Search Results (126)

Background/Objectives: Home Enteral Nutrition Therapy (HENT) is widely used for patients with preserved gastrointestinal function who cannot maintain adequate oral intake. It can be administered through commercial formulas (CF) or artisanal preparation (AP). Methods: This was a cross-sectional, descriptive, observational study with a quantitative and qualitative approach, conducted through semi-structured interviews by the researcher herself. Patients using HENT were evaluated for nutritional status using a 24 h dietary recall, and their quality of life was assessed using a questionnaire administered during an interview with the patient and/or caregiver. Microbial characteristics of the diets were evaluated by collecting samples and performing microbiological analyses according to standard methods. Results: 22 patients participated, mostly elderly, bedridden, and dependent, with gastrostomy as the primary method of administration (72.7%) and CF as the most commonly used (54.5%). AP consisted of cooked vegetables, legumes, milk, oil, and salt, and showed low nutritional diversity and a high risk of microbiological contamination due to manual handling. Frequent complications included diarrhea (72.7%) and mechanical complications (77.7%). Despite these issues, 91% of participants rated their quality of life as acceptable. Conclusions: HENT posed significant challenges to nutritional adequacy and microbiological safety, particularly among patients using artisanal preparations. These findings highlight the need for systematic monitoring and individualized adjustments by a multidisciplinary team, along with structured caregiver training, to optimize intake, reduce complications, and improve the quality and safety of home-based enteral therapy. Full article

This study presents the clinical trial readiness and optimization of a parallel robotic system developed for early-stage lower limb rehabilitation of bedridden patients using feedback from healthy users and clinicians. The system combines a parallel hip–knee mechanism with a Bowden cable-driven ankle module, both actuated by servomotors and controlled through a PLC platform. Experimental tests were performed in laboratory conditions with twenty healthy participants (aged 25–45) and ten clinicians, focusing on safety, ergonomics, clinical usability, and comfort through structured questionnaires. The responses were quantified and analyzed using a Mamdani-type fuzzy logic model, allowing subjective feedback to be converted into objective redesign priorities. Safety, torque capacity, and adaptability emerged as the key areas that need improvement. Subsequent mechanical and structural refinements resulted in substantial gains in user comfort, perceived safety, and clinician-reported applicability. The optimized robotic system demonstrates enhanced functionality and improved readiness for clinical evaluation, highlighting the benefit of incorporating fuzzy logic-based feedback into the development of rehabilitation robots. Full article

Today, there is a high demand for remote rehabilitation using mobile robotic complexes all over the world. They offer a wide range of options for convenient and effective therapy at home to patients and the elderly, especially those bedridden after musculoskeletal injuries. In this case, modern approaches to the development of exoskeletons for the rehabilitation of the lower extremities are especially relevant for the effective restoration of lost motor functions. Taking into account the advantages and features of robotic rehabilitation, this work is devoted to the development of a prototype exoskeleton for the ankle joint and experimental studies of the remote control module. The proposed new exoskeleton prototype design was integrated with a mobile wireless communication platform, allowing remote control of the position of the exoskeleton foot using a remote control device. As a result of functional testing, the root mean square error (RMSE) was 23.9° for dorsiflexion/plantarflexion movements and 12.8° for inversion and eversion movements, as well as an average signal transmission delay of about 100 ms and packet loss of 0.6%. These results reflect the technical feasibility of remote control at a distance of up to 10 m. The developed system is mobile, autonomous, and easy to use, which confirms its suitability as a laboratory platform for functional verification and testing of module consistency. Full article

This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We conduct an in-depth literature search and quantitative analysis of papers published in this area and focus independently of the application (drivers, office and wheelchair users, etc.). Quantitative information about the number of subjects, investigated scenarios, sensor types, machine learning usage, and laboratory vs. real-world works is extracted. In posture recognition, most works recognize at least forward, backward, left and right movements on a seat. The remaining works use the pressure sensing mat for bedridden people. In physiological parameters measurement, most works detect the heart rate and often also add respiration rate recognition. Machine learning algorithms are used in most cases and are taking on an ever-greater importance for classification and regression problems. Although all solutions use different techniques, returning satisfactory results, none of them try to detect small movements, which can pose challenges in determining the optimal sensor topology and sampling frequency required to detect fine movements. For physiological measurements, there are lots of challenges to overcome in noisy environments, notably the detection of heart rate, blood pressure, and respiratory rate at very low signal-to-noise levels. Full article

Urine pH serves as an indicator of systemic acid–base balance and helps detect early-stage urinary and renal disorders. However, conventional monitoring methods rely on instruments or manual procedures, limiting their use among vulnerable groups such as infants and bedridden elderly individuals. In this study, a pH-responsive smart hydrogel was developed and integrated into diapers to enable real-time, equipment-free, and visually interpretable urine pH monitoring. An optimised degumming process enabled one-step preparation of a silk fibroin–sericin aqueous solution. We employed a visible light-induced photo-crosslinking strategy to fabricate a dual-network hydrogel with enhanced strength and stability. Increasing sericin content accelerated gelation (≤15 min) and improved performance, achieving a maximum stress of 54 kPa, strain of 168%, and water absorption of 566%. We incorporated natural anthocyanins and fine-tuned them to produce four distinct colour changes in response to urine pH, with significantly improved colour differentiation (ΔE). Upon contact with urine, the hydrogel displays green within the normal pH range, indicating a healthy state. At the same time, a reddish-purple or blue colour serves as a visual warning of abnormal acidity or alkalinity. This intelligent hydrogel system combines rapid gelation, excellent mechanical properties, and a sensitive visual response, offering a promising platform for body fluid monitoring. Full article

An 89-year-old male developed a persistent high fever (around 39 °C) approximately two weeks following endoscopic reduction of sigmoid volvulus. He had no history of hypercalcemia but was using diuretics and proton pump inhibitors. Renal and thyroid status were normal. He was largely bedridden and asymptomatic except for fever. Laboratory tests demonstrated elevated C-reactive protein (4.75 mg/dL), but some tumor markers (including CEA, CA19-9, and CA125), anti-nuclear antibodies, MPO-ANCA, PR3-ANCA, β-D-glucan, and interferon-gamma release assay were all negative. Urinalysis was unremarkable. Blood cultures obtained from two sets were negative. Chest–abdomen–pelvis contrast-enhanced computed tomography (CT), and echocardiography did not reveal any evident neoplastic lesions or focal sites of infection. Despite various antibiotic therapies, the patient’s spike fever persisted for nearly one month, leading to a diagnosis of fever of unknown origin (FUO). The patient experienced partial symptomatic relief with corticosteroid therapy, though mild fever continued. Two months after the volvulus onset, diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) was performed, revealing hyperintensities at the right sternoclavicular joint, leading to a diagnosis of sternoclavicular arthritis. Neck CT revealed calcification in this joint. Despite difficulty in joint fluid analysis, low infection risk and the patient’s prolonged bedridden state and advanced age led to suspicion of pseudogout. Nonsteroidal anti-inflammatory drugs relieved fever and normalized inflammatory markers. DWIBS may be a valuable tool for detecting potential focus sites in FUO. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

(1) Background: Patients bedridden due to accidental injuries, diseases, or age-related functional impairments require accelerated recovery of autonomous limb movement. A prone-position rehabilitation training device was developed to provide training intensity tailored to patients’ motor capabilities. (2) Methods: Based on principles of human prone limb motion mechanics and torque balance, this study analyzed joint torque during limb movements using optical motion capture and six-dimensional force plate data. Joint torque curves during prone-position training were simulated, and a prototype device was developed. Prototype assembly and experimental validation of device–human synergy was conducted. (3) Results: Comparative analysis of joint torques between healthy individuals and patients revealed that joint torque increases as limbs contract inward. The maximum torque for upper limb joints was approximately 3.5 Nm, while the knee joint torque reached around 40 Nm. (4) Conclusions: Prototype testing confirmed the device’s design rationality, meeting human–machine synergy and rehabilitation training intensity requirements. This study provides a reference for the design of prone-position rehabilitation training devices. Full article

Introduction: Hereditary transthyretin amyloidosis (ATTRv) is a severe, multisystemic, autosomal dominant disease with variable penetrance caused by mutations in the TTR gene generating protein misfolding and accumulation of amyloid fibrils. The diagnosis is usually challenging because ATTRv may initially manifest with nonspecific multisystemic symptoms. Conversely, an early diagnosis is needed to start timely appropriate therapy. Hence, screening models have been proposed to improve ATTRv diagnosis. In this study, we propose a genetic screening model based on predefined “red flags” followed by “cascading screening” on first-degree relatives of patients who tested positive. Materials and methods: After obtaining written informed consent, genetic testing on salivary swabs was performed in individuals who met at least two major red flags for ATTRv (age > 65 years old, progressive sensory or sensorimotor neuropathy not responsive to steroids or immunomodulant therapies, recent and unexplained weight loss associated with gastrointestinal signs and symptoms, diagnosis of cardiac amyloidosis, bilateral or relapsing carpal tunnel syndrome, unexplained autonomic dysfunction) or one major flag and two minor flags (family history of neuropathy, ambulation disorders or cardiopathy, sudden cardiac death, a bedridden, wheelchaired patient without specific diagnosis excluding upper motor neuron diseases, infections, juvenile cardiac disease, ocular disorders, lumbar spine stenosis, biceps tendon rupture). Results: In the first screening phase, 29 suspected cases (individuals meeting at least two major red flags or one major red flag and two minor red flags) underwent genetic testing. One patient (3.5%) was diagnosed with hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN), carrying the Phe64Leu mutation. Then, cascade screening allowed for early recognition of two additional individuals (two pre-symptomatic carriers) among two first-degree relatives (100%). The identified patient was a 72-year-old man who had a family history of both cardiopathy, neuropathy, and a diagnosis of juvenile cardiac disease and progressive sensorimotor neuropathy unresponsive to steroids or immunomodulant therapies. Conclusions: ATTRv is a progressive and often fatal disease that should be promptly diagnosed and treated to stop progression and reduce mortality. Systematic screening for ATTRv yielded increased recognition of the disease in our neurological clinic. A focused approach for the screening of ATTRv-PN could lead to an earlier diagnosis and identification of asymptomatic carriers, enabling timely intervention through close clinical monitoring and early treatment initiation at symptom onset. Full article

This article presents an innovative system for the automated classification of postures of bedridden people using laser technology. The proposed method leverages low-cost structured light hardware and a custom algorithm to determine the side on which a person is lying. The solution prioritizes patient privacy by avoiding the capture of visual images while maintaining high accuracy in classifying postures. The system includes an enhanced hardware design with structured infrared light that is tailored for affordability and simplicity, and it incorporates an improved algorithm capable of robust posture detection. The experimental results obtained under real-world conditions validate the system’s performance and highlight its potential for deployment in clinical and home care environments. This work expands upon a previous study presented at the 6th International Conference on Application of Optics and Photonics, offering additional insights, refined methodologies, and a broader scope of validation. Full article

The transfer of patients, especially elderly or long-term bedridden individuals, has emerged as an important problem due to the growing aging population. The advancement of transfer nursing robots provides an intelligent solution to this problem. This paper presents a Parallel Master–Slave Cross-Coupled (PMSCC) cooperative control strategy based on adaptive fuzzy controllers to address the motion control challenges of a self-developed light Sensation Transfer Nursing Robot (LSTNR). First, the working principle of the LSTNR is introduced, followed by the establishment of its motion model. Next, the robot’s velocity is designed based on PMSCC cooperative control strategies, with an adaptive fuzzy controller performing motion control. Finally, the proposed cooperative control strategy is simulated and analyzed, and the robot is tested for patient transfer. The results show that the proposed control strategy reduces the velocity cooperation error between the robot’s motors. The average velocity error of the robot is reduced by 92.69%, 92.08%, 47.35%, and 87.78%, respectively, compared to the non-cooperatively controlled robot. This significantly addresses issues such as belt slack, tightness, and patient position deformation during operation, improving the transfer efficiency and effectiveness of the LSTNR. Full article

Background: Purple urine discoloration, known as purple urine bag syndrome (PUBS), has various etiologies ranging from benign to serious conditions. We present a case of cefiderocol-induced PUBS and review the literature. Methods: A 56-year-old bedridden patient developed purplish urine discoloration three days after initiating cefiderocol treatment for severe sepsis caused by carbapenem-resistant Acinetobacter baumannii/nosocomialis isolated from an infected sacral pressure ulcer. A comprehensive literature review of PubMed and Google Scholar was performed, with articles screened by two independent reviewers. Results: Our patient’s urine color cleared one day after cefiderocol discontinuation. Eight additional cases of cefiderocol-induced PUBS were identified in the literature. In all reported cases, urine discoloration resolved spontaneously within 1 to 3 days of cefiderocol cessation. Conclusions: Cefiderocol-induced PUBS is being increasingly recognized. While generally benign and self-limiting, it is crucial to exclude other potentially life-threatening diagnoses before attributing PUBS to cefiderocol. Full article

Class imbalance is a prevalent challenge in machine learning that arises from skewed data distributions in one class over another, causing models to prioritize the majority class and underperform on the minority classes. This bias can significantly undermine accurate predictions in real-world scenarios, highlighting the importance of the robust handling of imbalanced data for dependable results. This study examines one such scenario of real-time monitoring systems for fall risk assessment in bedridden patients where class imbalance may compromise the effectiveness of machine learning. It compares the effectiveness of two resampling techniques, the Synthetic Minority Oversampling Technique (SMOTE) and SMOTE combined with Edited Nearest Neighbors (SMOTEENN), in mitigating class imbalance and improving predictive performance. Using a controlled sampling strategy across various instance levels, the performance of both methods in conjunction with decision tree regression, gradient boosting regression, and Bayesian regression models was evaluated. The results indicate that SMOTEENN consistently outperforms SMOTE in terms of accuracy and mean squared error across all sample sizes and models. SMOTEENN also demonstrates healthier learning curves, suggesting improved generalization capabilities, particularly for a sampling strategy with a given number of instances. Furthermore, cross-validation analysis reveals that SMOTEENN achieves higher mean accuracy and lower standard deviation compared to SMOTE, indicating more stable and reliable performance. These findings suggest that SMOTEENN is a more effective technique for handling class imbalance, potentially contributing to the development of more accurate and generalizable predictive models in various applications. Full article

Stroke, amyotrophic lateral sclerosis (ALS), and Parkinson’s disease are some of the conditions that can lead to neuromotor disabilities requiring rehabilitation. To address the socio-economic burden that is amplified by the rapidly increasing elderly population, traditional rehabilitation techniques have recently been complemented by technological advancements, particularly Robot-Assisted Therapy (RAT). RAT enhances motor learning by improving both accuracy and consistency. This study proposes an innovative rehabilitation system that combines serious gaming and augmented reality (AR) with the LegUp parallel robot, developed for the spatial rehabilitation of the hip, knee, and ankle in bed-ridden patients. The system aims to improve patient outcomes and actively involve patients in their therapy. Electro-goniometers and a HoloLens 2 device are used to provide immediate feedback about the position of the patient’s joints, forming the basis of an interactive game in which the patient moves their leg to reach various targets. Two game modes were developed, each targeting different aspects of neuromotor rehabilitation, such as coordination, strength, and flexibility. Preliminary findings suggest that combining RAT with augmented reality-based serious gaming can increase patient motivation and engagement. Furthermore, the personalized and interactive nature of the therapy holds the potential to improve rehabilitation outcomes by fostering sustained engagement and effort. Full article

Osteoporosis is the most prevalent metabolic bone disease, especially when aggravated by aging and long-term bed rest of various causes and also when coupled with astronauts’ longer missions in space. Research on the use of static magnetic fields (SMFs) has been progressing as a noninvasive method for osteoporosis due to the complexity of the disease, the inconsistency of the effects of SMFs, and the ambiguity of the mechanism. This paper studied the effects of mice subjected to hindlimb unloading (UL, HLU) and reloading by the 0.2 T–0.4 T static magnetic field (MMF). Primary bone marrow mesenchymal stem cells (BMSCs) were extracted to explore the mechanism. Eight-week-old male C57BL/6 mice were used as an osteoporosis model by HLU for four weeks. The HLU recovery period (reloading, RL) was carried out on all FVEs and recovered in the geomagnetic field (45–64 μT, GMF) and MMF, respectively, for 12 h/d for another 4 weeks. The tibia and femur of mice were taken; also, the primary BMSCs were extracted. MMF promoted the recovery of mechanical properties after HLU, increased the number of osteoblasts, and decreased the number of adipocytes in the bone marrow. MMF decreased the total iron content and promoted the total calcium content in the tibia. In vitro experiments showed that MMF promoted the osteogenic differentiation of BMSCs and inhibited adipogenic differentiation, which is related to iron metabolism, the Wnt/β-catenin pathway, and the PPARγ pathway. MMF accelerated the improvement in bone metabolism and iron metabolism in RL mice to a certain extent, which improved the bone quality of mice. MMF mainly promoted osteogenic differentiation and reduced the adipogenic differentiation of BMSCs, which provides a reliable research direction and transformation basis for the osteoporosis of elderly, bedridden patients and astronauts. Full article