Assist-as-needed EMG-based control strategy for wearable powered assistive devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Robotic-based gait rehabilitation and assistance using Wearable Powered Assistive Devices (WPADs), such as orthosis and exoskeletons, has been growing in the rehabilitation area to recover and augment the motor function of neurologically impaired subjects. These WPADs should provide a personalized assistance, since physical condition and muscular fatigue modify from patient to patient. In this field, electromyography (EMG) signals have been used to control WPADs given their ability to infer the user’s motion intention. However, in cases of motor disability conditions, EMG signals present lower magnitudes when compared to EMG signals under healthy conditions. Thus, the use of WPADs managed by EMG signals may not have potential to provide the assistance that the patient requires. The main goal of this dissertation aims the development of an Assisted-As-Needed (AAN) EMG-based control strategy for a future insertion in a Smart Active Orthotic System (SmartOs). To achieve this goal, the following elements were developed and validated: (i) an EMG system to acquire muscle activity signals from the most relevant muscles during the motion of the ankle joint; (ii) machine learning-based tool for ankle joint torque estimation to serve as reference in the AAN EMG-based control strategy; and (iii) a tool for real EMG-based torque estimation using Tibialis Anterior (TA) and Gastrocnemius Lateralis (GASL) muscles and real ankle joint angles. EMG system showed satisfactory pattern correlations with a commercial system. The reference ankle joint torque was generated based on predicted reference ankle joint kinematics, walking speed information (from 1 to 4 km/h) and anthropometric data (body height from 1.51 m to 1.83 m and body mass from 52.0 kg to 83.7 kg), using five machine learning algorithms: Support Vector Regression (SVR), Random Forest (RF), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN provided the best performance, predicting the reference ankle joint torque with fitting curves ranging from 74.7 to 89.8 % and Normalized Root Mean Square Errors (NRMSEs) between 3.16 and 8.02 %. EMG-based torque estimation beneficiates of a higher number of muscles, since EMG data from TA and GASL are not enough to estimate the real ankle joint torque.A assistência e reabilitação robótica usando dispositivos de assistência ativos vestíveis (WPADs), como ortóteses e exosqueletos, tem crescido na área da reabilitação com o fim de recuperar e aumentar a função motora de sujeitos com alterações neurológicas. Estes dispositivos devem fornecer uma assistência personalizada, uma vez que a condição física e a fadiga muscular variam de paciente para paciente. Nesta área, sinais de eletromiografia (EMG) têm sido usados para controlar WPADs, dada a sua capacidade de inferir a intenção de movimento do utilizador. Contudo, em casos de deficiência motora, os sinais de EMG apresentam menor amplitude quando comparados com sinais de EMG em condições saudáveis e, portanto, o uso de WPADs geridos por sinais de EMG pode não oferecer a assistência que o paciente necessita. O principal objetivo desta dissertação visa o desenvolvimento de uma estratégia de controlo baseada em EMG capaz de fornecer assistência quando necessário, para futura integração num sistema ortótico ativo e inteligente (SmartOs). Para atingir este objetivo foram desenvolvidos e validados os seguintes elementos: (i) sistema de EMG para adquirir sinais de atividade muscular dos músculos mais relevantes no movimento da articulação do tornozelo; (ii) ferramenta de machine learning para estimação do binário da articulação do tornozelo para servir como referência na estratégia de controlo; e (iii) ferramenta de estimação do binário real do tornozelo considerando sinais de EMG dos músculos Tibialis Anterior (TA) e Gastrocnemius Lateralis (GASL) e ângulo real do tornozelo. O sistema de EMG apresentou correlações satisfatórias com um sistema comercial. O binário de referência para o tornozelo foi gerado com base no ângulo de referência da mesma articulação, velocidade de marcha (de 1 até 4 km/h) e dados antropométricos (alturas de 1.51 m até 1.83 e massas de 52.0 kg até 83.7 kg), usando cinco algoritmos de machine learning: Support Vector Machine, Random Forest, Multilayer Perceptron, Long-Short Term Memory e Convolutional Neural Network. CNN apresentou a melhor performance, prevendo binários de referência do tornozelo com um fit entre 74.7 e 89.8 % e Normalized Root Mean Square Errors (NRMSE) entre 3.16 e 8.02 %. A estimativa do torque com base em sinais de EMG requer a inclusão de um maior número de músculos, uma vez que sinais de EMG dos músculos TA e GASL não foram suficientes

Kinematics, speed, and anthropometry-based ankle joint torque estimation: a deep learning regression approach

Powered Assistive Devices (PADs) have been proposed to enable repetitive, user-oriented gait rehabilitation. They may include torque controllers that typically require reference joint torque trajectories to determine the most suitable level of assistance. However, a robust approach able to automatically estimate user-oriented reference joint torque trajectories, namely ankle torque, while considering the effects of varying walking speed, body mass, and height on the gait dynamics, is needed. This study evaluates the accuracy and generalization ability of two Deep Learning (DL) regressors (Long-Short Term Memory and Convolutional Neural Network (CNN)) to generate user-oriented reference ankle torque trajectories by innovatively customizing them according to the walking speed (ranging from 1.0 to 4.0 km/h) and users’ body height and mass (ranging from 1.51 to 1.83 m and 52.0 to 83.7 kg, respectively). Furthermore, this study hypothesizes that DL regressors can estimate joint torque without resourcing electromyography signals. CNN was the most robust algorithm (Normalized Root Mean Square Error: 0.70 ± 0.06; Spearman Correlation: 0.89 ± 0.03; Coefficient of Determination: 0.91 ± 0.03). No statistically significant differences were found in CNN accuracy (p-value > 0.05) whether electromyography signals are included as inputs or not, enabling a less obtrusive and accurate setup for torque estimation.This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant 2020.05711.BD, 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

PT-CRIS: Um miradouro sobre o universo científico nacional

Reconhecida a importância da ciência, tecnologia, inovação e do conhecimento gerado pela investigação científica, são inúmeros os sistemas de informação criados para dar resposta a necessidades de gestão e disseminação de informação em diferentes domínios. Contudo, a dispersão de informação em vários sistemas, a não adoção de normas/práticas de referência e consequentemente a replicação de informação criam dificuldades às várias entidades que gerem ou consultam informação sobre ciência e respetivos indicadores na capacidade de gestão, execução, avaliação e tomada de decisão relativa a processos de investigação. Surge assim a necessidade de criar um sistema que ofereça uma visão global do universo de ciência e tecnologia, agregando e relacionando informação de suporte à atividade científica desenvolvida em Portugal, i.e., informação sobre investigadores, organizações, programas de financiamento, projetos, resultados de investigação, instalações, equipamentos e serviços. O sistema, ao relacionar e contextualizar a informação científica atualmente dispersa em vários sistemas, permitirá transformar informação em conhecimento, aumentar a visibilidade e difusão da ciência e simplificar processos na gestão da produção científica nacional.Comunicação patrocinada pela KEEP SOLUTION

Real-time torque estimation using human and sensor data fusion for exoskeleton assistance

Robotic assistive devices have emerged as a potential complement for repeti-tive and user-centered gait rehabilitation. In this field, the development of electromyography (EMG)-based torque controls has played a crucial role in improving the user experience with robotic assistive devices. However, most existing approaches for EMG-based joint torque estimation (i) are designed for upper limbs; (ii) often do not consider the complexity of the walking mo-tion, focusing only on the stance phase; and (iii) rely on complex mathemat-ical models that result in time-consuming estimations. This study aims to address these shortcomings by evaluating the generalization ability of a Deep Learning regressor (Convolutional Neural Network (CNN)) for estimating ankle torque trajectories, in real-time. Several inputs were incorporated, namely, EMG signals from Tibialis Anterior and Gastrocnemius Lateralis, hip kinematic data in the sagittal plane (angle, angular velocity, angular acceler-ation), walking speed (from 1.5 to 2.0 km/h), user's demographic (gender and age) and anthropometric information (height and mass, ranging from 1.50 to 1.90 m and 50.0 to 90.0 kg, respectively, and shank and foot lengths). Re-sults showed that a CNN model with two convolutional layers showed the highest generalization ability (Root Mean Square Error: 23.4±8.36, Normal-ized Mean Square Error: 0.494±0.299, and Spearman Correlation 0.754±0.105). CNN model’s time-effectiveness was tested in an active ankle orthosis, being able to estimate ankle joint torques in less than 2 millisec-onds. This study contributes to a more time-effective model for real-time EMG-based torque estimation, enabling a promising advancement in EMG-based torque control for lower limb robotic assistive devices.This work was funded by the Fundação para a Ciência e Tecnologia under the scholarship reference 2020.05711.BD, under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND, with the FAIR project under grant 2022.05844.PTDC, under the national support to R&D units grant through the reference project UIDB/04436/2020 and UIDP/04436/2020, and under the scholarship reference POCI-01-0247-FEDER-039868_BI_04_2022_CMEMS

Abordando o tema equilíbrio ácido-base por meio de cordel na disciplina química analítica qualitativa.

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Os sistemas de informação geográfica no suporte a serviços móveis para o cidadão

O seguinte artigo descreve os trabalhos de investigação efectuados no âmbito do projecto USE-ME.GOV (USability-drivEn open platform for Mobile GOVernment), um projecto do 6º Programa Quadro, que tem como objectivo o fornecimento de uma plataforma aberta para a disponibilização de serviços móveis ao cidadão, podendo ser partilhada por uma rede de autarquias, instituições públicas e outros fornecedores de serviços de informação. O artigo explica como a informação geográfica e de localização dá suporte à contextualização do utilizador móvel, podendo essa informação ser cruzada e processada com outras dimensões que definem o contexto

Patient-physician discordance in assessment of adherence to inhaled controller medication: a cross-sectional analysis of two cohorts

We aimed to compare patient's and physician's ratings of inhaled medication adherence and to identify predictors of patient-physician discordance.(SFRH/BPD/115169/2016) funded by Fundação para a Ciência e Tecnologia (FCT); ERDF (European Regional Development Fund) through the operations: POCI-01-0145-FEDER-029130 ('mINSPIRERS—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases—generalisation and evaluation of gamification, peer support and advanced image processing technologies') cofunded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia).info:eu-repo/semantics/publishedVersio

Morphological and Postural changes in the foot during pregnancy and puerperium : a longitudinal study

The aim of this study is to observe the morphological and postural changes to the foot that take place during pregnancy and the puerperium. Method: In this descriptive, observational, longitudinal study, we analysed 23 pregnant women, with particular attention to morphological and postural aspects of the foot, at three time points during and after pregnancy: in weeks 9-13 of gestation, weeks 32-35 of gestation and weeks 4-6 after delivery. The parameters considered were changes in foot length, the Foot Posture Index (FPI) and the Hernández Corvo Index, which were analysed using a pedigraph and taking into account the Body Mass Index (BMI). The same procedure was conducted in each review. Results: The statistical analyses obtained for each foot did not differ significantly between the three measurement times. A pronator-type footprint was most frequently observed during the third trimester of pregnancy; it was predominantly neutral during the postpartum period. Statistically significant differences between the measurement times were obtained in the right foot for cavus vs. neutral foot type (between the first and third trimesters and also between the first trimester and the puerperium) (in both cases, p < 0.0001). Conclusions: Foot length increases in the third trimester and returns to normal in the puerperium. According to FPI findings, the third trimester of pregnancy is characterised by pronation, while the posture returns to neutrality during the postpartum period. During pregnancy, the plantar arch flattens, and this persists during the puerperium. The incidence of cavus foot increases significantly in the third trimester and in the puerperium

Quantification and tissue localization of selenium in rice (Oryza sativa l., poaceae) grains: A perspective of agronomic biofortification

grant number 101-030671In worldwide production, rice is the second-most-grown crop. It is considered a staple food for many populations and, if naturally enriched in Se, has a huge potential to reduce nutrient deficiencies in foodstuff for human consumption. This study aimed to develop an agronomic itinerary for Se biofortification of Oryza sativa L. (Poaceae) and assess potential physicochemical deviations. Trials were implemented in rice paddy field with known soil and water characteristics and two genotypes resulting from genetic breeding (OP1505 and OP1509) were selected for evaluation. Plants were sprayed at booting, anthesis and milky grain phases with two different foliar fertilizers (sodium selenate and sodium selenite) at different concentrations (25, 50, 75 and 100 g Se·ha−1). After grain harvesting, the application of selenate showed 4.9–7.1 fold increases, whereas selenite increased 5.9–8.4-fold in OP1509 and OP1505, respectively. In brown grain, it was found that in the highest treatment selenate or selenite triggered much higher Se accumulation in OP1505 relatively to OP1509, and that no relevant variation was found with selenate or selenite spraying in each genotype. Total protein increased exponentially in OP1505 genotype when selenite was applied, and higher dosage of Se also increased grain weight and total protein content. It was concluded that, through agronomic biofortification, rice grain can be enriched with Se without impairing its quality, thus highlighting its value in general for the industry and consumers with special needs.publishersversionpublishe

Productive and economic performance of feedlot young Nellore bulls fed whole oilseeds

ABSTRACT The effects of diets containing oilseeds were measured to evaluate the productive and economic parameters in the finishing of young, feedlot Nellore bulls. Twenty-four young Nellore bulls were used, with an initial body weight (BW) of 311.46±0.37 kg and 24 months of age, distributed into individual stalls ( 4 × 20 m) in a completely randomized design, totaling four treatments with six repetitions per treatment. Four diets (control, based on corn and soybean meal, and three diets containing cottonseed, soybean, and sunflower) were evaluated. Feed and orts were measured daily to calculate intake and costs. The dry matter intake of the control group was higher than soybean (10.64 kg/day), cotton (9.88 kg/day), and sunflower (9.30 kg/day) treatments, respectively. The cottonseed treatment showed the highest average neutral detergent fiber intake. There was a dietary effect of diets on average daily gain, total weight gain, and final weight. The soybean treatment showed the highest performance, total gain (232.55 kg), and final weight (544.38 kg). Oilseed intake can modify the fatty acids profile in the meat, decreasing its saturated fatty acid content. Whole soybean seed favors performance, improves feed efficiency, fatty acid profile, and fat distribution in the carcass, and can reduce production costs