Range of motion measurements based on depth camera for clinical rehabilitation

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaIn clinical rehabilitation, biofeedback increases the patient’s motivation which makes it one of the most effective motor rehabilitation mechanisms. In this field it is very helpful for the patient and even for the therapist to know the level of success and performance of the training process. The human motion tracking study can provide relevant information for this purpose. Existing lab-based Three-Dimensional (3D) motion capture systems are capable to provide this information in real-time. However, these systems still present some limitations when used in rehabilitation processes involving biofeedback. A new depth camera - the Microsoft KinectTM - was recently developed overcoming the limitations associated with the lab-based movement analysis systems. This depth camera is easy to use, inexpensive and portable. The aim of this work is to introduce a system in clinical practice to do Range of Motion(ROM) measurements, using the KinectTM sensor and providing real-time biofeedback. For this purpose, the ROM measurements were computed using the joints spatial coordinates provided by the official Microsoft KinectTM Software Development Kit (SDK)and also using our own developed algorithm. The obtained results were compared with a triaxial accelerometer data, used as reference. The upper movements studied were abduction, flexion/extension and internal/external rotation with the arm at 90 degrees of elevation. With our algorithm the Mean Error (ME) was less than 1.5 degrees for all movements. Only in abduction the KinectTM Sketelon Tracking obtained comparable data. In other movements the ME increased an order of magnitude. Given the potential benefits, our method can be a useful tool for ROM measurements in clinics

UNCERTAINTY IN MACHINE LEARNING A SAFETY PERSPECTIVE ON BIOMEDICAL APPLICATIONS

Uncertainty is an inevitable and essential aspect of the worldwe live in and a fundamental aspect of human decision-making. It is no different in the realm of machine learning. Just as humans seek out additional information and perspectives when faced with uncertainty, machine learning models must also be able to account for and quantify the uncertainty in their predictions. However, the uncertainty quantification in machine learning models is often neglected. By acknowledging and incorporating uncertainty quantification into machine learning models, we can build more reliable and trustworthy systems that are better equipped to handle the complexity of the world and support clinical decisionmaking. This thesis addresses the broad issue of uncertainty quantification in machine learning, covering the development and adaptation of uncertainty quantification methods, their integration in the machine learning development pipeline, and their practical application in clinical decision-making. Original contributions include the development of methods to support practitioners in developing more robust and interpretable models, which account for different sources of uncertainty across the core components of the machine learning pipeline, encompassing data, the machine learning model, and its outputs. Moreover, these machine learning models are designed with abstaining capabilities, enabling them to accept or reject predictions based on the level of uncertainty present. This emphasizes the importance of using classification with rejection option in clinical decision support systems. The effectiveness of the proposed methods was evaluated across databases with physiological signals from medical diagnosis and human activity recognition. The results support that uncertainty quantification was important for more reliable and robust model predictions. By addressing these topics, this thesis aims to improve the reliability and trustworthiness of machine learning models and contribute to fostering the adoption of machineassisted clinical decision-making. The ultimate goal is to enhance the trust and accuracy of models’ predictions and increase transparency and interpretability, ultimately leading to better decision-making across a range of applications.A incerteza é um aspeto inevitável e essencial do mundo em que vivemos e um aspeto fundamental na tomada de decisão humana. Não é diferente no âmbito da aprendizagem automática. Assim como os seres humanos, quando confrontados com um determinado nível de incerteza exploram novas abordagens ou procuram recolher mais informação, também os modelos de aprendizagem automática devem ter a capacidade de ter em conta e quantificar o grau de incerteza nas suas previsões. No entanto, a quantificação da incerteza nos modelos de aprendizagem automática é frequentemente negligenciada. O reconhecimento e incorporação da quantificação de incerteza nos modelos de aprendizagem automática, irá permitir construir sistemas mais fiáveis, melhor preparados para apoiar a tomada de decisão clinica em situações complexas e com maior nível de confiança. Esta tese aborda a ampla questão da quantificação de incerteza na aprendizagem automática, incluindo o desenvolvimento e adaptação de métodos de quantificação de incerteza, a sua integração no pipeline de desenvolvimento de modelos de aprendizagem automática e a sua aplicação prática na tomada de decisão clínica. Nos contributos originais, inclui-se o desenvolvimento de métodos para apoiar os profissionais de desenvolvimento na criação de modelos mais robustos e interpretáveis, que tenham em consideração as diferentes fontes de incerteza nos diversos componenteschave do pipeline de aprendizagem automática: os dados, o modelo de aprendizagem automática e os seus resultados. Adicionalmente, os modelos de aprendizagem automática são construídos com a capacidade de se abster, o que permite aceitar ou rejeitar uma previsão com base no nível de incerteza presente, o que realça a importância da utilização de modelos de classificação com a opção de rejeição em sistemas de apoio à decisão clínica. A eficácia dos métodos propostos foi avaliada em bases de dados contendo sinais fisiológicos provenientes de diagnósticos médicos e reconhecimento de atividades humanas. As conclusões sustentam a importância da quantificação da incerteza nos modelos de aprendizagem automática para obter previsões mais fiáveis e robustas. Desenvolvendo estes tópicos, esta tese pretende aumentar a fiabilidade e credibilidade dos modelos de aprendizagem automática, promovendo a utilização e desenvolvimento dos sistemas de apoio à decisão clínica. O objetivo final é aumentar o grau de confiança e a fiabilidade das previsões dos modelos, bem como, aumentar a transparência e interpretabilidade, proporcionando uma melhor tomada de decisão numa variedade de aplicações

Uncertainty-Based Rejection in Machine Learning: Implications for Model Development and Interpretability

POCI-01-0247-FEDER-033479Uncertainty is present in every single prediction of Machine Learning (ML) models. Uncertainty Quantification (UQ) is arguably relevant, in particular for safety-critical applications. Prior research focused on the development of methods to quantify uncertainty; however, less attention has been given to how to leverage the knowledge of uncertainty in the process of model development. This work focused on applying UQ into practice, closing the gap of its utility in the ML pipeline and giving insights into how UQ is used to improve model development and its interpretability. We identified three main research questions: (1) How can UQ contribute to choosing the most suitable model for a given classification task? (2) Can UQ be used to combine different models in a principled manner? (3) Can visualization techniques improve UQ’s interpretability? These questions are answered by applying several methods to quantify uncertainty in both a simulated dataset and a real-world dataset of Human Activity Recognition (HAR). Our results showed that uncertainty quantification can increase model robustness and interpretability.publishersversionpublishe

A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography

Funding Information: This work was supported by national funds from FCT Foundation for Science and Technology, Portugal , I.P. through the protect HomeSenseALS: Home-based monitoring of functional disability in amyotrophic lateral sclerosis with mobile sensing with reference PTDC/MEC-NEU/6855/2020 and research unit UIDB/FIS/04559/2020 (LIBPhys-UNL). Publisher Copyright: © 2022Amyotrophic Lateral Sclerosis (ALS) is a fast-progressing disease with no cure. Nowadays, needle electromyography (nEMG) is the standard practice for electrodiagnosis of ALS. Surface electromyography (sEMG) is emerging as a more practical and less painful alternative to nEMG but still has analytical and technical challenges. The objective of this work was to study the feasibility of using a set of morphological features extracted from sEMG to support a machine learning pipeline for ALS diagnosis. We developed a novel feature set to characterize sEMG based on quantitative measurements to surface representation of Motor Unit Action Potentials. We conducted several experiments to study the relevance of the proposed feature set either individually or combined with conventional feature sets from temporal, statistical, spectral, and fractal domains. We validated the proposed machine learning pipeline on a dataset with sEMG upper limb muscle data from 17 ALS patients and 24 control subjects. The results support the utility of the proposed feature set, achieving an F1 score of (81.9 ± 5.7) for the onset classification approach and (83.6 ± 6.9) for the subject classification approach, solely relying on features extracted from the proposed feature set in the right first dorsal interosseous muscle. We concluded that introducing the proposed feature set is relevant for automated ALS diagnosis since it increased the classifier performance during our experiments. The proposed feature set might also help design more interpretable classifiers as the features give additional information related to the nature of the disease, being inspired by the clinical interpretation of sEMG.publishersversionpublishe

Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.publishe

An infrastructure-free magnetic-based indoor positioning system with deep learning

POCI-01-0247-FEDER-033479Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.publishersversionpublishe

TSFEL: Time Series Feature Extraction Library

POCI-01-0247-FEDER-038436Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. Quite often, this process ends being a time consuming and complex task as data scientists must consider a combination between a multitude of domain knowledge factors and coding implementation. We present in this paper a Python package entitled Time Series Feature Extraction Library (TSFEL), which computes over 60 different features extracted across temporal, statistical and spectral domains. User customisation is achieved using either an online interface or a conventional Python package for more flexibility and integration into real deployment scenarios. TSFEL is designed to support the process of fast exploratory data analysis and feature extraction on time series with computational cost evaluation.publishersversionpublishe

Fingerprints and Floor Plans Construction for Indoor Localisation Based on Crowdsourcing

The demand for easily deployable indoor localisation solutions has been growing. Although several systems have been proposed, their limitations regarding the high implementation costs hinder most of them to be widely used. Fingerprinting-based IPS (Indoor Positioning Systems) depend on characteristics pervasively available in buildings. However, such systems require indoor floor plans, which might not be available, as well as environmental fingerprints, that need to be collected through human resources intensive processes. To overcome these limitations, this paper proposes an algorithm for the automatic construction of indoor maps and fingerprints, solely depending on non-annotated crowdsourced data from smartphones. Our system relies on multiple gait-model based filtering techniques for accurate movement quantification in combination with opportunistic sensing observations. After the reconstruction of users’ movement with PDR (Pedestrian Dead Reckoning) techniques, Wi-Fi measurements are clustered to partition the trajectories into segments. Similar segments, which belong to the same cluster, are identified using an adaptive approach based on a geomagnetic field distance. Finally, the floor plans are obtained through a data fusion process. Merging the acquired environmental data using the obtained floor plan, fingerprints are aligned to physical locations. Experimental results show that the proposed solution achieved comparable floor plans and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free IPS

Time Alignment Measurement for Time Series

Sem PDF conforme despacho. This work was supported by North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM) [NORTE-01-0145-FEDER-000026].When a comparison between time series is required, measurement functions provide meaningful scores to characterize similarity between sequences. Quite often, time series appear warped in time, i.e, although they may exhibit amplitude and shape similarity, they appear dephased in time. The most common algorithm to overcome this challenge is the Dynamic Time Warping, which aligns each sequence prior establishing distance measurements. However, Dynamic Time Warping takes only into account amplitude similarity. A distance which characterizes the degree of time warping between two sequences can deliver new insights for applications where the timing factor is essential, such well-defined movements during sports or rehabilitation exercises. We propose a novel measurement called Time Alignment Measurement, which delivers similarity information on the temporal domain. We demonstrate the potential of our approach in measuring performance of time series alignment methodologies and in the characterization of synthetic and real time series data acquired during human movement.publishersversionpublishe