Explore the vast range of titles available.

Background The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture. Results Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics. Conclusions This work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.

Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of automated acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using an automated assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale-Revised bulbar score (FRS-bulb) (median [interquartile range] of FRS-bulbar scores: 11[3]). The data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver-operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for automated acoustic analyses to detect and stratify ALS.

Human–robot walking with prosthetic legs and exoskeletons, especially over complex terrains, such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments. In this study, we present a comprehensive overview of the StairNet initiative and key research to date. First, we summarize the development of our large-scale data set with over 515,000 manually labeled images. We then provide a summary and detailed comparison of the performances achieved with different algorithms (i.e., 2D and 3D CNN, hybrid CNN and LSTM, and ViT networks), training methods (i.e., supervised learning with and without temporal data, and semi-supervised learning with unlabeled images), and deployment methods (i.e., mobile and embedded computing), using the StairNet data set. Finally, we discuss the challenges and future directions. To date, our StairNet models have consistently achieved high classification accuracy (i.e., up to 98.8%) with different designs, offering trade-offs between model accuracy and size. When deployed on mobile devices with GPU and NPU accelerators, our deep learning models achieved inference speeds up to 2.8 ms. In comparison, when deployed on our custom-designed CPU-powered smart glasses, our models yielded slower inference speeds of 1.5 s, presenting a trade-off between human-centered design and performance. Overall, the results of numerous experiments presented herein provide consistent evidence that StairNet can be an effective platform to develop and study new deep learning models for visual perception of human–robot walking environments, with an emphasis on stair recognition. This research aims to support the development of next-generation vision-based control systems for robotic prosthetic legs, exoskeletons, and other mobility assistive technologies.

A bstract Background Functional electrical stimulation (FES) can be used in rehabilitation to aid or improve function in people with paralysis. In clinical settings, it is common practice to use transcutaneous electrodes to apply the electrical stimulation, since they are non-invasive, and can be easily applied and repositioned as necessary. However, the current electrode options available for transcutaneous FES are limited and can have practical disadvantages, such as the need for a wet interface with the skin for better comfort and performance. Hence, we were motivated to develop a dry stimulation electrode which could perform equivalently or better than existing commercially available options. Methods We manufactured a thin-film dry polymer nanocomposite electrode, characterized it, and tested its performance for stimulation purposes with thirteen healthy individuals. We compared its functionality in terms of stimulation-induced muscle torque and comfort level against two other types of transcutaneous electrodes: self-adhesive hydrogel and carbon rubber. Each electrode type was also tested using three different stimulators and different intensity levels of stimulation. Results We found the proposed dry polymer nanocomposite electrode to be functional for stimulation, as there was no statistically significant difference between its performance to the other standard electrodes. Namely, the proposed dry electrode had comparable muscle torque generated and comfort level as the self-adhesive hydrogel and carbon rubber electrodes. From all combinations of electrode type and stimulators tested, the dry polymer nanocomposite electrode with the MyndSearch stimulator had the most comfortable average rating. Conclusions The dry polymer nanocomposite electrode is a durable and flexible alternative to existing self-adhesive hydrogel and carbon rubber electrodes, which can be used without the addition of a wet interfacing agent (i.e., water or gel) to perform as well as the current electrodes used for stimulation purposes.

Background Despite the proliferation of clinical research that can be used to inform Clinical Practice Guidelines there remain many areas where the number and quality of research studies vary widely. Using the Canadian Clinical Practice Guideline for Moderate-to-Severe Traumatic Brain Injury (MOD-SEV TBI) as an example, there is a lack of robust research evidence, derived from randomized controlled trials, meta-analyses, and systematic reviews to inform the recommendations. Randomized controlled trials in this field often have limitations, such as smaller sample sizes and gender and racial disparities in enrollment, that reduce the level of evidence they can provide. Notably, evidence is often lacking in the priority areas identified by people with lived experience (PWLE) and guideline end-users. Methods The Canadian Clinical Practice Guideline for MOD-SEV TBI rehabilitation is a Living Guideline that implemented a robust and replicable process to mitigate these issues. This process includes: 1. Identification of Priorities by PWLE of MOD-SEV TBI and Guideline End-Users; 2. Involvement of Diverse Multidisciplinary Expert Panels, Including PWLE; 3. Compilation, Review and Evaluation of Published MOD-SEV TBI Evidence; 4. Identification of Gaps in the Published Literature; 5. Formulation of Recommendations, Rigorous Grading of Available Evidence and Formal Voting; 6. Creation of Knowledge Translation and Mobilization Tools and 7. Publication of the Updated Living Guideline. Results Since 2014–15, the Canadian TBI Living Guideline has implemented and refined this process to produce high-quality expert consensus-based recommendations and knowledge translation and mobilization tools across 21 comprehensive domains of TBI rehabilitation. There are 351 recommendations in the current version of the Canadian TBI Living Guideline; 68% of these are primarily consensus-based recommendations. Developing a comprehensive guideline in areas where research may not be present or strong ensures that the Guideline is comprehensive and addresses the priority needs of clinicians and PWLE. Conclusions The use of robust, transparent, and replicable evidence reviews and expert consensus building process produces clinical guidelines that are relevant and applicable even when empirical data are lacking or absent. This process of developing consensus-based recommendations can be used to develop guidelines in other content areas and populations facing similar challenges.

Individuals with incomplete spinal-cord injury/disease are at an increased risk of falling due to their impaired ability to maintain balance. Our research group has developed a closed-loop visual-feedback balance training (VFBT) system coupled with functional electrical stimulation (FES) for rehabilitation of standing balance (FES + VFBT system); however, clinical usage of this system is limited by the use of force plates, which are expensive and not easily accessible. This study aimed to investigate the feasibility of a more affordable and accessible sensor such as a depth camera or pressure mat in place of the force plate. Ten able-bodied participants (7 males, 3 females) performed three sets of four different standing balance exercises using the FES + VFBT system with the force plate. A depth camera and pressure mat collected centre of mass and centre of pressure data passively, respectively. The depth camera showed higher Pearson's correlation ( r  > 98) and lower root mean squared error (RMSE < 10 mm) than the pressure mat ( r  > 0.82; RMSE < 4.5 mm) when compared with the force plate overall. Stimulation based on the depth camera showed lower RMSE than that based on the pressure mat relative to the FES + VFBT system. The depth camera shows potential as a replacement sensor to the force plate for providing feedback to the FES + VFBT system.

Introduction Gait impairments in Parkinson’s disease (PD) are treated with dopaminergic medication or deep-brain stimulation (DBS), although the magnitude of the response is variable between individuals. Computer vision-based approaches have previously been evaluated for measuring the severity of parkinsonian gait in videos, but have not been evaluated for their ability to identify changes within individuals in response to treatment. This pilot study examines whether a vision-based model, trained on videos of parkinsonism, is able to detect improvement in parkinsonian gait in people with PD in response to medication and DBS use. Methods A spatial–temporal graph convolutional model was trained to predict MDS-UPDRS-gait scores in 362 videos from 14 older adults with drug-induced parkinsonism. This model was then used to predict MDS-UPDRS-gait scores on a different dataset of 42 paired videos from 13 individuals with PD, recorded while ON and OFF medication and DBS treatment during the same clinical visit. Statistical methods were used to assess whether the model was responsive to changes in gait in the ON and OFF states. Results The MDS-UPDRS-gait scores predicted by the model were lower on average (representing improved gait; p  = 0.017, Cohen’s d = 0.495) during the ON medication and DBS treatment conditions. The magnitude of the differences between ON and OFF state was significantly correlated between model predictions and clinician annotations ( p  = 0.004). The predicted scores were significantly correlated with the clinician scores (Kendall’s tau-b = 0.301, p  = 0.010), but were distributed in a smaller range as compared to the clinician scores. Conclusion A vision-based model trained on parkinsonian gait did not accurately predict MDS-UPDRS-gait scores in a different PD cohort, but detected weak, but statistically significant proportional changes in response to medication and DBS use. Large, clinically validated datasets of videos captured in many different settings and treatment conditions are required to develop accurate vision-based models of parkinsonian gait.

Background There is a growing interest in exploring industrial-grade collaborative robots (cobots) for rehabilitation. This review explores their application for motor rehabilitation of the upper and lower extremities after a stroke and spinal cord injury (SCI). The article highlights the inherent safety features of cobots, emphasizing their design advantages over custom-built or traditional rehabilitation robots in terms of potential safety and time efficiency. Methods Database searches and reference list screening were conducted to identify studies relating to the use of cobots for upper and lower extremity rehabilitation among individuals with stroke and SCI. These articles were then reviewed and summarized. Results Thirty-three studies were included in this review. The findings suggest that the use of cobots in motor rehabilitation is still in the early stages. Some of the cobots used were equipped with sensors to detect and respond to the movement of the extremities and minimize the risk of injury. This safety aspect is crucial for patients with motor impairments. Most training protocols implemented with the cobots engaged users in repetitive task-based exercises with an overall positive user experience. Thus far, these devices have been primarily evaluated in individuals with stroke and SCI that affect the lower extremities, with no study addressing upper extremity impairments. This initial focus serves as a preliminary step toward assessing their applicability for individuals with stroke and SCI. Conclusions Cobots may have the capacity to transform therapy and support healthcare professionals in delivering more personalized and effective rehabilitation. However, there is limited evidence on their use to support upper and lower extremity rehabilitation among individuals with stroke and SCI. Further research and development are needed to refine these technologies and broaden their applications in rehabilitation settings to enhance functional recovery and overall quality of life for individuals with stroke and SCI.