Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth

Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements

Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.publishedVersio

Inertial measurement units: a brief state of the art on gait analysis

Gait analysis systems are monitoring systems that establish a symbiosis relationship with Ambient Assisted Living (AAL) environments. Human locomotion analysis has a very important role always aiming at improving the quality of life both for individuals needing treatment or rehabilitation, as well as for healthy and elderly people. In fact, a deep and detailed knowledge about gait characteristics at a given time, and not least, monitoring and evaluating over time, will allow early diagnosis of diseases and their complications, and contribute to the decision of the treatment that should be chosen. There are several techniques used for gait measuring such as: Image Processing, Floor Sensors, and Wearable Sensors. Among the wearable sensors, has emerged an electronic device that combines multiple sensors designated by Inertial Measurement Unit (IMU). This device measures angular rate, body's specific force, and in some cases the magnetic field, and this information may be used to monitor human gait. In this article, the aim is: i) to verify the sensors that build up the IMUs, and the resulting designations that the device may have depending on the sensors it contains; ii) to list the applications of the IMUs on gait analysis; iii) to be aware of the devices available on the market and the associated commercial brands; and iv) to list the advantages and disadvantages associated with the device compared to other gait analysis systems. Concerning the literature in the scientific community, although there are some studies that focus on gait analysis or IMUs, none of them aggregates the purposes that will be addressed in this article.This work is supported by the FCT - Fundação para a Ciência e Tecnologia - with the scholarship reference SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145- FEDER-006941

Validation of a Biomechanical Injury and Disease Assessment Platform Applying an Inertial-Based Biosensor and Axis Vector Computation

Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg’s flexion and extension knee movements and applied to a living subject’s upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury

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

A Wearable Sensor Network for Gait Analysis: A 6-Day Experiment of Running Through the Desert

International audienceThis paper presents a new system for analysis of walking and running gaits. The system is based on a network of wireless nodes with various types of embedded sensors. It has been designed to allow long-term recording in outdoor environments and was tested during the 2010 "Sultan Marathon des Sables" desert race. A runner was fitted with the sensory network for six days of the competition. Although technical problems have limited the amount of data recorded, the experiment was nevertheless suc- cessful: the system did not interfere with the runner, who finished with a high ranking, the concept was validated and high quality data were ac- quired. It should be noted that the loss of some of the measurements was mainly due to problems with the cable connectors between the nodes and batteries. In this paper, we describe the technical aspects of the system developed, the experimental conditions under which it was validated, and give examples of the data obtained with some preliminary processing

Using Distributed Wearable Sensors to Measure and Evaluate Human Lower Limb Motions

This paper presents a wearable sensor approach to motion measurements of human lower limbs, in which subjects perform specified walking trials at self-administered speeds so that their level walking and stair ascent capacity can be effectively evaluated. After an initial sensor alignment with the reduced error, quaternion is used to represent 3-D orientation and an optimized gradient descent algorithm is deployed to calculate the quaternion derivative. Sensors on the shank offer additional information to accurately determine the instances of both swing and stance phases. The Denavit-Hartenberg convention is used to set up the kinematic chains when the foot stays stationary on the ground, producing state constraints to minimize the estimation error of knee position. The reliability of this system, from the measurement point of view, has been validated by means of the results obtained from a commercial motion tracking system, namely, Vicon, on healthy subjects. The step size error and the position estimation accuracy change are studied. The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved. The proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift

Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system

Providing location everywhere

Anacleto R., Figueiredo L., Novais P., Almeida A., Providing Location Everywhere, in Progress in Artificial Intelligence, Antunes L., Sofia Pinto H. (eds), Lecture Notes in Artificial Intelligence 7026, Springer-Verlag, ISBN 978-3-540-24768-2, (Proceedings of the 15th Portuguese conference on Artificial Intelligence - EPIA 2011, Lisboa, Portugal), pp 15-28, 2011.The ability to locate an individual is an essential part of many applications, specially the mobile ones. Obtaining this location in an open environment is relatively simple through GPS (Global Positioning System), but indoors or even in dense environments this type of location system doesn’t provide a good accuracy. There are already systems that try to suppress these limitations, but most of them need the existence of a structured environment to work. Since Inertial Navigation Systems (INS) try to suppress the need of a structured environment we propose an INS based on Micro Electrical Mechanical Systems (MEMS) that is capable of, in real time, compute the position of an individual everywhere