Coleção de Artigos Acadêmicos

URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227

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Agora exibindo 1 - 10 de 24

An Automated Electronic System in a Motorized Wheelchair for Telemonitoring: Mixed Methods Study Based on Internet of Things
(2023) Gradim, Luma Carolina Câmara; Santana, André Luiz Maciel; José, Marcelo Archanjo; Zuffo, Marcelo Knörich; Lopes, Roseli de Deus
Background: Wheelchair positioning systems can prevent postural deficits and pressure injuries. However, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user. Objective: This study aims to present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things (IoT), for automated positioning as part of a study on wheelchairs and telemonitoring. Methods: We conducted a mixed methods study with a user-centered design approach, interviews with 16 wheelchair users and 66 professionals for the development of system functions, and a formative assessment of 5 participants with descriptive analysis to design system concepts. Results: We presented a new wheelchair system with hardware and software components developed based on coparticipation with singular components in an IoT architecture. In an IoT solution, the incorporation of sensors from the inertial measurement unit was crucial. These sensors were vital for offering alternative methods to monitor and control the tilt and recline functions of a wheelchair. This monitoring and control could be achieved autonomously through a smartphone app. In addition, this capability addressed the requirements of real users. Conclusions: The technologies presented in this system can benefit telemonitoring and favor real feedback, allowing quality provision of health services to wheelchair users. User-centered development favored development with specific functions to meet the real demands of users. We emphasize the importance of future studies on the correlation between diagnoses and the use of the system in a real environment to help professionals in treatment.
Biomechanical comparison of three locking plate constructs for stabilization of a femoral segmental defect model in cats
(2026) Carbonari, Marcelo José; Coris, Jeniffer Gabriela Figueroa; Rahal, Sheila Canevese; Pereira, César Augusto M.; Cassanego, Guilherme Rech; Wei, Teng Hsiang; Caldeira, Fátima Maria C.
This study biomechanically evaluated three titanium locking plate constructs for stabilizing a segmental defect model that simulated a comminuted femoral shaft fracture in cats. Four groups were formed: G1 – single locking plate; G2 – plate-rod construct; G3 – dual plate in an orthogonal configuration; and G4 – intact bone. G1 used 16-hole locking plate with bicortical screws placed in holes 1, 3, 7, 10, 14, and 16. In G2, an intramedullary pin was included, with monocortical screws in the same positions. In G3, screws were placed as in G1, except that monocortical screws were used in holes 7 and 10. Additionally, an 8-hole plate was applied in orthogonal configuration, with monocortical screws placed in holes 1, 3, 6, and 8. All constructs underwent cyclic eccentric axial compression, followed by destructive testing, except G4, which was tested only destructively. During cyclic testing, stiffness did not differ between G2 and G3, while G1 exhibited lower stiffness. Deformation measured with Gauge 1 (16-hole plate) was greatest in G1, with no significant difference between G2 and G3. Deformation measured with Gauge 2 (intramedullary pin and orthogonal plate) was greater in G2 than in G3. In destructive testing, stiffness did not differ between G1 and G2, or between G2 and G3. Maximum force and displacement did not differ among Groups 1, 2 and 3. Group 4 differ from other groups for these variables. In conclusion, both the locking plate–rod construct and the dual titanium locking plate demonstrated similar stiffness, but locking plate–rod configuration exhibited greater deformation.
Audiovisual interactive artwork via web-deployed software: Motus composes Homino-idea
(2022) Amstalden, Augusto Piato; TIAGO FERNANDES TAVARES; Costa Neto, Anésio Azevedo; Camarini, Giovana Cardi
Many art installations rely on camera-based audiovisual interactions, and this commonly requires specialized hardware and software. Consequently, audiovisual installations are usually restricted to wealthier areas, in which the specialized equipment can be afforded and properly hosted. In countries with an evident income unbalance linked to location, the geographic restriction leads to an audience restriction. In this work, we present the development of a web-deployed composition tool for audiovisual interactions that runs on the client side and does not require installing any additional software. Simultaneously, it provides visual feedback that can aid the audience to understand the experience. Consequently, the tool can be used to compose audiovisual interactions that reach a large audience via web. We further explore the tool by composing the audiovisual installation Homino-idea. The installation is inspired by the interactions between humans and the environment, and can be either shown in art venues or used online.
A multi-sensor human gait dataset captured through an optical system and inertial measurement units
(2022) Santos, Geise; Wanderley, Marcelo; TIAGO FERNANDES TAVARES; Rocha, Anderson
Diferent technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, ftting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant’s legs to capture accelerometer and gyroscope data, and 42 refexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre processed in each of two sessions, performed on diferent days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specifc for each system.
Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories
(2022) Santos, Geise; TIAGO FERNANDES TAVARES; Rocha, Anderson
Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition feld. Whereas gait recognition works usually focus on improving end-to-end performance measures, this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identifcation scenario. In this setup, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and diferences between subjects in a verifcation setup. The explorations concluded that periodic components occurring in frequencies between 6 and 10 Hz are more signifcant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.
Neonatal mortality prediction with routinely collected data: a machine learning approach
(2021) ANDRE FILIPE DE MORAES BATISTA; Diniz, Carmen S. G.; Bonilha, Eliana A.; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Background: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas. Methods: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data. Results: The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO’s five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records. Conclusion: Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.
Predictors of tooth loss: A machine learning approach
(2021) Elani, Hawazin W.; ANDRE FILIPE DE MORAES BATISTA; W. Murray Thomson; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Introduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.
Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach
(2021) Nascimento, Carla Ferreira do; Hellen Geremias dos Santos; ANDRE FILIPE DE MORAES BATISTA; Lay, Alejandra Andrea Roman; Duarte, Yeda Aparecida Oliveira
Background: Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models. Methods: Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data. Results: The outcome with highest predictive performance was death by DRS (AUC−ROC = 0.89), followed by the other specific causes (AUC−ROC = 0.87), DCS (AUC−ROC = 0.67) and neoplasms (AUC−ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting. Conclusion: The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.
Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning"
(2021) Chiavegatto Filho, Alexandre; ANDRE FILIPE DE MORAES BATISTA; Santos, Hellen Geremias dos
A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil
(2021) Fernandes, Fernando Timoteo; Oliveira, Tiago Almeida de; Teixeira, Cristiane Esteves; ANDRE FILIPE DE MORAES BATISTA; Costa, Gabriel Dalla; Chiavegatto Filho, Alexandre Dias Porto
The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.

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