaches are emerging. Thirst modulation strategies, including chewing gum to stimulate salivation and acupuncture for autonomic regulation, offer potential benefits in reducing excessive fluid intake. Additionally, technological innovations, such as mobile applications and telemonitoring, enhance self-management by providing real-time feedback on fluid intake. Biofeedback-driven dialysis systems enable dynamic ultrafiltration adjustments, improving fluid removal efficiency while minimizing hemodynamic instability. Artificial intelligence (AI) is advancing predictive analytics by integrating wearable bioimpedance sensors and dialysis data to anticipate fluid overload and refine individualized dialysis prescriptions, driving precision-based volume management. Finally, optimizing dialysis frequency and duration has shown promise in achieving better fluid balance and cardiovascular stability, suggesting that a personalized, multimodal approach is essential for effective IDWG management. Conclusions: Despite decades of research, IDWG remains a persistent challenge in hemodialysis, requiring a multifaceted, patient-centered approach. While traditional interventions provide partial solutions, integrating thirst modulation strategies, real-time monitoring, biofeedback dialysis adjustments, and AI-driven predictive tools represent the next frontier in fluid management. Future research should focus on long-term feasibility, patient adherence, and clinical efficacy, ensuring these innovations translate into tangible improvements in quality of life and cardiovascular health for dialysis patients. Full article

Football players, particularly in youth leagues, face a high risk of lower limb injuries due to improper movement patterns. While programs like FIFA 11+ help reduce injuries, they lack real-time, personalized feedback for biomechanical correction. This concept protocol outlines the development of a DAid^® smart socks-based biofeedback system that integrates biomechanical monitoring with mixed reality (MR) feedback to enhance injury prevention. The DAid^® smart socks, equipped with pressure sensors and inertial measurement units (IMUs), track plantar pressure distribution and the center of pressure (COP). Real-time feedback is delivered via a Meta Quest 3 MR headset, enabling athletes to adjust movement patterns instantly. This protocol establishes a framework for evaluating the system’s feasibility and effectiveness in optimizing biomechanics and reducing injury risks. By combining wearable technology with MR-based feedback, this study advances injury prevention strategies, with potential applications in rehabilitation and performance training. Full article

Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management. Full article

In this study, we present a framework to develop and evaluate a virtual reality exposure therapy system with biofeedback toward scorpion phobia treatment. The system is developed based on the methodology for the development of virtual reality educational environments; usability is evaluated with the System Usability Scale (SUS), the affects are measured with the Positive and Negative Affect Schedule (PANAS), and the biofeedback heart rate is measured in real time using a wearable device and the HypeRate app. A descriptive study was conducted with a non-probabilistic convenience sample of undergraduate students. The non-clinical sample consisted of 51 participants (11 women and 40 men) (mean = 20.75, SD = 2.42 years). The system usability score was 75.49, higher than the average of 68. For positive affects, the average value of the overall sample was 28.18, while for negative affects it was 13.67. The results of this preliminary study, while not determining that the system could currently be applied in clinical settings, demonstrate however that the system can initially be considered as a pre-feasibility study, and if the limitations of the unbalanced non-clinical sample are addressed, it could be used in the future for this purpose. The main contribution is the proposed framework to integrate usability and affects evaluation, as well as biofeedback in a VRET system toward scorpion phobia treatment. Full article

Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback device to enhance movement awareness for individuals with irregular gait and posture. Through the integration of inertial measurement units (IMUs), MATLAB, and sophisticated audio feedback mechanisms, the device offers real-time, intuitive cues to facilitate gait correction and improve functional mobility. Utilising a single wearable sensor attached to the L4 vertebrae, the system captures kinematic parameters to generate auditory feedback through discrete and continuous tones corresponding to heel strike events and sagittal plane rotations. A preliminary test that involved 20 participants under various audio feedback conditions was conducted to assess the system’s accuracy, reliability, and user synchronisation. The results indicate a promising improvement in movement awareness facilitated by auditory cues. This suggests a potential for enhancing gait and balance, particularly beneficial for individuals with compromised gait or those undergoing a rehabilitation process. This paper details the development process, experimental setup, and initial findings, discussing the integration challenges and future research directions. It also presents a novel approach to providing real-time feedback to participants about their balance, potentially enabling them to make immediate adjustments to their posture and movement. Future research should evaluate this method in varied real-world settings and populations, including the elderly and individuals with Parkinson’s disease. Full article

Work-related diseases and disorders remain a significant global health concern, necessitating multifaceted measures for mitigation. One potential measure is work technique training utilizing augmented feedback through wearable motion capture systems. However, there exists a research gap regarding its current effectiveness in both real work environments and controlled settings, as well as its ability to reduce postural exposure and retention effects over short, medium, and long durations. A rapid review was conducted, utilizing two databases and three previous literature reviews to identify relevant studies published within the last twenty years, including recent literature up to the end of 2023. Sixteen studies met the inclusion criteria, of which 14 were of high or moderate quality. These studies were summarized descriptively, and the strength of evidence was assessed. Among the included studies, six were rated as high quality, while eight were considered moderate quality. Notably, the reporting of participation rates, blinding of assessors, and a-priori power calculations were infrequently performed. Four studies were conducted in real work environments, while ten were conducted in controlled settings. Vibration feedback was the most common feedback type utilized (n = 9), followed by auditory (n = 7) and visual feedback (n = 1). All studies employed corrective feedback initiated by the system. In controlled environments, evidence regarding the effectiveness of augmented feedback from wearable motion capture systems to reduce postural exposure ranged from strong evidence to no evidence, depending on the time elapsed after feedback administration. Conversely, for studies conducted in real work environments, the evidence ranged from very limited evidence to no evidence. Future reach needs are identified and discussed. Full article

Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been proven to successfully elicit changes in gait patterns, RAS-based biofeedback as a treatment for TGA has not been explored. In this study, a wearable RAS-based biofeedback gait training system was developed to measure temporal gait symmetry in real time and deliver RAS accordingly. Three different RAS-based biofeedback strategies were compared: open- and closed-loop RAS at constant and variable target levels. The main objective was to assess the ability of the system to induce TGA with able-bodied (AB) participants and evaluate and compare each strategy. With all three strategies, temporal symmetry was significantly altered compared to the baseline, with the closed-loop strategy yielding the most significant changes when comparing at different target levels. Speed and cadence remained largely unchanged during RAS-based biofeedback gait training. Setting the metronome to a target beyond the intended target may potentially bring the individual closer to their symmetry target. These findings hold promise for developing personalized and effective gait training interventions to address TGA in patient populations with mobility limitations using RAS. Full article

Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. Results: Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. Conclusions: Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness. Full article

The potential application of using a wearable force myography (FMG) band for monitoring the occupational safety of a human participant working in collaboration with an industrial robot was studied. Regular physical human–robot interactions were considered as activities of daily life in pHRI (pHRI-ADL) to recognize human-intended motions during such interactions. The force myography technique was used to read volumetric changes in muscle movements while a human participant interacted with a robot. Data-driven models were used to observe human activities for useful insights. Using three unsupervised learning algorithms, isolation forest, one-class SVM, and Mahalanobis distance, models were trained to determine pHRI-ADL/regular, preset activities by learning the latent features’ distributions. The trained models were evaluated separately to recognize any unwanted interactions that differed from the normal activities, i.e., anomalies that were novel, inliers, or outliers to the normal distributions. The models were able to detect unusual, novel movements during a certain scenario that was considered an unsafe interaction. Once a safety hazard was detected, the control system generated a warning signal within seconds of the event. Hence, this study showed the viability of using FMG biofeedback to indicate risky interactions to prevent injuries, improve occupational health, and monitor safety in workplaces that require human participation. Full article

Touch Craft Ltd completed a feasibility study to understand the market position, technologies and user requirement for a smart sock sensing system designed to help monitor foot problems for people living with diabetes. The proposed system would involve a non-invasive, low-cost approach to gathering and measuring skin and foot data considering parameters such as pressure, temperature, and activity levels. The smart sock sensing system would enable both clinicians and patients to have a broader view of foot health and assist in improving the quality of life for patients and their carers. Full article

In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups. Full article