On the Estimation of Hospital Beds Occupancy After Hip Surgery

For hospitals, the variability on demand (number of daily urgent admissions) and the variability in the Length of Stay (LoS) (bed occupancy) may affect the quality of service provided to patients and the effectiveness of the overall service. This paper studies the LoS of 238 patients who performed hip surgery in the orthopedic service of a Portuguese hospital in 2014. It uses variables available in electronic databases, such as Age, Gender, ASA classification; Surgical Apgar Score, Type of hip surgery; Weekday of the surgery; Starting hour of the surgery and Duration of surgery to predict LoS and provides a model that correctly indicate if a patient stays more than 7 days in 72.1% of the cases.- This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under grant 2020.05711.BD, under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND, and in part by the FEDER Funds through the COMPETE 2020— Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Programas de apoio institucional ao empreendedorismo: a experiência da Universidade do Minho

Em muitos países tem sido feito um esforço significativo no sentido de promover o empreendedorismo. As universidades, dado o seu potencial de conhecimento e pesquisa, têm implementado programas de incentivo ao empreendedorismo, nomeadamente com a criação de gabinetes de transferência tecnológica, incubadoras, centros de empreendedorismo ou a criação de fundos internos para estimular a aplicação de patentes, licenciamento e criação de spin-offs. A Universidade do Minho, um dos exemplos na região norte do país, tem em funcionamento um laboratório de ideias de negócio, designado por IdeaLab, que apoia a geração e o desenvolvimento de ideias de negócio de base tecnológica e/ou conhecimento intensivo

Microalgae biomass harvesting by electrocoagulation

The use microalgae biomass for the production of biofuels has received great attention in the last decades. Microalgae biofuels could be important alternative to conventional biofuels since microalgae could be produced at high rates without the need of neither arable land, potable water or competition with food. However, the high energy intensive harvesting processes are limiting the commercial production of microalgae biofuels. In this study, Electro-Coagulation (EC) was used for harvesting the freshwater microalga Chlorella vulgaris and the marine microalga Nannochloropsis sp. The results show that EC could be an alternative to the conventional harvesting processes since it is efficient and produces good quality biomass with low energy requirements

Absence of light exposure increases pathogenicity of Pseudomonas aeruginosa pneumonia-associated clinical isolates

Pseudomonas aeruginosa can alter its lifestyle in response to changes in environmental conditions. The switch to a pathogenic host-associated lifestyle can be triggered by the luminosity settings, resorting to at least one photoreceptor which senses light and regulates cellular processes. This study aimed to address how light exposure affects the dynamic and adaptability of two P. aeruginosa pneumonia-associated isolates, HB13 and HB15. A phenotypic characterization of two opposing growth conditions, constant illumination and intensity of full-spectrum light and total absence of light, was performed. Given the nature of P. aeruginosa pathogenicity, distinct fractions were characterized, and its inherent pathogenic potential screened by comparing induced morphological alterations and cytotoxicity against human pulmonary epithelial cells (A549 cell line). Growth in the dark promoted some virulence-associated traits (e.g., pigment production, LasA proteolytic activity), which, together with higher cytotoxicity of secreted fractions, supported an increased pathogenic potential in conditions that better mimic the lung microenvironment of P. aeruginosa. These preliminary findings evidenced that light exposure settings may influence the P. aeruginosa pathogenic potential, likely owing to differential production of virulence factors. Thus, this study raised awareness towards the importance in controlling light conditions during bacterial pathogenicity evaluation approaches, to more accurately interpret bacterial responses.FCT -Fundação para a Ciência e a Tecnologia(SFRH/BD/98558/2013)info:eu-repo/semantics/publishedVersio

Assist-as-needed impedance control strategy for a wearable ankle robotic orthosis

The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user's needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users' motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training.This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks

Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-body approach. We investigate a general, real-time, full-body pose estimation framework based on two RGB+D camera streams with non-overlapping views mounted on a smart walker equipment used in rehabilitation. Human keypoint estimation is performed using a two-stage neural network framework. The 2D-Stage implements a detection module that locates body keypoints in the 2D image frames. The 3D-Stage implements a regression module that lifts and relates the detected keypoints in both cameras to the 3D space relative to the walker. Model predictions are low-pass filtered to improve temporal consistency. A custom acquisition method was used to obtain a dataset, with 14 healthy subjects, used for training and evaluating the proposed framework offline, which was then deployed on the real walker equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage and 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms when deployed on the constrained hardware of the walker. We present a novel approach to patient monitoring and data-driven human-in-the-loop control in the context of smart walkers. It is able to extract a complete and compact body representation in real-time and from inexpensive sensors, serving as a common base for downstream metrics extraction solutions, and Human-Robot interaction applications. Despite promising results, more data should be collected on users with impairments, to assess its performance as a rehabilitation tool in real-world scenarios.Comment: Accepted for publication in Expert Systems with Application

Daily locomotion recognition and prediction: A kinematic data-based machine learning approach

More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several LMs (walking direction, level-ground walking, ascend and descend stairs, and ascend and descend ramps) and respective LMTs. We compared diverse state-of-the-art feature processing and dimensionality reduction methods and machine-learning classifiers to find an effective tool for recognition and prediction of LMs and LMTs. The comparison included kinematic patterns from 10 able-bodied subjects. The more accurate tools were achieved using min-max scaling [-1; 1] interval and 'mRMR plus forward selection' algorithm for feature normalization and dimensionality reduction, respectively, and Gaussian support vector machine classifier. The developed tool was accurate in the recognition (accuracy >99% and >96%) and prediction (accuracy >99% and >93%) of daily LMs and LMTs, respectively, using exclusively kinematic data. The use of kinematic data yielded an effective recognition and prediction tool, predicting the LMs and LMTs one-step-ahead. This timely prediction is relevant for assistive devices providing personalized assistance in daily scenarios. The kinematic data-based machine learning tool innovatively addresses several LMs and LMTs while allowing the user to self-select the leading limb to perform LMTs, ensuring a natural gait.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941