Validation of Estimators for Weight-Bearing and Shoulder Joint Loads Using Instrumented Crutches

This research paper aimed to validate two methods for measuring loads during walking with instrumented crutches: one method to estimate partial weight-bearing on the lower limbs and another to estimate shoulder joint reactions. Currently, gait laboratories, instrumented with high-end measurement systems, are used to extract kinematic and kinetic data, but such facilities are expensive and not accessible to all patients. The proposed method uses instrumented crutches to measure ground reaction forces and does not require any motion capture devices or force platforms. The load on the lower limbs is estimated by subtracting the forces measured by the crutches from the subject’s total weight. Since the model does not consider inertia contribution in dynamic conditions, the estimation improves with low walking cadence when walking with the two-point contralateral and the three-point partial weight-bearing patterns considered for the validation tests. The shoulder joint reactions are estimated using linear regression, providing accurate values for the forces but less accurate torque estimates. The crutches data are acquired and processed in real-time, allowing for immediate feedback, and the system can be used outdoors in real-world walking conditions. The validation of this method could lead to better monitoring of partial weight-bearing and shoulder joint reactions, which could improve patient outcomes and reduce complications

DEEP LEARNING FOR GESTURE RECOGNITION IN GYM TRAINING PERFORMED BY A VISION-BASED AUGMENTED REALITY SMART MIRROR

This paper illustrates the development and the validation of a smart mirror for sport training. The application is based the skeletonization algorithm MediaPipe and runs on an embedded device Nvidia Jetson Nano equipped with two fisheye cameras. The software has been evaluated considering the exercise biceps curl. The elbow angle has been measured by both MediaPipe and the motion capture system BTS (ground truth), and the resulting values have been compared to determine angle uncertainty, residual errors, and intra-subject and inter-subject repeatability. The uncertainty of the joints’ estimation and the quality of the image captured by the cameras reflect on the final uncertainty of the indicator over time, highlighting the areas of improvements for further developments

Computer vision-based mapping of grapevine vigor variability for enhanced fertilization strategies through intelligent pruning estimation

The objective of this study is to develop an affordable and non-invasive method using computer vision to estimate pruning weight in commercial vineyards. The study aims to enable controlled fertilization by leveraging pruning data as an indicator of plant vigor [1]. The methodology entails the analysis of RGB and DEPTH images acquired through an embedded platform (Figure 1) in a vineyard cultivating corvina grapes using the guyot method [2]. Initially, pruning weight was evaluated by processing pictures taken manually with a controlled background. Then, we developed an algorithm to estimate pruned wood weight based on these images. Subsequently, a mobile sensor platform was utilized to automatically capture grapevine images without a controlled background. Collected data will then be used to deploy a convolutional neural network (CNN) for intelligent pruning estimation capable of extracting meaningful data from real-world environments. Additionally, we integrated and validated a visual-odometry sensor (Intel Realsense T265) to map the spatial variability of pruning estimation results

Deep learning-based hand gesture recognition for collaborative robots

This paper is a first step towards a smart hand gesture recognition set up for Collaborative Robots using a Faster R-CNN Object Detector to find the accurate position of the hands in RGB images. In this work, a gesture is defined as a combination of two hands, where one is an anchor and the other codes the command for the robot. Other spatial requirements are used to improve the performances of the model and filter out the incorrect predictions made by the detector. As a first step, we used only four gestures

Workstation meta-collaborative basate su sistemi di visione e algoritmi intelligenti

In questo contributo si presentano le caratteristiche di una workstation industriale meta-collaborativa, ideata per garantire la collaborazione uomo-robot a prescindere dalla presenza o meno di barriere fisiche tra le parti, in ottica di Industria 4.0. Per garantire ciò, il sistema proposto, realizzato in ROS, si basa sul canale di comunicazione visivo e adotta una comunicazione per mezzo di comandi gestuali. Lo sviluppo del sistema è ancora in corso, pertanto si presentano risultati parziali relativi al riconoscimento e alla traduzione del gesto, realizzato tramite l’object detector R-FCN

Neonatal hypoxic-ischemic encephalopathy after acute carbon monoxide intoxication during pregnancy. A case report and brief review of the literature

Carbon monoxide (CO) poisoning during pregnancy is a rare occurrence, associated with high maternal and fetal mortality rates. As CO can cross the placenta, leading to intrauterine hypoxia, CO intoxication can result in neurological sequelae and neurologic complications in fetuses who survive. We report a case of a preterm newborn acutely exposed to CO in-utero and delivered by emergent cesarean section at the 31st week of gestation due to the severe burns suffered by the mother following an indoor boiler explosion. As CO has serious adverse effects both on the mother and fetus, it is important to recognize and treat poisoning in a timely manner. Despite maternal blood CO levels, CO intoxication at critical stage of central nervous system development can lead to hypoxic-ischemic lesions, thus interdisciplinary care and follow up for these patients are mandatory

The Clinical Impact of Methotrexate-Induced Stroke-Like Neurotoxicity in Paediatric Departments: An Italian Multi-Centre Case-Series

IntroductionStroke-like syndrome (SLS) is a rare subacute neurological complication of intrathecal or high-dose (>= 500 mg) Methotrexate (MTX) administration. Its clinical features, evoking acute cerebral ischaemia with fluctuating course symptoms and a possible spontaneous resolution, have elicited interest among the scientific community. However, many issues are still open on the underlying pathogenesis, clinical, and therapeutic management and long-term outcome. Materials and MethodsWe retrospectively analyzed clinical, radiological and laboratory records of all patients diagnosed with SLS between 2011 and 2021 at 4 National referral centers for Pediatric Onco-Hematology. Patients with a latency period that was longer than 3 weeks between the last MTX administration of MTX and SLS onset were excluded from the analysis, as were those with unclear etiologies. We assessed symptom severity using a dedicated arbitrary scoring system. Eleven patients were included in the study. ResultsThe underlying disease was acute lymphoblastic leukemia type B in 10/11 patients, while fibroblastic osteosarcoma was present in a single subject. The median age at diagnosis was 11 years (range 4-34), and 64% of the patients were women. Symptoms occurred after a mean of 9.45 days (+/- 0.75) since the last MTX administration and lasted between 1 and 96 h. Clinical features included hemiplegia and/or cranial nerves palsy, paraesthesia, movement or speech disorders, and seizure. All patients underwent neuroimaging studies (CT and/or MRI) and EEG. The scoring system revealed an average of 4.9 points (+/- 2.3), with a median of 5 points (maximum 20 points). We detected a linear correlation between the severity of the disease and age in male patients. ConclusionsSLS is a rare, well-characterized complication of MTX administration. Despite the small sample, we have been able to confirm some of the previous findings in literature. We also identified a linear correlation between age and severity of the disease, which could improve the future clinical management

The risk stratification of adverse neonatal outcomes in women with gestational diabetes (STRONG) study

Aims: To assess the risk of adverse neonatal outcomes in women with gestational diabetes (GDM) by identifying subgroups of women at higher risk to recognize the characteristics most associated with an excess of risk. Methods: Observational, retrospective, multicenter study involving consecutive women with GDM. To identify distinct and homogeneous subgroups of women at a higher risk, the RECursive Partitioning and AMalgamation (RECPAM) method was used. Overall, 2736 pregnancies complicated by GDM were analyzed. The main outcome measure was the occurrence of adverse neonatal outcomes in pregnancies complicated by GDM. Results: Among study participants (median age 36.8 years, pre-gestational BMI 24.8 kg/m2), six miscarriages, one neonatal death, but no maternal death was recorded. The occurrence of the cumulative adverse outcome (OR 2.48, 95% CI 1.59–3.87), large for gestational age (OR 3.99, 95% CI 2.40–6.63), fetal malformation (OR 2.66, 95% CI 1.00–7.18), and respiratory distress (OR 4.33, 95% CI 1.33–14.12) was associated with previous macrosomia. Large for gestational age was also associated with obesity (OR 1.46, 95% CI 1.00–2.15). Small for gestational age was associated with first trimester glucose levels (OR 1.96, 95% CI 1.04–3.69). Neonatal hypoglycemia was associated with overweight (OR 1.52, 95% CI 1.02–2.27) and obesity (OR 1.62, 95% CI 1.04–2.51). The RECPAM analysis identified high-risk subgroups mainly characterized by high pre-pregnancy BMI (OR 1.68, 95% CI 1.21–2.33 for obese; OR 1.38 95% CI 1.03–1.87 for overweight). Conclusions: A deep investigation on the factors associated with adverse neonatal outcomes requires a risk stratification. In particular, great attention must be paid to the prevention and treatment of obesity

Measuring cognitive impairment and monitoring cognitive decline in Huntington's disease:a comparison of assessment instruments

Background Progressive cognitive decline is an inevitable feature of Huntington’s disease (HD) but specific criteria and instruments are still insufficiently developed to reliably classify patients into categories of cognitive severity and to monitor the progression of cognitive impairment. Methods We collected data from a cohort of 180 positive gene-carriers: 33 with premanifest HD and 147 with manifest HD. Using a specifically developed gold-standard for cognitive status we classified participants into those with normal cognition, those with mild cognitive impairment, and those with dementia. We administered the Parkinson’s Disease-Cognitive Rating Scale (PD-CRS), the MMSE and the UHDRS cogscore at baseline, and at 6-month and 12-month follow-up visits. Cutoff scores discriminating between the three cognitive categories were calculated for each instrument. For each cognitive group and instrument we addressed cognitive progression, sensitivity to change, and the minimally clinical important difference corresponding to conversion from one category to another. Results The PD-CRS cutoff scores for MCI and dementia showed excellent sensitivity and specificity ratios that were not achieved with the other instruments. Throughout follow-up, in all cognitive groups, PD-CRS captured the rate of conversion from one cognitive category to another and also the different patterns in terms of cognitive trajectories. Conclusion The PD-CRS is a valid and reliable instrument to capture MCI and dementia syndromes in HD. It captures the different trajectories of cognitive progression as a function of cognitive status and shows sensitivity to change in MCI and dementia