311 research outputs found
Variability reduction in stencil printing of solder paste for surface mount technology
Competition in stencil printing to produce excellence in the finished product
is intense. Faults in the printing process are a major source of board failure. Studies
have shown that over 63% of defects identified after reDow originated from the
solder paste printing ( A. Lotfi ,1998 ) . However. understanding these failures are a
challenging problem as the printing process has a large number of non linearly
dependent variables such as factors relating to paste (formulation. viscosity), the
environment (temperature, humidity) and machine parameter (alignment, pressure
and speed of squeegee, blade hardness etc). The process engineer is challenged to
widen the process window so that future modifications to the process, such as the
addition of a new component, can be achieved with little. if any, change in materials
or process parameters. This thesis reports the effect of temperature and humidity
variation from the manufacturing environment on the solder paste consistency and
optimization of the essential parameters of squeegee pressure, squeegee speed.
separation speed and print gap. The outcome of variation in temperature and
humidity to the solder paste viscosity were analyzed and tests were done to
determine the characteristic of the solder paste. The tests results indicate that the
temperature and humidity has an impact on the solder paste printability. thus some
attempts must be taken to control these variables. For parameter optimization. the
analysis was carried out using statistical optimization. The main aim was to combine
these parameters with three main pitch categories to produce the acceptable print
formation. The results showed that. the ideal print result requires optimum statistical
combinations of four parameters essentially related to a particular pitch. It is also
shown that there is a diversity and contrasts of the combination of the parameters for
each category of pitch. Detailed explanations as to the phenomenon are outlined in
the thesis
Gait rehabilitation monitor
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.Este trabalho apresenta uma estrutura móvel acessível, simples e não intrusiva, que permite
a monitorização e a assistência remota de pacientes durante a reabilitação da marcha, por médicos
e fisioterapeutas que monitorizam a reabilitação da marcha do paciente. O sistema inclui um
conjunto de 2 IMUs (Inertial Mesaurement Units) Shimmer3 da marca Shimmer, compatíveís com
Bluetooth, um smartphone Android para recolha, e pré-processamento de dados e armazenamento
numa base de dados local.
Algoritmos de baixa carga computacional baseados em ângulos Euler e sinais de
acelerómetros, giroscópios e magnetómetros foram desenvolvidos e utilizados para a classificação
e identificação de diversas perturbações da marcha. Estes algoritmos incluem o alinhamento e
sincronização dos dados dos sensores IMUs usando uma referência temporal comum, além de
algoritmos de detecção de passos e strides para auxiliar a segmentação dos sinais recolhidos
remotamente pelaappdestaframeworke identificar os passos da marcha extraindo as características
relevantes para treinar e testar um classificador que faça a predição de deficiências na marcha
durante as sessões de monitorização.
Um conjunto de drivers do fabricante Shimmer é usado para fazer a conexão entre a app e
o conjunto de IMUs através de Bluetooth.
A app desenvolvida permite aos utilizadores recolher dados e treinar um modelo de
classificação para identificar os tipos de marcha normais e patológicos.
O sistema fornece uma REST API disponível num servidor backend recorrendo a
bibliotecas Java e Python e a uma base de dados PostgreSQL.
O tipo de machine-learning é Supervisionado usando Extremely Randomized Trees.
Features no domínio do tempo, da frequência e do tempo-frequência foram extraídas dos sinais
recolhidos e processados para treinar o classificador.
Para testar a estrutura, um conjunto de marchas patológicas e normais foram utilizadas para
treinar um modelo e testar o classificador
Gait analysis in a box: A system based on magnetometer-free IMUs or clusters of optical markers with automatic event detection
Gait analysis based on full-body motion capture technology (MoCap) can be used in rehabilitation to aid in decision making during treatments or therapies. In order to promote the use of MoCap gait analysis based on inertial measurement units (IMUs) or optical technology, it is necessary to overcome certain limitations, such as the need for magnetically controlled environments, which affect IMU systems, or the need for additional instrumentation to detect gait events, which affects IMUs and optical systems. We present a MoCap gait analysis system called Move Human Sensors (MH), which incorporates proposals to overcome both limitations and can be configured via magnetometer-free IMUs (MH-IMU) or clusters of optical markers (MH-OPT). Using a test–retest reliability experiment with thirty-three healthy subjects (20 men and 13 women, 21.7 ± 2.9 years), we determined the reproducibility of both configurations. The assessment confirmed that the proposals performed adequately and allowed us to establish usage considerations. This study aims to enhance gait analysis in daily clinical practice
Gait pattern detection for amputated prosthetic using fuzzy algorithm
Conventional gait rehabilitation treatment does not provide quantitative and graphical information on abnormal gait kinematics, and the match of the intervention strategy to the underlying clinical presentation may be limited by clinical expertise and experience. Amputated patient with prosthetic leg suffered with gait deviation due to variety causes commonly alignment and fitting problem. Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. The work included in this project focuses on developing a system to measure the angular displacement of human joint of lower part with patients having this problem and then applying gait phase detection using intelligent algorithm. The developed prototype has three inertial measurement units (IMU) sensor to measure and quantify body gait on thigh, shank and foot. The data from specific placement sensor on body part was evaluated and process in Arduino and MATLAB via serial communication. IMU provides the orientation of two axes and from this, it determined elevated position of each joint by using well established trigonometry technique in board to generate displacement angle during walking. The data acquired from the motion tests was displayed graphically through GUI MATLAB. A fuzzy inference system (FIS) was implementing to improve precision of the detection of gait phase from obtained gait trajectories. The prototype and FIS system showed satisfactory performance and has potential to emerge as a tool in diagnosing and predicting the pace of the disease and a possible feedback system for rehabilitation of prosthetic patients
Wearables for Movement Analysis in Healthcare
Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes
Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice
This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation
Sit-to-Stand Phases Detection by Inertial Sensors
The Sit-to-Stand(STS) is defined as the transition from the sitting to standing
position. It is commonly adopted in clinical practice because musculoskeletal or
neurological degenerative disorders, as well as the natural process of ageing,
deter-mine an increased difficulty in rising up from a seated position.
This study aimed to detect the Sit To Stand phases using data from inertial sensors.
Due to the high variability of this movement, and, consequently the difficulty to define
events by thresholds, we used the machine learning. We collected data from 27
participants (13 females,24.37\ub13.32 years old). They wore 10 Inertial Sensors placed
on: trunk,back(L4-L5),left and right thigh, tibia, and ankles. The par-ticipants were
asked to stand from an height adjustable chair for 10 times. The STS exercises were
recorded separately. The starting and ending points of each phase were identified by
key events. The pre-processing included phases splitting in epochs. The features
extracted were: mean, standard deviation, RMS, Max and min, COV and first
derivative. The features were on the epochs for each sensor.
To identify the most fitting classifier, two classifier algorithms,K-nearest Neighbours(
KNN) and Support Vector Machine (SVM) were trained. From the data
recorded, four dataset were created varying the epochs duration, the number
of sensors. The validation model used to train the classifier. As validation
model, we compared the results of classifiers trained using Kfold and Leave
One Subject out (LOSO) models. The classifier performances were evaluated
by confusion matrices and the F1 scores.
The classifiers trained using LOSO technique as validation model showed
higher values of predictive accuracy than the ones trained using Kfold. The
predictive accuracy of KNN and SVM were reported below:
\u2022 KFold
\u2013 mean of overall predictive accuracy KNN: 0.75; F1 score: REST 0.86,
TRUNK LEANING 0.35,STANDING 0.60,BALANCE 0.54, SITTING 0.55
\u2013 mean of overall predictive accuracy SVM: 0.75; F1 score: REST 0.89,
TRUNK LEANING 0.48,STANDING 0.48,BALANCE 0.59, SITTING 0.62
\u2022 LOSO
\u2013 mean of overall predictive accuracy KNN: 0.93; F1 score: REST 0.96,
TRUNK LEANING 0.79,STANDING 0.89,BALANCE 0.95, SITTING 0.88
\u2013 mean of overall predictive accuracy SVM: 0.95; F1 score phases:
REST 0.98, TRUNK LEANING 0.86,STANDING 0.91,BALANCE 0.98,
SIT-TING 0.9
Mechatronic Design of a Lower Limb Exoskeleton
This chapter presents a lower limb exoskeleton mechatronic design. The design aims to be used as a walking support device focused on patients who suffer of partial lower body paralysis due to spine injuries or caused by a stroke. First, the mechanical design is presented and the results are validated through dynamical simulations performed in Autodesk Inventor and MATLAB. Second, a communication network design is proposed in order to establish a secure and fast data link between sensors, actuators, and microprocessors. Finally, patient‐exoskeleton system interaction is presented and detailed. Movement generation is performed by means of digital signal processing techniques applied to electromyography (EMG) and electrocardiography (EEG) signals. Such interaction system design is tested and evaluated in MATLAB whose results are presented and explained. A proposal of real‐time supervisory control is also presented as a part of the integration of every component of the exoskeleton
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