Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

The background of this paper is to apply advanced real-time gait analysis to walking interventions in daily life setting. A vast of wearable devices provide gait information but not more than pedometer functions such as step counting, displacement, and velocity. This paper suggests a real-time gait analysis method based on a head-worn inertial measurement unit. A novel analysis method implements real-time detection of gait events (heel strike, toe off, and mid-stance phase) and immediately provides detailed spatiotemporal parameters. The reliability of this method was proven by a measurement with over 11 000 steps from seven participants on a 400-m outdoor track. The advanced gait analysis was conducted without any limitation of a fixed reference frame (e.g., indoor stage and infrared cameras). The mean absolute error in step-counting was 0.24%. Compared to a pedometer, additional gait parameters were obtained such as foot-ground contact time (CT) and CT ratio. The gait monitoring system can be used as real-time and long-term feedback, which is applicable in the management of the health status and on injury prevention. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.European Commission/H2020-FETPROACT-2014/641321/E

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Analysis and enhancement of interpersonal coordination using inertial measurement unit solutions

Die heutigen mobilen Kommunikationstechnologien haben den Umfang der verbalen und textbasierten Kommunikation mit anderen Menschen, sozialen Robotern und künstlicher Intelligenz erhöht. Auf der anderen Seite reduzieren diese Technologien die nonverbale und die direkte persönliche Kommunikation, was zu einer gesellschaftlichen Thematik geworden ist, weil die Verringerung der direkten persönlichen Interaktionen eine angemessene Wahrnehmung sozialer und umgebungsbedingter Reizmuster erschweren und die Entwicklung allgemeiner sozialer Fähigkeiten bremsen könnte. Wissenschaftler haben aktuell die Bedeutung nonverbaler zwischenmenschlicher Aktivitäten als soziale Fähigkeiten untersucht, indem sie menschliche Verhaltensmuster in Zusammenhang mit den jeweilgen neurophysiologischen Aktivierungsmustern analzsiert haben. Solche Querschnittsansätze werden auch im Forschungsprojekt der Europäischen Union "Socializing sensori-motor contingencies" (socSMCs) verfolgt, das darauf abzielt, die Leistungsfähigkeit sozialer Roboter zu verbessern und Autismus-Spektrumsstörungen (ASD) adäquat zu behandeln. In diesem Zusammenhang ist die Modellierung und das Benchmarking des Sozialverhaltens gesunder Menschen eine Grundlage für theorieorientierte und experimentelle Studien zum weiterführenden Verständnis und zur Unterstützung interpersoneller Koordination. In diesem Zusammenhang wurden zwei verschiedene empirische Kategorien in Abhängigkeit von der Entfernung der Interagierenden zueinander vorgeschlagen: distale vs. proximale Interaktionssettings, da sich die Struktur der beteiligten kognitiven Systeme zwischen den Kategorien ändert und sich die Ebene der erwachsenden socSMCs verschiebt. Da diese Dissertation im Rahmen des socSMCs-Projekts entstanden ist, wurden Interaktionssettings für beide Kategorien (distal und proximal) entwickelt. Zudem wurden Ein-Sensor-Lösungen zur Reduzierung des Messaufwands (und auch der Kosten) entwickelt, um eine Messung ausgesuchter Verhaltensparameter bei einer Vielzahl von Menschen und sozialen Interaktionen zu ermöglichen. Zunächst wurden Algorithmen für eine kopfgetragene Trägheitsmesseinheit (H-IMU) zur Messung der menschlichen Kinematik als eine Ein-Sensor-Lösung entwickelt. Die Ergebnisse bestätigten, dass die H-IMU die eigenen Gangparameter unabhängig voneinander allein auf Basis der Kopfkinematik messen kann. Zweitens wurden—als ein distales socSMC-Setting—die interpersonellen Kopplungen mit einem Bezug auf drei interagierende Merkmale von „Übereinstimmung“ (engl.: rapport) behandelt: Positivität, gegenseitige Aufmerksamkeit und Koordination. Die H-IMUs überwachten bestimmte soziale Verhaltensereignisse, die sich auf die Kinematik der Kopforientierung und Oszillation während des Gehens und Sprechens stützen, so dass der Grad der Übereinstimmung geschätzt werden konnte. Schließlich belegten die Ergebnisse einer experimentellen Studie, die zu einer kollaborativen Aufgabe mit der entwickelten IMU-basierten Tablet-Anwendung durchgeführt wurde, unterschiedliche Wirkungen verschiedener audio-motorischer Feedbackformen für eine Unterstützung der interpersonellen Koordination in der Kategorie proximaler sensomotorischer Kontingenzen. Diese Dissertation hat einen intensiven interdisziplinären Charakter: Technologische Anforderungen in den Bereichen der Sensortechnologie und der Softwareentwicklung mussten in direktem Bezug auf vordefinierte verhaltenswissenschaftliche Fragestellungen entwickelt und angewendet bzw. gelöst werden—und dies in zwei unterschiedlichen Domänen (distal, proximal). Der gegebene Bezugsrahmen wurde als eine große Herausforderung bei der Entwicklung der beschriebenen Methoden und Settings wahrgenommen. Die vorgeschlagenen IMU-basierten Lösungen könnten dank der weit verbreiteten IMU-basierten mobilen Geräte zukünftig in verschiedene Anwendungen perspektiv reich integriert werden.Today’s mobile communication technologies have increased verbal and text-based communication with other humans, social robots and intelligent virtual assistants. On the other hand, the technologies reduce face-to-face communication. This social issue is critical because decreasing direct interactions may cause difficulty in reading social and environmental cues, thereby impeding the development of overall social skills. Recently, scientists have studied the importance of nonverbal interpersonal activities to social skills, by measuring human behavioral and neurophysiological patterns. These interdisciplinary approaches are in line with the European Union research project, “Socializing sensorimotor contingencies” (socSMCs), which aims to improve the capability of social robots and properly deal with autism spectrum disorder (ASD). Therefore, modelling and benchmarking healthy humans’ social behavior are fundamental to establish a foundation for research on emergence and enhancement of interpersonal coordination. In this research project, two different experimental settings were categorized depending on interactants’ distance: distal and proximal settings, where the structure of engaged cognitive systems changes, and the level of socSMCs differs. As a part of the project, this dissertation work referred to this spatial framework. Additionally, single-sensor solutions were developed to reduce costs and efforts in measuring human behaviors, recognizing the social behaviors, and enhancing interpersonal coordination. First of all, algorithms using a head worn inertial measurement unit (H-IMU) were developed to measure human kinematics, as a baseline for social behaviors. The results confirmed that the H-IMU can measure individual gait parameters by analyzing only head kinematics. Secondly, as a distal sensorimotor contingency, interpersonal relationship was considered with respect to a dynamic structure of three interacting components: positivity, mutual attentiveness, and coordination. The H-IMUs monitored the social behavioral events relying on kinematics of the head orientation and oscillation during walk and talk, which can contribute to estimate the level of rapport. Finally, in a new collaborative task with the proposed IMU-based tablet application, results verified effects of different auditory-motor feedbacks on the enhancement of interpersonal coordination in a proximal setting. This dissertation has an intensive interdisciplinary character: Technological development, in the areas of sensor and software engineering, was required to apply to or solve issues in direct relation to predefined behavioral scientific questions in two different settings (distal and proximal). The given frame served as a reference in the development of the methods and settings in this dissertation. The proposed IMU-based solutions are also promising for various future applications due to widespread wearable devices with IMUs.European Commission/HORIZON2020-FETPROACT-2014/641321/E

A machine learning approach for stride speed estimation based on a head-mounted IMU

Walking speed in real-life conditions is typically estimated through wearable inertial sensors mounted on waist, lower limbs, or wrists. Very recently, head-mounted inertial sensors are emerging for gait assessment. The present study explores the feasibility of measuring the stride speed with a head-mounted inertial sensor in both laboratory and real-world settings. The developed algorithm exploits a Temporal Convolutional Network for the detection of the gait events and a Gaussian Process Regression for the stride speed estimation. The experimental evaluation was carried out on healthy young participants during both standardised indoor and real-world walking trials. For indoor trials, errors were smaller than previous studies (0.05 m/s). As expected, errors increased at lower speed regimes due to a reduced signals amplitude. During 2.5-hours real-world evaluation, errors were slightly larger but acceptable (0.1 m/s). Reported results are encouraging and show the feasibility of estimating gait speed with a single head-worn inertial sensor

Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version

Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system

© 2019, The Author(s). The hand-wrist region is reported as the most common injury site in boxing. Boxers are at risk due to the amount of wrist motions when impacting training equipment or their opponents, yet we know relatively little about these motions. This paper describes a new method for quantifying wrist motion in boxing using an electromagnetic tracking system. Surrogate testing procedure utilising a polyamide hand and forearm shape, and in vivo testing procedure utilising 29 elite boxers, were used to assess the accuracy and repeatability of the system. 2D kinematic analysis was used to calculate wrist angles using photogrammetry, whilst the data from the electromagnetic tracking system was processed with visual 3D software. The electromagnetic tracking system agreed with the video-based system (paired t tests) in both the surrogate ( 0.9). In the punch testing, for both repeated jab and hook shots, the electromagnetic tracking system showed good reliability (ICCs > 0.8) and substantial reliability (ICCs > 0.6) for flexion–extension and radial-ulnar deviation angles, respectively. The results indicate that wrist kinematics during punching activities can be measured using an electromagnetic tracking system

Head Trajectory Diagrams for Gait Symmetry Analysis Using a Single Head-Worn IMU

Gait symmetry analysis plays an important role in the diagnosis and rehabilitation of pathological gait. Recently, wearable devices have also been developed for simple gait analysis solutions. However, measurement in clinical settings can differ from gait in daily life, and simple wearable devices are restricted to a few parameters, providing one-sided trajectories of one arm or leg. Therefore, head-worn devices with sensors (e.g., earbuds) should be considered to analyze gait symmetry because the head sways towards the left and right side depending on steps. This paper proposed new visualization methods using head-worn sensors, able to facilitate gait symmetry analysis outside as well as inside. Data were collected with an inertial measurement unit (IMU) based motion capture system when twelve participants walked on the 400-m running track. From head trajectories on the transverse and frontal plane, three types of diagrams were displayed, and five concepts of parameters were measured for gait symmetry analysis. The mean absolute percentage error (MAPE) of step counting was lower than 0.65%, representing the reliability of measured parameters. The methods enable also left-right step recognition (MAPE ≤ 2.13%). This study can support maintenance and relearning of a balanced healthy gait in various areas with simple and easy-to-use devices

Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine

Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art

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