Combining inertial-based ergonomic assessment with biofeedback for posture correction: a narrative review

Work-related musculoskeletal disorders (WRMSDs) are the most reported work-related health problem in the European Union, representing an economic burden equivalent to 2% of its gross domestic product. Awkward postures are one of the main risk factors. Several postural assessment tools try to identify ergonomic exposure factors for evaluating WRMSD risk, yet these are commonly based on observation. Replacing observations with objective measurements can bring more accuracy and reproducibility to this analysis; hence, a direct measurement approach for the assessment is desired. This review looks for two-fold solutions, able to not only monitor workers’ posture using inertial sensors but also to return that information to the user, in a biofeedback loop. It presents systems for posture risk assessment, regarding ergonomic methods, sensors’ and actuators’ characteristics, and validation protocols. In particular, this review advances previous manuscripts by exploring the literature regarding different biofeedback strategies and ways to encode meaningful information in the cues, i.e., able to deliver intuitive ergonomic guidance so that the user becomes aware and changes into a more neutral posture. The combination of inertial sensors and vibrotactile motors stood out, due to its effectiveness in reducing postural risk. Directional feedback to guide users’ segments individually was found to be a promising strategy, although its validation is still limited. The results of the reviewed manuscripts pointed out the relevant practices, potentialities, and limitations of the existing solutions, allowing the identification of future challenges.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and the INTEGRATOR project under Grant 2022.15668.MIT. Sara Cerqueira was supported by the doctoral Grant FRH/BD/151382/2021, financed by FCT, under MIT Portugal Program

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments

Breathing Monitoring and Pattern Recognition with Wearable Sensors

This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection

Wearable technology: role in respiratory health and disease

In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine

Tractor cabin ergonomics analyses by means of Kinect motion capture technology

Kinect is the de facto standard for real-time depth sensing and motion capture cameras. The sensor is here proposed for exploiting body tracking during driving operations. The motion capture system was developed taking advantage of the Microsoft software development kit (SDK), and implemented for real-time monitoring of body movements of a beginner and an expert tractor drivers, on different tracks (straight and with curves) and with different driving conditions (manual and assisted steering). Tests show how analyses can be done not only in terms of absolute movements, but also in terms of relative shifts, allowing for quantification of angular displacements or rotations

Biosensing and Actuation—Platforms Coupling Body Input-Output Modalities for Affective Technologies

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer’s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of “orchestration”. By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users’ body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies

Wearable Sensors and Machine Learning based Human Movement Analysis – Applications in Sports and Medicine

Die Analyse menschlicher Bewegung außerhalb des Labors unter realen Bedingungen ist in den letzten Jahren sowohl in sportlichen als auch in medizinischen Anwendungen zunehmend bedeutender geworden. Mobile Sensoren, welche am Körper getragen werden, haben sich in diesem Zusammenhang als wertvolle Messinstrumente etabliert. Auf Grund des Umfangs, der Komplexität, der Heterogenität und der Störanfälligkeit der Daten werden vielseitige Analysemethoden eingesetzt, um die Daten zu verarbeiten und auszuwerten. Zudem sind häufig Modellierungsansätze notwendig, da die gemessenen Größen nicht auf direktem Weg aussagekräftige biomechanische Variablen liefern. Seit wenigen Jahren haben sich hierfür Methoden des maschinellen Lernens als vielversprechende Instrumente zur Ermittlung von Zielvariablen, wie beispielsweise der Gelenkwinkel, herausgestellt. Aktuell befindet sich die Forschung an der Schnittstelle aus Biomechanik, mobiler Sensoren und maschinellem Lernen noch am Anfang. Der Bereich birgt grundsätzlich ein erhebliches Potenzial, um einerseits das Spektrum an mobilen Anwendungen im Sport, insbesondere in Sportarten mit komplexen Bewegungsanforderungen, wie beispielsweise dem Eishockey, zu erweitern. Andererseits können Methoden des maschinellen Lernens zur Abschätzung von Belastungen auf Körperstrukturen mittels mobiler Sensordaten genutzt werden. Vor allem die Anwendung mobiler Sensoren in Kombination mit Prädiktionsmodellen zur Ermittlung der Kniegelenkbelastung, wie beispielsweise der Gelenkmomente, wurde bisher nur unzureichend erforscht. Gleichwohl kommt der mobilen Erfassung von Gelenkbelastungen in der Diagnostik und Rehabilitation von Verletzungen sowie Muskel-Skelett-Erkrankungen eine zentrale Bedeutung zu. Das übergeordnete Ziel dieser Dissertation ist es, festzustellen inwieweit tragbare Sensoren und Verfahren des maschinellen Lernens zur Quantifizierung sportlicher Bewegungsmerkmale sowie zur Ermittlung der Belastung von Körperstrukturen bei der Ausführung von Alltags- und Sportbewegungen eingesetzt werden können. Die Dissertation basiert auf vier Studien, welche in internationalen Fachzeitschriften mit Peer-Review-Prozess erschienen sind. Die ersten beiden Studien konzentrieren sich zum einen auf die automatisierte Erkennung von zeitlichen Events und zum anderen auf die mobile Leistungsanalyse während des Schlittschuhlaufens im Eishockey. Die beiden weiteren Studien präsentieren jeweils einen neuartigen Ansatz zur Schätzung von Belastungen im Kniegelenk mittels künstlich neuronalen Netzen. Zwei mobile Sensoren, welche in eine Kniebandage integriert sind, dienen hierbei als Datenbasis zur Ermittlung von Kniegelenkskräften während unterschiedlicher Sportbewegungen sowie von Kniegelenksmomenten während verschiedener Lokomotionsaufgaben. Studie I zeigt eine präzise, effiziente und einfache Methode zur zeitlichen Analyse des Schlittschuhlaufens im Eishockey mittels einem am Schlittschuh befestigten Beschleunigungssensor. Die Validierung des neuartigen Ansatzes erfolgt anhand synchroner Messungen des plantaren Fußdrucks. Der mittlere Unterschied zwischen den beiden Erfassungsmethoden liegt sowohl für die Standphasendauer als auch der Gangzyklusdauer unter einer Millisekunde. Studie II zeigt das Potenzial von Beschleunigungssensoren zur Technik- und Leistungsanalyse des Schlittschuhlaufens im Eishockey. Die Ergebnisse zeigen für die Standphasendauer und Schrittintensität sowohl Unterschiede zwischen beschleunigenden Schritten und Schritten bei konstanter Geschwindigkeit als auch zwischen Teilnehmern unterschiedlichen Leistungsniveaus. Eine Korrelationsanalyse offenbart, insbesondere für die Schrittintensität, einen starken Zusammenhang mit der sportlichen Leistung des Schlittschuhlaufens im Sinne einer verkürzten Sprintzeit. Studie III präsentiert ein tragbares System zur Erfassung von Belastungen im Kniegelenk bei verschiedenen sportlichen Bewegungen auf Basis zweier mobiler Sensoren. Im Speziellen werden unterschiedliche lineare Bewegungen, Richtungswechsel und Sprünge betrachtet. Die mittels künstlich neuronalem Netz ermittelten dreidimensionalen Kniegelenkskräfte zeigen, mit Ausnahme der mediolateralen Kraftkomponente, für die meisten analysierten Bewegungen eine gute Übereinstimmung mit invers-dynamisch berechneten Referenzdaten. Die abschließende Studie IV stellt eine Erweiterung des in Studie III entwickelten tragbaren Systems zur Ermittlung von Belastungen im Kniegelenk dar. Die ambulante Beurteilung der Gelenkbelastung bei Kniearthrose steht hierbei im Fokus. Die entwickelten Prädiktionsmodelle zeigen für das Knieflexionsmoment eine gute Übereinstimmung mit invers-dynamisch berechneten Referenzdaten für den Großteil der analysierten Bewegungen. Demgegenüber ist bei der Ermittlung des Knieadduktionsmoments mittels künstlichen neuronalen Netzen Vorsicht geboten. Je nach Bewegung, kommt es zu einer schwachen bis starken Übereinstimmung zwischen der mittels Prädiktionsmodell bestimmten Belastung und dem Referenzwert. Zusammenfassend tragen die Ergebnisse von Studie I und Studie II zur sportartspezifischen Leistungsanalyse im Eishockey bei. Zukünftig können sowohl die Trainingsqualität als auch die gezielte Verbesserung sportlicher Leistung durch den Einsatz von am Körper getragener Sensoren in hohem Maße profitieren. Die methodischen Neuerungen und Erkenntnisse aus Studie III und Studie IV ebnen den Weg für die Entwicklung neuartiger Technologien im Gesundheitsbereich. Mit Blick in die Zukunft können mobile Sensoren zur intelligenten Analyse menschlicher Bewegungen sinnvoll eingesetzt werden. Die vorliegende Dissertation zeigt, dass die mobile Bewegungsanalyse zur Erleichterung der sportartspezifischen Leistungsdiagnostik unter Feldbedingungen beiträgt. Zudem zeigt die Arbeit, dass die mobile Bewegungsanalyse einen wichtigen Beitrag zur Verbesserung der Gesundheitsdiagnostik und Rehabilitation nach akuten Verletzungen oder bei chronischen muskuloskelettalen Erkrankungen leistet

Methods for monitoring the human circadian rhythm in free-living

Our internal clock, the circadian clock, determines at which time we have our best cognitive abilities, are physically strongest, and when we are tired. Circadian clock phase is influenced primarily through exposure to light. A direct pathway from the eyes to the suprachiasmatic nucleus, where the circadian clock resides, is used to synchronise the circadian clock to external light-dark cycles. In modern society, with the ability to work anywhere at anytime and a full social agenda, many struggle to keep internal and external clocks synchronised. Living against our circadian clock makes us less efficient and poses serious health impact, especially when exercised over a long period of time, e.g. in shift workers. Assessing circadian clock phase is a cumbersome and uncomfortable task. A common method, dim light melatonin onset testing, requires a series of eight saliva samples taken in hourly intervals while the subject stays in dim light condition from 5 hours before until 2 hours past their habitual bedtime. At the same time, sensor-rich smartphones have become widely available and wearable computing is on the rise. The hypothesis of this thesis is that smartphones and wearables can be used to record sensor data to monitor human circadian rhythms in free-living. To test this hypothesis, we conducted research on specialised wearable hardware and smartphones to record relevant data, and developed algorithms to monitor circadian clock phase in free-living. We first introduce our smart eyeglasses concept, which can be personalised to the wearers head and 3D-printed. Furthermore, hardware was integrated into the eyewear to recognise typical activities of daily living (ADLs). A light sensor integrated into the eyeglasses bridge was used to detect screen use. In addition to wearables, we also investigate if sleep-wake patterns can be revealed from smartphone context information. We introduce novel methods to detect sleep opportunity, which incorporate expert knowledge to filter and fuse classifier outputs. Furthermore, we estimate light exposure from smartphone sensor and weather in- formation. We applied the Kronauer model to compare the phase shift resulting from head light measurements, wrist measurements, and smartphone estimations. We found it was possible to monitor circadian phase shift from light estimation based on smartphone sensor and weather information with a weekly error of 32±17min, which outperformed wrist measurements in 11 out of 12 participants. Sleep could be detected from smartphone use with an onset error of 40±48 min and wake error of 42±57 min. Screen use could be detected smart eyeglasses with 0.9 ROC AUC for ambient light intensities below 200lux. Nine clusters of ADLs were distinguished using Gaussian mixture models with an average accuracy of 77%. In conclusion, a combination of the proposed smartphones and smart eyeglasses applications could support users in synchronising their circadian clock to the external clocks, thus living a healthier lifestyle

Improving the prevention of fall from height on construction sites through the combination of technologies

Fall from height is a cause of concern in the construction sector. Appropriate use of a harness can be the difference between an incident or a critical accident. Monitoring the proper use of a harness in the workplace using Bluetooth Low Energy (BLE) devices is a recent and effective approach. The aim of this article is to identify typical limitations in a BLE monitoring system in order to propose solutions according to the existing literature. Alternative solutions found in the literature showed that the integration of BLE with other technologies such as building information modeling, radio-frequency identification or the global positioning system can improve the effectiveness of current monitoring approaches based only on BLE and reduce rates of fall from height accidents. For correct integration, both technological factors (cost, compatibility, data transmission) and cultural factors (social acceptance, procedures, etc.) must be taken into account