- Detailed record: https://infoscience.epfl.ch/record/232604?ln=en
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The aim of this study was to test the feasibility and accuracy of a smartphone application to measure the body length of children using the integrated camera and to evaluate the subsequent weight estimates. A prospective clinical trial of children aged 0–<13 years admitted to the emergency department of the University Children’s Hospital Zurich. The primary outcome was to validate the length measurement by the smartphone application «Optisizer». The secondary outcome was to correlate the virtually calculated ordinal categories based on the length measured by the app to the categories based on the real length. The third and independent outcome was the comparison of the different weight estimations by physicians, nurses, parents and the app. For all 627 children, the Bland Altman analysis showed a bias of −0.1% (95% CI −0.3–0.2%) comparing real length and length measured by the app. Ordinal categories of real length were in excellent agreement with categories virtually calculated based upon app length (kappa = 0.83, 95% CI 0.79–0.86). Children’s real weight was underestimated by physicians (−3.3, 95% CI −4.4 to −2.2%, p < 0.001), nurses (−2.6, 95% CI −3.8 to −1.5%, p < 0.001) and parents (−1.3, 95% CI −1.9 to −0.6%, p < 0.001) but overestimated by categories based upon app length (1.6, 95% CI 0.3–2.8%, p = 0.02) and categories based upon real length (2.3, 95% CI 1.1–3.5%, p < 0.001). Absolute weight differences were lowest, if estimated by the parents (5.4, 95% CI 4.9–5.9%, p < 0.001). This study showed the accuracy of length measurement of children by a smartphone application: body length determined by the smartphone application is in good agreement with the real patient length. Ordinal length categories derived from app-measured length are in excellent agreement with the ordinal length categories based upon the real patient length. The body weight estimations based upon length corresponded to known data and limitations. Precision of body weight estimations by paediatric physicians and nurses were comparable and not different to length based estimations. In this non-emergency setting, parental weight estimation was significantly better than all other means of estimation (paediatric physicians and nurses, length based estimations) in terms of precision and absolute difference.
While robots have been popular as a tool for STEM teaching, the use of robots in other learning scenarios is novel. The field of HRI has started to report on how to make effective robots usable in educational contexts. However, many chal- lenges remain. For instance, which interaction strategies aid learning, and which hamper learning? How can we deal with the current technical limitations of robots? Answering these and other questions requires a multidisciplinary effort, inclu- ding contributions from pedagogy, developmental psychology, (computational) linguistics, artificial intelligence and HRI, among others. This abstract provides a brief overview of the current state-of-the-art in social robots designed for learning and describes the aims of the Robots for Learning (R4L) workshop in bringing together a multidisciplinary audience for furthering the development of market-ready educational robots.
Event cameras offer many advantages over standard frame-based cameras, such as low latency, high temporal resolution, and a high dynamic range. They respond to pixel- level brightness changes and, therefore, provide a sparse output. However, in textured scenes with rapid motion, millions of events are generated per second. Therefore, state- of-the-art event-based algorithms either require massive parallel computation (e.g., a GPU) or depart from the event-based processing paradigm. Inspired by frame-based pre-processing techniques that reduce an image to a set of features, which are typically the input to higher-level algorithms, we propose a method to reduce an event stream to a corner event stream. Our goal is twofold: extract relevant tracking information (corners do not suffer from the aperture problem) and decrease the event rate for later processing stages. Our event-based corner detector is very efficient due to its design principle, which consists of working on the Surface of Active Events (a map with the timestamp of the lat- est event at each pixel) using only comparison operations. Our method asynchronously processes event by event with very low latency. Our implementation is capable of pro- cessing millions of events per second on a single core (less than a micro-second per event) and reduces the event rate by a factor of 10 to 20.
Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons and human-machine interfaces is a promising avenue for the restoration and substitution of lost and impaired functions for these users. In this study, we present a novel hand exoskeleton, mano, designed to assist and restore hand functions of people with motor disabilities during activities of daily living (ADL) and in neurorehabilitative scenarios. Compared to state-of-the-art devices, our system is fully wearable, portable and minimally obtrusive on the hand. The exoskeleton can actively control flexion and extension of all fingers, while allowing natural somatosensorial interactions with the environment surrounding the users. We evaluated the device from four different perspectives. A mechanical characterization, showing that the exoskeleton can cover more than 70% of healthy hand workspace and it can achieve forces at the fingertips sufficient for ADL. A functional characterization, where we showed how two users who suffered from spinal cord injuries were able to perform several ADL for the first time since their accidents. Thirdly, we evaluated the system from a neuroimaging perspective, showing that the device can elicit EEG brain patterns typical of natural hand motions. We finally exemplified the control of the hand exoskeleton within an exemplar framework, a brain-machine interface scenario, showing how motor intention can be successfully decoded for a continuous control of the device. Overall, our results showed that the device represents an ecological solution for use both in ADL and in scenarios aimed at promoting sensorimotor recovery.
Wearable soft robotic systems are enabling safer human-robot interaction and are proving to be instrumental for biomedical rehabilitation. In this manuscript, we propose a novel, modular, wearable robotic device for human (lumbar) spine assistance that is developed using vacuum driven, soft pneumatic actuators (V-SPA). The actuators can handle large, repetitive loads efficiently under compression. Computational models to capture the complex non-linear mechanical behavior of individual actuator modules and the integrated assistive device are developed using the finite element method (FEM). The models presented can predict system behavior at large values of mechanical deformations and allow for rapid design iterations. It is shown that a single actuator module can be used to obtain a variety of different motion and force profiles and yield multiple degrees of freedom (DOF) depending on the module loading conditions, resulting in high system versatility and adaptability, and efficient replication of the targeted motion range for the human spinal cord. The efficacy of the finite element model is first validated for a single module using experimental results that include free displacement and blocked-forces. These results are then extended to encompass an extensive investigation of bio-mechanical performance requirements from the module assembly for the human spine-assistive device proposed.