Cláudia Quaresma

A Mechanical Instrument to Evaluate Posture of the Spinal Column in Pregnant Women

Back pain is a very important problem in modern society. To better understand this problem we need instruments that evaluate the spinal column in a global way.

Optimization of sitting posture classification based on user identification

In a precursory work, an intelligent sensing chair prototype was developed to classify 12 standardized sitting postures using 8 pneumatic bladders (4 in the chairs seat and 4 in the backrest) connected to piezoelectric sensors to measure inner pressure. A Classification of around 80% was obtained using Neural Networks. This work aims to demonstrate how algorithmic optimization can be applied to a newly developed prototype to improve posture classification performance. The aforementioned optimization is based on the split of users by sex and use two different previously trained Neural Networks (one for Male and the other for Female). Results showed that the best neural network parameters had an overall classification 89.0% (from the 92.1% for Female Classification and 85.8% for Male, which translates into an overall optimization of around 8%). Automatic separation of these sets was achieved with Decision Trees with an overall classification optimization of 87.1%.

Occupational participation and institutionalized elderly people

ABSTRACT To identify the occupational participation pattern of institutionalized elderly people in Portugal, a non-experimental investigation with a descriptive correlational and inferential basis was undertaken. The 42 participants, 21 from the Associação de Bem Estar Social dos Reformados e Idosos de Canhestros and 21 from the Santa Casa da Misericórdia de Ferreira do Alentejo in Portugal were assessed using the Occupational Questionnaire. From the total sample, 73.8% (n = 31) were female and 26.2% (n = 11) were male, in the age range 68 and 94 years. Data analysis was carried out using SPSS® v. 24 software. It was found that the participants perform a smaller number of occupations during the night, mostly related to sleeping preparation. From their perspective, resting occupations represent 46.8% of the day. They attributed higher feelings of self-efficacy and satisfaction to the occupations performed in the afternoon. There were no statistically significant differences in the average scores for the independent variables schooling or gender, with the exception of Average Degree of Satisfaction, which showed that women were more satisfied. The dependent variables positively influenced each other, that is, as one dependent variable increased the other dependent variables also increased.

Optimization of Sitting Posture Classification based on Anthropometric Data

An intelligent chair prototype was developed in order to detect and correct the adoption of bad sitting postures during long periods of time. A pneumatic system was enclosed in the chair (4 air bladders inside the seat pad and 4 in the backrest) to classify 12 standardized sitting postures, with a classification score of 80.9%. Recently we used algorithmic optimization applied to the existing classification algorithm (based on Neural Networks) to split users (using Classification Trees) by their sex and used two different previously trained Neural Networks (Male and Female) to get an improved classification of 89.0% when the user was identified and 87.1% for unidentified users. In this work we aim to investigate the usage of the anthropometric information (height and weight) to further optimize our classification process. Here we use four Machine Learning Techniques (Neural Networks, Support Vector Machines, Classification Trees and Naive Bayes) to automatically split the users in 2 classes (above and below the specific anthropometric median value). Results showed that Classification Trees worked best on automatically separating the body characteristics (i.e. Height) with a global optimization of 88.3%. During the classification process, if the user is identified, we skip the splitting step, and this optimization increases to 90.2%.

Real-Time Fuzzy Monitoring of Sitting Posture: Development of a New Prototype and a New Posture Classification Algorithm to Detect Postural Transitions

In a previous work, a chair prototype was used to detect 11 standardized siting postures of users, using just 8 air bladders (4 in the chair’s seat and 4 in the backrest) and one pressure sensor for each bladder. In this paper we describe the development of a new prototype, which is able to classify 12 standard postures with an overall score of 80.9 % (using a Neural Network Algorithm). We tested how this Algorithm worked during postural transitions (frontal and lateral flexion) and in intermediate postures, identifying some limitation of this Algorithm. This prompted the development of a Posture Classification Algorithm based on Fuzzy Logic and is able to determine if the user is adopting a good or a bad posture for specific time periods, using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing Neural Network Algorithm. This newly developed Classification Algorithms is advancing the development of new Posture Correction Algorithms based on Fuzzy Actuators.

Development of Vertebral Metrics – An Instrument to Analyse the Spinal Column

In order to outline prevention and intervention strategies in the PublicHealth area it is crucial to determine the quantitative characteristics of biomechanical changes in the spinal column. Most researchers links rachialgiae aetiology to these biomechanical modifications, but these studies are limited due to the invasive nature of the technics available for quantification of the spinal column anatomic parameters. . For this reasons it is important to develop non-invasive methodologies that can be applied to general population. Most of existing non-invansive techniques have the handicap of not providing an overall view of all biomechanical parameters of the spine.

Intelligent Chair Sensor

In order to build an intelligent chair capable of posture detection and correction we developed a prototype that gathers the pressure map of the chair’s seat pad and backrest and classifies the user posture and changes its conformation. We gathered the pressure maps for eleven standardized postures in order to perform the automatic posture classification, using neural networks. First we tried to find the best parameters for the neural network classification of our data, obtaining an overall classification of around 80% for eleven postures. Those neural networks were exported to a mobile application in order to do real-time classification of those postures. Results showed a real-time classification of around 70% for eleven standardized postures, but we improved the overall classification score to 93.4% when we reduced the posture identification to eight postures, even when this classification was done with unfamiliar users to the posture identification system.

An Integrated System Combining Virtual Reality with a Glove with Biosensors for Neuropathic Pain: A Concept Validation

Spinal cord injuries are among the most traumatic situations, having relevant repercussions on an individual’s occupation performance. Although loss of functionality is considered to be the most significant consequence, neuropathic pain can determine an individual’s inability to return to daily activities. Therefore, it is imperative to develop new technologies with significant impact on the rehabilitation process of the spinal cord injuries. VR4NeuroPain combines virtual reality with a glove “GNeuroPathy”, covered with a variety of biosensors, that allows for the collection of physiological parameters and motor stimulation. The main purpose of this paper is to describe the system VR4NeuroPain, and to validate the “GNeuroPathy” concept. With that in mind, and after calibrating the VR4NeuroPain system using with a group of 16 individuals, the validation results showed that “GNeuroPathy” was comfort, accessibility in place and the collection of physiological parameters was performed as expected.

Low Cost Inertial Measurement Unit for Motion Capture in Biomedical Applications

A low-cost inertial measurement unit has been developed for accurate motion capture, allowing real-time spatial position registration (linear and angular) of the user’s whole-body. For this, we implemented a dedicated circuit for 9 degrees of freedom motion sensors, composed of an accelerometer, gyroscope and a magnetometer. We also applied signal processing and data fusion algorithms to prevent the inherent drift of the position signal. This drift is known to exist during the sensor integration process and the implemented algorithms showed promising results. This system is meant to be used in two specific biomedical applications. The first one is linked to the development of a low-cost system for gait analysis of the whole-body, which can be used in home-based rehabilitation systems. The second application is related to the real-time analysis of working postures and the identification of ergonomic risk factors for musculoskeletal disorders.

Development of an Equipment to Detect and Quantify Muscular Spasticity: Spastimed – A New Solution

iii RESUMO v FIGURE INDEX xi TABLE INDEX xiii SYMBOLS AND NOTATIONS xv INTRODUCTION 1 1. THEORETICAL STUDY AND STATE OF THE ART 3 1.1 Spasticity 3 1.1.1 Clinic and etymologic definition 3 1.1.2 Epidemiology and impact 3 1.1.3 Treatments 4 1.2 Spasticity assessment methods 5 1.2.1 Clinical Scales 5 1.2.2 Electrophysiological measurements 7 1.2.3 Biomechanical measurements 7 1.2.3.1 Spasticity Measurement System 9 1.2.3.2 On-line spasticity measurement system 9 1.2.3.3 Portable system with force transducer 10 1.2.3.4 Portable Spasticity Assessment Device 10 1.2.3.5 MyotonometerTM 11 1.2.3.6 Isokinetic Dynamometers (Biodex multi-joint SystemTM and Kincom 500H) 11 1.2.3.7 Instrument developed by Ines Clemente – “The Glove” 13 1.3 Project motivation and contextualisation 14