paulo Leite - Profile on Academia.edu (original) (raw)

Papers by paulo Leite

Journal of Physics: Conference Series, 2019

This paper presents a simple wearable, non-intrusive affordable mobile framework that allows remo... more This paper presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), an Android smartphone and a developed app for collecting, primary processing of data and for persistence of data in a remote PostgreSQL database, which is available in a remote server and where further data processing is performed. This framework provides gait features classifier by invoking an implemented REST API available in the remote server. Low computational load algorithms based on Euler angles and filtered signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms. After segmentation of the remotely collected signals for gait strides identification relevant features were extracted to feed, train and test a classifier for prediction of gait abnormalities using supervised machine learning type and Extremely Randomized Trees method.

2017 E-Health and Bioengineering Conference (EHB), Jun 1, 2017

This work presents a simple wearable, non-intrusive affordable mobile framework that allows remot... more This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.

Archivum Immunologiae et Therapiae Experimentalis, 2014

Sustained chronic inflammation induces activation of genes involved in cellular proliferation and... more Sustained chronic inflammation induces activation of genes involved in cellular proliferation and apoptosis, thereby causing skeletal muscle degeneration. To investigate in vitro effects of isolated pentacyclic triterpenes from Eugenia punicifolia (Ep-CM) upon signaling pathways involved in the regulation of skeletal muscle cell line proliferation, and in vivo muscular tissue remodeling. C2C12 cells were seeded on eight-well plates and [ 3 H]thymidine incorporation, TUNEL assays, mitochondria viability, zymography for matrix metalloproteases (MMPs), Western blot analysis for MAPKinase signaling pathway, NFjB activation and HMGB1 production subsequently determined under basal conditions and after Ep-CM treatment. A polymer containing Ep-CM was implanted on the volar surface of gastrocnemius muscles subjected to acute injury induced by bupivacaine for local slow and gradual release of bioactive compounds, and mice killed 4 days after surgery. Ep-CM inhibited proliferation of C2C12 myoblast cell line in a dose-dependent manner, confirmed by reduction of [ 3 H]-thymidine uptake without affecting cell viability or inducing apoptosis. The cytostatic effect of Ep-CM occurred mainly via inhibition of phosphorylated extracellular signal-regulated kinase (pERK) activation and DNA synthesis, possibly inhibiting the G1 phase of the cell cycle, since Ep-CM increased pAkt and p27 kip1 but reduced Cyclin D1. Ep-CM in vitro treatment increased MMP-9 and MMP-2 activities of C2C12 myoblast cells, but reduced in vivo MMP-9 activity and acute muscular inflammation. Besides cytostatic and antiinflammatory effects, Ep-CM pentacyclic triterpenes also contributed to degradation of basement membrane components by activating mechanisms of skeletal muscle remodeling in response to local injury.

Proceedings of the International Conference on Biomedical Electronics and Devices, 2012

Current Electrocardiographic (ECG) signal acquisition methods are generally highly intrusive, as ... more Current Electrocardiographic (ECG) signal acquisition methods are generally highly intrusive, as they involve the use of pre-gelled electrodes and cabled sensors placed directly on the person, at the chest or limbs level. Moreover, systems that make use of alternative conductive materials to overcome this issue, only provide heart rate information and not the detailed signal itself. We present a comparison and evaluation of two types of dry electrodes as interface with the skin, targeting wearable and low intrusiveness applications, which enable ECG measurement without the need for any apparatus permanently fitted to the individual. In particular, our approach is targeted at ECG biometrics using signals collected at the hand or finger level. A custom differential circuit with virtual ground was also developed for enhanced usability. Our work builds upon the current stateof-the-art in sensoring devices and processing tools, and enables novel data acquisition settings through the use of dry electrodes. Experimental evaluation was performed for Ag/AgCl and Electrolycra materials, and results show that both materials exhibit adequate performance for the intended application.

In biometric recognition based on Electrocardiographic (ECG) signals, there are two main approach... more In biometric recognition based on Electrocardiographic (ECG) signals, there are two main approaches for feature extraction: fiducial and non-fiducial. Fiducial methods use points of interest within single heartbeat waveforms, obtained by segmenting the ECG signal using QRS complexes as a reference. In this paper we study several QRS detection algorithms, with the purpose of determining what is the best algorithm in the context of finger based ECG biometrics using fiducial approaches; our main focus is the real-time segmentation of ECG signals resulting on a set of single heart beats. We propose a method combining the adaptive characteristics of the algorithm by Christov, with the strategy of the widely adopted Engelse and Zeelenberg algorithm. Experimental results obtained for real-world data show that online approaches are competitive with offline versions, and represent a contribution for the realization of real-time biometric recognition.

Study and evaluation of a single differential sensor design based on electro-textile electrodes for ECG biometrics applications

In this paper we present a study and evaluation of a custom single differential sensor design for... more In this paper we present a study and evaluation of a custom single differential sensor design for ECG data acquisition, recurring to electro-textile electrodes as the interface between the sensor and the skin. Our work is focused on improving current ...

Journal of Physics: Conference Series, 2019

This paper presents a simple wearable, non-intrusive affordable mobile framework that allows remo... more This paper presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), an Android smartphone and a developed app for collecting, primary processing of data and for persistence of data in a remote PostgreSQL database, which is available in a remote server and where further data processing is performed. This framework provides gait features classifier by invoking an implemented REST API available in the remote server. Low computational load algorithms based on Euler angles and filtered signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms. After segmentation of the remotely collected signals for gait strides identification relevant features were extracted to feed, train and test a classifier for prediction of gait abnormalities using supervised machine learning type and Extremely Randomized Trees method.

2017 E-Health and Bioengineering Conference (EHB), Jun 1, 2017

This work presents a simple wearable, non-intrusive affordable mobile framework that allows remot... more This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.

Archivum Immunologiae et Therapiae Experimentalis, 2014

Sustained chronic inflammation induces activation of genes involved in cellular proliferation and... more Sustained chronic inflammation induces activation of genes involved in cellular proliferation and apoptosis, thereby causing skeletal muscle degeneration. To investigate in vitro effects of isolated pentacyclic triterpenes from Eugenia punicifolia (Ep-CM) upon signaling pathways involved in the regulation of skeletal muscle cell line proliferation, and in vivo muscular tissue remodeling. C2C12 cells were seeded on eight-well plates and [ 3 H]thymidine incorporation, TUNEL assays, mitochondria viability, zymography for matrix metalloproteases (MMPs), Western blot analysis for MAPKinase signaling pathway, NFjB activation and HMGB1 production subsequently determined under basal conditions and after Ep-CM treatment. A polymer containing Ep-CM was implanted on the volar surface of gastrocnemius muscles subjected to acute injury induced by bupivacaine for local slow and gradual release of bioactive compounds, and mice killed 4 days after surgery. Ep-CM inhibited proliferation of C2C12 myoblast cell line in a dose-dependent manner, confirmed by reduction of [ 3 H]-thymidine uptake without affecting cell viability or inducing apoptosis. The cytostatic effect of Ep-CM occurred mainly via inhibition of phosphorylated extracellular signal-regulated kinase (pERK) activation and DNA synthesis, possibly inhibiting the G1 phase of the cell cycle, since Ep-CM increased pAkt and p27 kip1 but reduced Cyclin D1. Ep-CM in vitro treatment increased MMP-9 and MMP-2 activities of C2C12 myoblast cells, but reduced in vivo MMP-9 activity and acute muscular inflammation. Besides cytostatic and antiinflammatory effects, Ep-CM pentacyclic triterpenes also contributed to degradation of basement membrane components by activating mechanisms of skeletal muscle remodeling in response to local injury.

Proceedings of the International Conference on Biomedical Electronics and Devices, 2012

Current Electrocardiographic (ECG) signal acquisition methods are generally highly intrusive, as ... more Current Electrocardiographic (ECG) signal acquisition methods are generally highly intrusive, as they involve the use of pre-gelled electrodes and cabled sensors placed directly on the person, at the chest or limbs level. Moreover, systems that make use of alternative conductive materials to overcome this issue, only provide heart rate information and not the detailed signal itself. We present a comparison and evaluation of two types of dry electrodes as interface with the skin, targeting wearable and low intrusiveness applications, which enable ECG measurement without the need for any apparatus permanently fitted to the individual. In particular, our approach is targeted at ECG biometrics using signals collected at the hand or finger level. A custom differential circuit with virtual ground was also developed for enhanced usability. Our work builds upon the current stateof-the-art in sensoring devices and processing tools, and enables novel data acquisition settings through the use of dry electrodes. Experimental evaluation was performed for Ag/AgCl and Electrolycra materials, and results show that both materials exhibit adequate performance for the intended application.

In biometric recognition based on Electrocardiographic (ECG) signals, there are two main approach... more In biometric recognition based on Electrocardiographic (ECG) signals, there are two main approaches for feature extraction: fiducial and non-fiducial. Fiducial methods use points of interest within single heartbeat waveforms, obtained by segmenting the ECG signal using QRS complexes as a reference. In this paper we study several QRS detection algorithms, with the purpose of determining what is the best algorithm in the context of finger based ECG biometrics using fiducial approaches; our main focus is the real-time segmentation of ECG signals resulting on a set of single heart beats. We propose a method combining the adaptive characteristics of the algorithm by Christov, with the strategy of the widely adopted Engelse and Zeelenberg algorithm. Experimental results obtained for real-world data show that online approaches are competitive with offline versions, and represent a contribution for the realization of real-time biometric recognition.

Study and evaluation of a single differential sensor design based on electro-textile electrodes for ECG biometrics applications

In this paper we present a study and evaluation of a custom single differential sensor design for... more In this paper we present a study and evaluation of a custom single differential sensor design for ECG data acquisition, recurring to electro-textile electrodes as the interface between the sensor and the skin. Our work is focused on improving current ...