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

Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours

Personal Navigation Based on Wireless Networks and Inertial Sensors

Tato práce se zaměřuje na vývoj navigačního algoritmu pro systémy vhodné k lokalizaci osob v budovách a městských prostorech. Vzhledem k požadovaným nízkým nákladům na výsledný navigační systém byla uvažována integrace levných inerciálních senzorů a určování vzdálenosti na základě měření v bezdrátových sítích. Dále bylo předpokládáno, že bezdrátová síť bude určena k jiným účelům (např: měření a regulace), než lokalizace, proto bylo použito měření síly bezdrátového signálu. Kvůli snížení značné nepřesnosti této metody, byla navrhnuta technika mapování ztrát v bezdrátovém kanálu. Nejprve jsou shrnuty různé modely senzorů a prostředí a ty nejvhodnější jsou poté vybrány. Jejich efektivní a nové využití v navigační úloze a vhodná fůze všech dostupných informací jsou hlavní cíle této práce.This thesis deals with navigation system based on wireless networks and inertial sensors. The work aims at a development of positioning algorithm suitable for low-cost indoor or urban pedestrian navigation application. The sensor fusion was applied to increase the localization accuracy. Due to required low application cost only low grade inertial sensors and wireless network based ranging were taken into account. The wireless network was assumed to be preinstalled due to other required functionality (for example: building control) therefore only received signal strength (RSS) range measurement technique was considered. Wireless channel loss mapping method was proposed to overcome the natural uncertainties and restrictions in the RSS range measurements. The available sensor and environment models are summarized first and the most appropriate ones are selected secondly. Their effective and novel application in the navigation task, and favorable fusion (Particle filtering) of all available information are the main objectives of this thesis.

Wearable Sensing for Solid Biomechanics: A Review

Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in health care, sport, well-being, and workflow analysis. Conventional laboratory-based biomechanical analysis systems and observation-based tests are designed only to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less-constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing on sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations, that can affect the accuracy and robustness of existing methods and different methods for reducing the impact of these sources of errors are described in this paper. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, and the future direction of sensing for biomechanics are also discussed

Design of human surrogates for the study of biomechanical injury: a review

Human surrogates are representations of living human structures employed to replicate “real-life” injurious scenarios in artificial environments. They are used primarily to evaluate personal protective equipment (PPE) or integrated safety systems (e.g., seat belts) in a wide range of industry sectors (e.g., automotive, military, security service, and sports equipment). Surrogates are commonly considered in five major categories relative to their form and functionality: human volunteers, postmortem human surrogates, animal surrogates, anthropomorphic test devices, and computational models. Each surrogate has its relative merits. Surrogates have been extensively employed in scenarios concerning “life-threatening” impacts (e.g., penetrating bullets or automotive accidents). However, more frequently occurring nonlethal injuries (e.g., fractures, tears, lacerations, contusions) often result in full or partial debilitation in contexts where optimal human performance is crucial (e.g., military, sports). Detailed study of these injuries requires human surrogates with superior biofidelity to those currently available if PPE designs are to improve. The opportunities afforded by new technologies, materials, instrumentation, and processing capabilities should be exploited to develop a new generation of more sophisticated human surrogates. This paper presents a review of the current state of the art in human surrogate construction, highlighting weaknesses and opportunities, to promote research into improved surrogates for PPE development

Intelligent signal processing for digital healthcare monitoring

Ein gesunder Gang ist ein komplexer Prozess und erfordert ein Gleichgewicht zwischen verschiedenen neurophysiologischen Systemen im Körper und gilt als wesentlicher Indikator für den physischen und kognitiven Gesundheitszustand einer Person. Folglich würden Anwendungen im Bereich der Bioinformatik und des Gesundheitswesens erheblich von den Informationen profitieren, die sich aus einer längeren oder ständigen Überwachung des Gangs, der Gewohnheiten und des Verhaltens von Personen unter ihren natürlichen Lebensbedingungen und bei ihren täglichen Aktivitäten mit Hilfe intelligenter Geräte ergeben. Vergleicht man Trägheitsmess- und stationäre Sensorsysteme, so bieten erstere hervorragende Möglichkeiten für Ganganalyseanwendungen und bieten mehrere Vorteile wie geringe Größe, niedriger Preis, Mobilität und sind leicht in tragbare Systeme zu integrieren. Die zweiten gelten als der Goldstandard, sind aber teuer und für Messungen im Freien ungeeignet. Diese Arbeit konzentriert sich auf die Verbesserung der Zeit und Qualität der Gangrehabilitation nach einer Operation unter Verwendung von Inertialmessgeräten, indem sie eine neuartige Metrik zur objektiven Bewertung des Fortschritts der Gangrehabilitation in realen Umgebungen liefert und die Anzahl der verwendeten Sensoren für praktische, reale Szenarien reduziert. Daher wurden die experimentellen Messungen für eine solche Analyse in einer stark kontrollierten Umgebung durchgeführt, um die Datenqualität zu gewährleisten. In dieser Arbeit wird eine neue Gangmetrik vorgestellt, die den Rehabilitationsfortschritt anhand kinematischer Gangdaten von Aktivitäten in Innen- und Außenbereichen quantifiziert und verfolgt. In dieser Arbeit wird untersucht, wie Signalverarbeitung und maschinelles Lernen formuliert und genutzt werden können, um robuste Methoden zur Bewältigung von Herausforderungen im realen Leben zu entwickeln. Es wird gezeigt, dass der vorgeschlagene Ansatz personalisiert werden kann, um den Fortschritt der Gangrehabilitation zu verfolgen. Ein weiteres Thema dieser Arbeit ist die erfolgreiche Anwendung von Methoden des maschinellen Lernens auf die Ganganalyse aufgrund der großen Datenmenge, die von den tragbaren Sensorsystemen erzeugt wird. In dieser Arbeit wird das neuartige Konzept des ``digitalen Zwillings'' vorgestellt, das die Anzahl der verwendeten Wearable-Sensoren in einem System oder im Falle eines Sensorausfalls reduziert. Die Evaluierung der vorgeschlagenen Metrik mit gesunden Teilnehmern und Patienten unter Verwendung statistischer Signalverarbeitungs- und maschineller Lernmethoden hat gezeigt, dass die Einbeziehung der extrahierten Signalmerkmale in realen Szenarien robust ist, insbesondere für das Szenario mit Rehabilitations-Gehübungen in Innenräumen. Die Methodik wurde auch in einer klinischen Studie evaluiert und lieferte eine gute Leistung bei der Überwachung des Rehabilitationsfortschritts verschiedener Patienten. In dieser Arbeit wird ein Prototyp einer mobilen Anwendung zur objektiven Bewertung des Rehabilitationsfortschritts in realen Umgebungen vorgestellt

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Fusion of wearable and visual sensors for human motion analysis

Human motion analysis is concerned with the study of human activity recognition, human motion tracking, and the analysis of human biomechanics. Human motion analysis has applications within areas of entertainment, sports, and healthcare. For example, activity recognition, which aims to understand and identify different tasks from motion can be applied to create records of staff activity in the operating theatre at a hospital; motion tracking is already employed in some games to provide an improved user interaction experience and can be used to study how medical staff interact in the operating theatre; and human biomechanics, which is the study of the structure and function of the human body, can be used to better understand athlete performance, pathologies in certain patients, and assess the surgical skill of medical staff. As health services strive to improve the quality of patient care and meet the growing demands required to care for expanding populations around the world, solutions that can improve patient care, diagnosis of pathology, and the monitoring and training of medical staff are necessary. Surgical workflow analysis, for example, aims to assess and optimise surgical protocols in the operating theatre by evaluating the tasks that staff perform and measurable outcomes. Human motion analysis methods can be used to quantify the activities and performance of staff for surgical workflow analysis; however, a number of challenges must be overcome before routine motion capture of staff in an operating theatre becomes feasible. Current commercial human motion capture technologies have demonstrated that they are capable of acquiring human movement with sub-centimetre accuracy; however, the complicated setup procedures, size, and embodiment of current systems make them cumbersome and unsuited for routine deployment within an operating theatre. Recent advances in pervasive sensing have resulted in camera systems that can detect and analyse human motion, and small wear- able sensors that can measure a variety of parameters from the human body, such as heart rate, fatigue, balance, and motion. The work in this thesis investigates different methods that enable human motion to be more easily, reliably, and accurately captured through ambient and wearable sensor technologies to address some of the main challenges that have limited the use of motion capture technologies in certain areas of study. Sensor embodiment and accuracy of activity recognition is one of the challenges that affect the adoption of wearable devices for monitoring human activity. Using a single inertial sensor, which captures the movement of the subject, a variety of motion characteristics can be measured. For patients, wearable inertial sensors can be used in long-term activity monitoring to better understand the condition of the patient and potentially identify deviations from normal activity. For medical staff, inertial sensors can be used to capture tasks being performed for automated workflow analysis, which is useful for staff training, optimisation of existing processes, and early indications of complications within clinical procedures. Feature extraction and classification methods are introduced in thesis that demonstrate motion classification accuracies of over 90% for five different classes of walking motion using a single ear-worn sensor. To capture human body posture, current capture systems generally require a large number of sensors or reflective reference markers to be worn on the body, which presents a challenge for many applications, such as monitoring human motion in the operating theatre, as they may restrict natural movements and make setup complex and time consuming. To address this, a method is proposed, which uses a regression method to estimate motion using a subset of fewer wearable inertial sensors. This method is demonstrated using three sensors on the upper body and is shown to achieve mean estimation accuracies as low as 1.6cm, 1.1cm, and 1.4cm for the hand, elbow, and shoulders, respectively, when compared with the gold standard optical motion capture system. Using a subset of three sensors, mean errors for hand position reach 15.5cm. Unlike human motion capture systems that rely on vision and reflective reference point markers, commonly known as marker-based optical motion capture, wearable inertial sensors are prone to inaccuracies resulting from an accumulation of inaccurate measurements, which becomes increasingly prevalent over time. Two methods are introduced in this thesis, which aim to solve this challenge using visual rectification of the assumed state of the subject. Using a ceiling-mounted camera, a human detection and human motion tracking method is introduced to improve the average mean accuracy of tracking to within 5.8cm in a laboratory of 3m × 5m. To improve the accuracy of capturing the position of body parts and posture for human biomechanics, a camera is also utilised to track the body part movements and provide visual rectification of human pose estimates from inertial sensing. For most subjects, deviations of less than 10% from the ground truth are achieved for hand positions, which exhibit the greatest error, and the occurrence of sources of other common visual and inertial estimation errors, such as measurement noise, visual occlusion, and sensor calibration are shown to be reduced.Open Acces

Sensors and Technologies in Spain: State-of-the-Art

The aim of this special issue was to provide a comprehensive view on the state-of-the-art sensor technology in Spain. Different problems cause the appearance and development of new sensor technologies and vice versa, the emergence of new sensors facilitates the solution of existing real problems. [...

Characterizing smart environments as interactive and collective platforms: A review of the key behaviors of responsive architecture

Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural perspective is largely missing. In order to provide a formal understanding of smart environments in architecture, this paper conducts a systematic literature review of scholarly sources over the last decade, focusing on four related subjects: (1) responsive architecture, (2) kinetic architecture, (3) adaptive architecture and (4) intelligent buildings. Through this review, the paper identifies and examines interactive and collective behaviors in smart environments, thereby contributing to defining the properties of creative, smart spaces in the contemporary digital ecosystem. In addition, this research offers a means of systematically characterizing and constructing smart environments as interactive and collective platforms, enabling occupants to sense, experience and understand smart spaces