Identification of diseases based on the use of inertial sensors: a systematic review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer for the automatic recognition of different diseases, and it may powerful the different treatments with the use of less invasive and painful techniques for patients. This paper is focused in the systematic review of the studies available in the literature for the automatic recognition of different diseases with accelerometer sensors. The disease that is the most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implements for the recognition of Parkinson’s disease reported an accuracy of 94%. Other diseases are recognized in less number that will be subject of further analysis in the future.info:eu-repo/semantics/publishedVersio

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Signal processing for the measurement of the results of the timed-up and go test using sensors

Dissertação de Mestrado apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interativos.Os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis tem permitido o surgimento de vários estudos em diferentes áreas da vida humana. Estes dispositivos estão equipados com diversos sensores que permitem adquirir diferentes parâmetros físicos e fisiológicos de diferentes indivíduos. Os dispositivos móveis apresentam-se com cada vez mais soluções, funcionalidades e capacidade de processamento. A presença de sensores nos dispositivos móveis, como o acelerómetro, magnetómetro e giroscópio, permite a aquisição de sinais relacionados com atividade física e movimento do ser humano. Em acréscimo, dado que estes dispositivos incluem possibilidade de ligação via Bluetooth, outros sensores podem ser utilizados em conjunto com os sensores incluídos no dispositivo móvel. O desenvolvimento deste tipo de sistemas inteligentes com sensores é um dos temas abordados no desenvolvimento de sistemas de Ambient Assisted Living (AAL). Diversas áreas da medicina têm beneficiado com estes avanços, proporcionando cuidados de saúde à distância, mas o foco desta dissertação é um dos testes funcionais focados na fisioterapia, o Timed-Up and Go test. O Timed-Up and Go test define-se como um teste muito utilizado por fisioterapeutas na recuperação de lesões e é constituído por seis fases, onde o individuo se encontra sentado numa cadeira, levanta-se, caminha três metros, inverte a marcha, caminha três metros e volta a sentar-se na cadeira. O âmbito desta dissertação consiste na análise estatística e com inteligência artificial dos dados recolhidos durante a execução do Timed-Up and Go test com recurso a diversos sensores, sendo que para isso foi desenvolvida uma aplicação móvel que permite adquirir os dados de diversos sensores durante a execução do teste com pessoas idosas institucionalizadas. A dissertação foca-se na criação de um método de análise dos resultados do Timed-Up and Go test com recurso ao acelerómetro e magnetómetro do dispositivo móvel e um sensor de pressão, ligado a um dispositivo BITalino, posicionado na cadeira. Ao mesmo tempo, foram recolhidos sinais de sensores de Eletrocardiografia e Eletroencefalografia, conectados a outro dispositivo BITalino, para análise de diferentes problemas de saúde. Assim, implementaram-se métodos estatísticos e de inteligência artificial para a análise dos dados recolhidos a partir destes sensores com recurso ao procedimento experimental inicialmente executado. Inicialmente, foi realizada a revisão da literatura relacionada com o Timed- Up and Go test e o uso de sensores, sendo que a revisão de literatura terminou com a identificação das doenças passíveis de serem identificadas com recurso aos sensores inerciais. Seguidamente, apresentou-se a proposta de arquitetura a ser utilizada para a recolha dos dados, tendo em conta os sensores anteriormente referidos. Os dados presentes neste estudo foram recolhidos de 40 idosos institucionalizados da região do Fundão (Portugal), equipados com um dispositivo móvel e um dispositivo BITalino, bem como os restantes sensores. Por fim, passou-se então à análise dos dados recolhidos que foi dividida em 3 estágios, começando pela análise do acelerómetro, magnetómetro e sensor de pressão para identificação dos parâmetros do Timed-Up and Go test, utilizando métodos estatísticos para a análise dos dados recolhidos. No segundo estágio foram implementados métodos estatísticos para correlacionar as doenças passiveis de serem detetadas por sensores de Eletrocardiografia e Eletroencefalografia. Por fim, no terceiro estágio foram implementados métodos de inteligência artificial, i.e., redes neuronais artificiais, para relacionar as doenças do foro cardíaco e nervoso com os dados dos diferentes indivíduos de modo a aferir as suas características. Como trabalho futuro, os resultados apresentados nesta dissertação podem servir para a criação de sistemas de baixo-custo, e de acesso a todos os cidadãos, que permitam a deteção mais atempada de determinados distúrbios e possam servir de auxílio aos profissionais de saúde no diagnóstico e tratamento de doenças

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

MODELING HYPERBARIC CHAMBER ENVIRONMENT AND CONTROL SYSTEM

Deep water activities are essential for many industrial fields, for instance in repairing and installation of underwater cables, pipes and constructions, marine salvage and rescue opera- tions. In some cases, these activities must be performed in deep water and hence require special equipment and prepared and experienced personnel. In some critical situations, re- motely controlled vehicles (ROVs) can't be used and a human diver intervention is required. In the last case, divers are required to perform work at high depths, which could be as low as 300m below the water surface. Usually, this is the limit depth for commercial diving and when operations must be carried out even deeper, ROVs remain only possibility to perform them. In the past, the safety regulations were less strict and numerous operations on depth of 300-350 meters of seawater were conducted. However, in the beginning of the 90s gov- ernments and companies started to impose limits on depths of operation; for instance, in Norway maximum operational depth for saturation divers is limited to 180 meters of sea- water (Imbert et al., 2019). Obviously, harsh environmental conditions impose various limitations on performed activi- ties; indeed, low temperature, poor visibility and high pressure make it difficult not only to operate at depth, but even to achieve the point of intervention. One of the main problems is related to elevated pressure, which rises for about 1 bar for each 10 meters of water depth and could achieve up to 20-25 bars at required depth, while pressure inside divers\u2019 atmospheric diving suites must be nearly the same. Considering this, there are several evident limitations. First is related to the fact that at high atmospheric pressure oxy- gen becomes poisonous for human body and special breath gas mixtures are required to avoid health issues. The second one is maximum pressure variation rate which would not cause damage for the human body; indeed, fast compression or decompression could easily cause severe damages and even death of divers. Furthermore, surveys found that circa 1/3 of divers experience headache during decompression which usually last for at least several hours and up to several days (Imbert et al., 2019). The same study indicates that majority of the divers experience fatigue after saturation and it lasts on average more than 4 days before return to normal. Obviously, risk of accidents increases with high number of compression- decompression cycles. To address these issues, in commercial deep water diving the common practice is to perform pressurization only one time before the start of the work activity which typically lasts 20-30 days and consequent depressurization after its end. Hence, divers are living for several weeks in isolated pressurized environments, typically placed on board of a Dive Support Vessel (DSV), usually barge or a ship, and go up and down to the workplace using submersible decompression chamber also known as the bell. While long-term work shifts provide numerous advantages, there is still necessity to perform life support supervision of the plant, the bell and the diving suits, which require presence of well qualified personnel. Currently, most of training activities are performed on empty plant during idle time, but obviously this approach is low efficient and costly, as well as accom- panied by the risk to broke equipment. To address such issues, this research project proposes utilization of simulator of plant and its life support system, devoted to train future Life-Support Supervisors (LSS), taking into account gas dynamics, human behaviour and physiology as well as various aspect of opera- tion of saturation diving plants

How to build better fall detection technology : a search for characteristics unique to falls and methods to robustly evaluate performance

Falls can have severe consequences for older adults, such as bone fractures and long periods unable to get up from the ground, known as a long-lie. The capability to automatically detect falls would reduce long-lies through ensuring prompt arrival of assistance and would be valuable in fall risk assessment and fall prevention research. This research aimed to identify why existing wearable fall detection technology has not achieved acceptable performance and where further development should focus. There have been a plethora of attempts at fall detection; real-world testing is in an embryonic stage, nevertheless, it is clear performance has been poor. The focus has been on the testing of complete system performance, most commonly with acted falls, and it has been unclear how to improve performance. A new framework for the development of fall detection is proposed which promotes targeted investigation of how real-world performance can be improved. An improved method to quantify real-world performance is also proposed based on a systematic review of previous approaches. To prepare for the analysis of a real-world dataset, a pilot study was conducted which focused on the development and testing of posture classification algorithms. One of the world's largest datasets of real-world falls and activities of daily living was collected over 2 years in collaboration with 17 care homes across Scotland and the north of England. Twenty fall signals were extracted from 1,919 days of thigh-worn accelerometer recordings collected with 42 participants. Analysis of the data focused on falls from an upright to a sedentary (sitting or lying) posture, 16 falls met this criterion and were included in the analysis. To allow the data to be thoroughly checked for quality, the dataset was reduced to 104 days, from which 4,293 upright to sedentary transitions were extracted (including the 16 falls). This study was the first to: discern that falls may be too diverse to classify as a single group and focus on a subtype of fall, use posture transitions to select events for analysis, assess the importance of peak jerk and vertical velocity for fall detection, and investigate the occurrence of multiple impacts during falls. The results demonstrated that the core features used previously do not yield sufficient separation of the falls to allow detection without high rates of false positives. For the first time, it was shown that (1) a rapid increase in deceleration may be more indicative of a fall than the peak deceleration, and (2) multiple impacts occur frequently in falls but not other movements

A descriptive model for determining optimal human performance in systems. Volume 3 - An approach for determining the optimal role of man and allocation of functions in an aerospace system

Optimal role of man in space, allocation of men and machines in aerospace systems, and descriptive model for determining optimal human performanc

Proceedings of the 7th international conference on disability, virtual reality and associated technologies, with ArtAbilitation (ICDVRAT 2008)

The proceedings of the conferenc