IMUs: validation, gait analysis and system’s implementation

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a prevalent problem in actual society. The number of falls has been increasing greatly in the last fifteen years. Some falls result in injuries and the cost associated with their treatment is high. However, this is a complex problem that requires several steps in order to be tackled. Namely, it is crucial to develop strategies that recognize the mode of locomotion, indicating the state of the subject in various situations, namely normal gait, step before fall (pre-fall) and fall situation. Thus, this thesis aims to develop a strategy capable of identifying these situations based on a wearable system that collects information and analyses the human gait. The strategy consists, essentially, in the construction and use of Associative Skill Memories (ASMs) as tools for recognizing the locomotion modes. Consequently, at an early stage, the capabilities of the ASMs for the different modes of locomotion were studied. Then, a classifier was developed based on a set of ASMs. Posteriorly, a neural network classifier based on deep learning was used to classify, in a similar way, the same modes of locomotion. Deep learning is a technique actually widely used in data classification. These classifiers were implemented and compared, providing for a tool with a good accuracy in recognizing the modes of locomotion. In order to implement this strategy, it was previously necessary to carry out extremely important support work. An inertial measurement units’ (IMUs) system was chosen due to its extreme potential to monitor outpatient activities in the home environment. This system, which combines inertial and magnetic sensors and is able to perform the monitoring of gait parameters in real time, was validated and calibrated. Posteriorly, this system was used to collect data from healthy subjects that mimicked Fs. Results have shown that the accuracy of the classifiers was quite acceptable, and the neural networks based classifier presented the best results with 92.71% of accuracy. As future work, it is proposed to apply these strategies in real time in order to avoid the occurrence of falls.As quedas são um problema predominante na sociedade atual. O número de quedas tem aumentado bastante nos últimos quinze anos. Algumas quedas resultam em lesões e o custo associado ao seu tratamento é alto. No entanto, trata-se de um problema complexo que requer várias etapas a serem abordadas. Ou seja, é crucial desenvolver estratégias que reconheçam o modo de locomoção, indicando o estado do sujeito em várias situações, nomeadamente, marcha normal, passo antes da queda (pré-queda) e situação de queda. Assim, esta tese tem como objetivo desenvolver uma estratégia capaz de identificar essas situações com base num sistema wearable que colete informações e analise a marcha humana. A estratégia consiste, essencialmente, na construção e utilização de Associative Skill Memories (ASMs) como ferramenta para reconhecimento dos modos de locomoção. Consequentemente, numa fase inicial, foram estudadas as capacidades das ASMs para os diferentes modos de locomoção. Depois, foi desenvolvido um classificador baseado em ASMs. Posteriormente, um classificador de redes neuronais baseado em deep learning foi utilizado para classificar, de forma semelhante, os mesmos modos de locomoção. Deep learning é uma técnica bastante utilizada em classificação de dados. Estes classificadores foram implementados e comparados, fornecendo a uma ferramenta com uma boa precisão no reconhecimento dos modos de locomoção. Para implementar esta estratégia, era necessário realizar previamente um trabalho de suporte extremamente importante. Um sistema de unidades de medição inercial (IMUs), foi escolhido devido ao seu potencial extremo para monitorizar as atividades ambulatórias no ambiente domiciliar. Este sistema que combina sensores inerciais e magnéticos e é capaz de efetuar a monitorização de parâmetros da marcha em tempo real, foi validado e calibrado. Posteriormente, este Sistema foi usado para adquirir dados da marcha de indivíduos saudáveis que imitiram quedas. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e o classificador baseado em redes neuronais apresentou os melhores resultados com 92.71% de precisão. Como trabalho futuro, propõe-se a aplicação destas estratégias em tempo real de forma a evitar a ocorrência de quedas

A Low Complexity 6DoF Magnetic Tracking System For Biomedical Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

A methodology for the performance evaluation of inertial measurement units

This paper presents a methodology for a reliable comparison among Inertial Measurement Units or attitude estimation devices in a Vicon environment. The misalignment among the reference systems and the lack of synchronization among the devices are the main problems for the correct performance evaluation using Vicon as reference measurement system. We propose a genetic algorithm coupled with Dynamic Time Warping (DTW) to solve these issues. To validate the efficacy of the methodology, a performance comparison is implemented between the WB-3 ultra-miniaturized Inertial Measurement Unit (IMU), developed by our group, with the commercial IMU InertiaCube3™ by InterSense

Ergowear: desenvolvimento de um vestuário inteligente para monitorização postural e biofeedback

Dissertação de mestrado em Engenharia Biomédica (especialização em Eletrónica Médica)Atualmente, as Lesões Musculoesqueléticas Relacionadas com o Trabalho (LMERT) são considera das o ”problema relacionado com o trabalho mais prevalente”na União Europeia, levando a um custo estimado de cerca de 240 biliões de euros. Em casos mais severos, estes distúrbios podem causar danos vitalícios à saúde do trabalhador, reduzindo a sua qualidade de vida. De facto, LMERTs são con sideradas a principal causa da reforma precoce dos trabalhadores. Foi reportado que os segmentos da parte superior do corpo são mais suceptíveis ao desenvolvimento de LMERTs. Para mitigar a prevalência de LMERTs, ergonomistas maioritariamente aplicam métodos de avaliação observacionais, que são alta mente dependentes da experiência do analista, e apresentam baixa objetividade e repetibilidade. Desta maneira, esforços têm sido feitos para desenvolver ferramentas de avaliação ergonómica baseadas na instrumentação, para compensar essas limitações. Além disso, com a ascensão do conceito da indústria 5.0, o trabalhador humano volta a ser o foco principal na indústria, juntamente com o robô colaborativo. No entanto, para alcançar uma relação verdadeiramente colaborativa e simbiótica entre o trabalhador e o robô, este último precisa de reconhecer as intenções do trabalhador. Para superar este obstáculo, sis temas de captura de movimento podem ser integrados nesta estrutura, fornecendo dados de movimento ao robô colaborativo. Esta dissertação visa a melhoria de um sistema de captura de movimento autónomo, da parte supe rior do corpo, de abordagem inercial que servirá, não apenas para monitorizar a postura do trabalhador, mas também avaliar a ergonomia do usuário e fornecer consciencialização postural ao usuário, por meio de motores de biofeedback. Além disso, o sistema foi já idealizado tendo em mente a sua integração numa estrutura colaborativa humano-robô. Para atingir estes objetivos, foi aplicada uma metodologia de design centrado no utilizador, começando pela análise do Estado da Arte, a avaliação das limitações do sistema anterior, a definição dos requisitos do sistema, o desenvolvimento da peça de vestuário, arquite tura do hardware e arquitetura do software do sistema. Por fim, o sistema foi validado para verificar se estava em conformidade com os requisitos especificados. O sistema é composto por 9 Unidades de Medição Inercial (UMI), posicionados na parte inferior e superior das costas, cabeça, braços, antebraços e mãos. Também foi integrado um sistema de atuação, para biofeedback postural, composto por 6 motores vibrotáteis, localizados na região lombar e próximo do pescoço, cotovelos e pulsos. O sistema é alimentado por uma powerbank e todos os dados adquiridos são enviados para uma estação de processamento, via WiFi (User Datagram Protocol (UDP)), garantindo autonomia. O sistema tem integrado um filtro de fusão Complementar Extendido e uma sequência de calibração Sensor-para-Segmento estática, de maneira a aumentar a precisão da estimativa dos ângulos das articulações. Além disso, o sistema é capaz de amostrar os dados angulares a 240 Hz, enquanto que o sistema anterior era capaz de amostrar no máximo a 100 Hz, melhorando a resolução da aquisição dos dados. O sistema foi validado em termos de hardware e usabilidade. Os testes de hardware abordaram a caracterização da autonomia, frequência de amostragem, robustez mecânica e desempenho da comuni cação sem fio do sistema, em diversos contextos, e também para verificar se estes estão em conformidade com os requisitos técnicos previamente definidos, que foi o caso. Adicionalmente, as especificações da nova versão do sistema foram comparadas com a anterior, onde se observou uma melhoria direta signifi cativa, como por exemplo, maior frequência de amostragem, menor perda de pacote, menor consumo de corrente, entre outras, e com sistemas comerciais de referência (XSens Link). Testes de usabilidade foram realizados com 9 participantes que realizaram vários movimentos uniarticulares e complexos. Após os testes, os usuários responderam a um questionário baseado na Escala de Usabilidade do Sistema (EUS). O sistema foi bem aceite pelos os usuários, em termos de estética e conforto, em geral, comprovando um elevado nível de vestibilidade.Nowadays, Work-Related Musculoskeletal Disorders (WRMSDs) are considered the ”most prevalent work-related problem” in the European Union (EU), leading to an estimated cost of about 240 billion EUR. In more severe cases, these disorders can cause life-long impairments to the workers’ health, reducing their quality of life. In fact, WRMSDs are the main cause for the workers’ early retirement. It was reported that the upper body segments of the worker are more susceptible to the development of WRMSDs. To mitigate the prevalence of WRMSD, ergonomists mostly apply observational assessment methods, which are highly dependant on the analyst’s expertise, have low objectivity and repeatability. Therefore, efforts have been made to develop instrumented-based ergonomic assessment tools, to compensate for these limitations. Moreover, with the rise of the 5.0 industry concept, the human worker is once again the main focus in the industry, along with the Collaborative Robot (cobot). However, to achieve a truly collaborative relation between the worker and the cobot, the latter needs to know the worker’s intentions. To surpass this obstacle, Motion Capture (MoCap) systems can be integrated in this framework, providing motion data to the cobot. This dissertation aims at the improvement of a stand-alone, upper-body, inertial, MoCap system, that will serve to not only monitor the worker’s posture, but also to assess the user’s ergonomics and provide posture awareness to the user, through biofeedback motors. Furthermore, it was also designed to integrate a human-robot collaborative framework. To achieve this, a user-centred design methodology was applied, starting with analyzing the State of Art (SOA), assessing the limitations of the previous system, defining the system’s requirements, developing the garment, hardware architecture and software architecture of the system. Lastly, the system was validated to ascertain if it is in conformity with the specified requirements. The developed system is composed of 9 Inertial Measurement Units (IMUs), placed on the lower and upper back, head, upper arms, forearms and hands. An actuation system was also integrated, for postural biofeedback, and it is comprised of 6 vibrotactile motors, located in the lower back, and in close proximity to the neck, elbows and wrists. The system is powered by a powerbank and all of the acquired data is sent to a main station, via WiFi (UDP), granting a standalone characteristic. The system integrates an Extended Complementary Filter (ECF) and a static Sensor-to-Segment (STS) calibration sequence to increase the joint angle estimation accuracy. Furthermore, the system is able to sample the angular data at 240 Hz, while the previous system was able to sample it at a maximum 100 Hz, improving the resolution of the data acquisition. The system was validated in terms of hardware and usability. The hardware tests addressed the char acterization of the system’s autonomy, sampling frequency, mechanical robustness and wireless commu nication performance in different contexts, and ascertain if they comply with the technical requirements, which was the case. Moreover, the specifications of the new version were compared with the previous one, where a significant direct improvement was observed, such as, higher sampling frequency, lower packet loss, lower current consumption, among others, and with a commercial system of reference (XSens Link). Usability tests were carried out with 9 participants who performed several uni-joint and complex motions. After testing, users answered a questionnaire based on the System Usability Scale (SUS). The system was very well accepted by the participants, regarding aesthetics and overall comfort, proving to have a high level of wearability

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions

Purpose: Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. Methods: This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject’s movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR. Results: Statistically significant differences between dynamic activities (“walking slow”, “walking fast”, “running” and “cycling”) and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy). Conclusion: Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system

Reliable and robust detection of freezing of gait episodes with wearable electronic devices

A wearable wireless sensing system for assisting patients affected by Parkinson's disease is proposed. It uses integrated micro-electro-mechanical inertial sensors able to recognize the episodes of involuntary gait freezing. The system operates in real time and is designed for outdoor and indoor applications. Standard tests were performed on a noticeable number of patients and healthy persons and the algorithm demonstrated its reliability and robustness respect to individual specific gait and postural behaviors. The overall performances of the system are excellent with a specificity higher than 97%

A Smart Safety Helmet using IMU and EEG sensors for analysis of worker’s fatigue

It is known that head gesture and mental states can reflect some human behaviors related to a risk of accident when using machine-tools. The research works presented in this paper aim to reduce the number of injury and thus increase worker safety. Instead using camera, this paper presents a Smart Safety Helmet (SSH) in order to track head gestures and mental states of worker able to recognize anomalous behavior. Information extracted from SSH is used for computing risk level of accident (a safety level) for preventing and reducing injury or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of 9DOF Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reach a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd