Exploring the Application of Wearable Movement Sensors in People with Knee Osteoarthritis

People with knee osteoarthritis have difficulty with functional activities, such as walking or get into/out of a chair. This thesis explored the clinical relevance of biomechanics and how wearable sensor technology may be used to assess how people move when their clinician is unable to directly observe them, such as at home or work. The findings of this thesis suggest that artificial intelligence can be used to process data from sensors to provide clinically important information about how people perform troublesome activities

Inertial sensors-based lower-limb rehabilitation assessment: A comprehensive evaluation of gait, kinematic and statistical metrics

Analysis of biomechanics is frequently used in both clinical and sporting practice in order to assess human motion and their performance of defined tasks. Whilst camera-based motion capture systems have long been regarded as the ‘Gold-standard’ for quantitative movement-based analysis, their application is not without limitations as regards potential sources of variability in measurements, high cost, and practicality of use for larger patient/subject groups. Another more practical approach, which presents itself as a viable solution to biomechanical motion capture and monitoring in sporting and patient groups, is through the use of small-size low-cost wearable Micro-ElectroMechanical Systems (MEMs)-based inertial sensors. The clinical aim of the present work is to evaluate rehabilitation progress following knee injuries, identifying a number of metrics measured via a wireless inertial sensing system. Several metrics in the time-domain have been considered to be reliable for measuring and quantifying patient progress across multiple exercises in different activities. This system was developed at the Tyndall National Institute and is able to provide a complete and accurate biomechanics assessment without the constraints of a motion capture laboratory. The results show that inertial sensors can be used for a quantitative assessment of knee joint mobility, providing valuable information to clinical experts as regards the trend of patient progress over the course of rehabilitation

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

Feedback from activity trackers improves daily step count after knee and hip arthroplasty: A randomized controlled trial

Background: Commercial wrist-worn activity monitors have the potential to accurately assess activity levels and are being increasingly adopted in the general population. The aim of this study was to determine if feedback from a commercial activity monitor improves activity levels over the first 6 weeks after total hip arthroplasty (THA) or total knee arthroplasty (TKA). Methods: One hundred sixty-three consecutive subjects undergoing primary TKA or THAwere randomized into 2 groups. Subjects received an activity tracker with the step display obscured 2 weeks before surgery and completed patient-reported outcome measures (PROMs). On day 1 after surgery, participants were randomized to either the “feedback (FB) group” or the “no feedback (NFB) group.” The FB group was able to view their daily step count and was given a daily step goal. Participants in the NFB group wore the device with the display obscured for 2 weeks after surgery, after which time they were also able to see their daily step count but did not receive a formal step goal. The mean daily steps at 1, 2, 6 weeks, and 6 months were monitored. At 6 months after surgery, subjects repeated PROMs and daily step count collection. Results: Of the 163 subjects, 95 underwent THA and 68 underwent TKA. FB subjects had a significantly higher (P \u3c .03) mean daily step count by 43% in week 1, 33% in week 2, 21% in week 6, and 17% at 6 months, compared with NFB. The FB subjects were 1.7 times more likely to achieve a mean 7000 steps per day than the NFB subjects at 6 weeks after surgery (P ¼ .02). There was no significant difference between the groups in PROMs at 6 months. Ninety percent of FB and 83% of NFB participants reported that they were satisfied with the results of the surgery (P ¼ .08). At 6 months after surgery, 70% of subjects had a greater mean daily step count compared with their preoperative level. Conclusion: Subjects who received feedback from a commercial activity tracker with a daily step goal had significantly higher activity levels after hip and knee arthroplasty over 6 weeks and 6 months, compared with subjects who did not receive feedback in a randomized controlled trial. Commercial activity trackers may be a useful and effective adjunct after arthroplasty

Wearable Systems for Monitoring Mobility-Related Activities in Chronic Disease: A Systematic Review

The use of wearable motion sensing technology offers important advantages over conventional methods for obtaining measures of physical activity and/or physical functioning in individuals with chronic diseases. This review aims to identify the actual state of applying wearable systems for monitoring mobility-related activity in individuals with chronic disease conditions. In this review we focus on technologies and applications, feasibility and adherence aspects, and clinical relevance of wearable motion sensing technology. PubMed (Medline since 1990), PEdro, and reference lists of all relevant articles were searched. Two authors independently reviewed randomised trials systematically. The quality of selected articles was scored and study results were summarised and discussed. 163 abstracts were considered. After application of inclusion criteria and full text reading, 25 articles were taken into account in a full text review. Twelve of these papers evaluated walking with pedometers, seven used uniaxial accelerometers to assess physical activity, six used multiaxial accelerometers, and two papers used a combination approach of a pedometer and a multiaxial accelerometer for obtaining overall activity and energy expenditure measures. Seven studies mentioned feasibility and/or adherence aspects. The number of studies that use movement sensors for monitoring of activity patterns in chronic disease (postural transitions, time spent in certain positions or activities) is nonexistent on the RCT level of study design. Although feasible methods for monitoring human mobility are available, evidence-based clinical applications of these methods in individuals with chronic diseases are in need of further development

Effects of obesity on walking patterns and adaptability during obstacle crossing

Obesity is a worldwide public health epidemic with no sign of yet abating. Although previous studies have examined the impact of obesity on walking, little is known about the effects of practice on walking patterns in individuals with obesity. The purpose of this current study was to evaluate whether an obstacle-crossing task may detect walking deficits in a group of adults electing to undergo bariatric surgery. With a cross-sectional design, we collected walking parameters as 24 adults (M age= 46.19, SD= 12.90) with obese body mass index (BMI) scores (M BMI= 41.68, SD= 5.80) and 26 adults (M age= 21.88, SD= 3.48) with normal BMI scores (M BMI= 23.09, SD= 4.47) walked in 5 conditions for 5 trials each: on flat ground, crossing over low, medium, and high obstacles, and again on flat ground. The timing and distance of participants' steps were collected with a mechanized gait carpet (GAITRite, Inc.). We conducted 5 (condition) repeated measures (RM) ANOVAs on our main dependent variables, which measured how fast (velocity) and long (step length) participants' steps were and how much time they spent with one (single limb support time) versus two (double limb support time) feet on the ground. The results showed within session improvements in participants' walking patterns. Comparisons of the first and last trials on flat ground showed that participants took longer, faster steps by increasing step length and velocity (ps<.01). They also spent more time with one versus two feet on the ground via increased single limb support time and decreased double limb support time (ps<.001). Our findings suggest that an obstacle-crossing task may help spur improvements in walking patterns even before adults elect to undergo bariatric surgery

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Quantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

The knee is one of the most injured body parts, causing 18 million patients to be seen in clinics every year. Because the knee is a weight-bearing joint, it is prone to pathologies such as osteoarthritis and ligamentous injuries. Existing technologies for monitoring knee health can provide accurate assessment and diagnosis for acute injuries. However, they are mainly confined to clinical or laboratory settings only, time-consuming, expensive, and not well-suited for longitudinal monitoring. Developing a novel technology for joint health assessment beyond the clinic can further provide insights on the rehabilitation process and quantitative usage of the knee joint. To better understand the underlying properties and fundamentals of joint sounds, this research will investigate the relationship between the changes in the knee joint structure (i.e. structural damage and joint contact force) and the JAEs while developing novel techniques for analyzing these sounds. We envision that the possibility of quantifying joint structure and joint load usage from these acoustic sensors would advance the potential of JAE as the next biomarker of joint health that can be captured with wearable technology. First, we developed a novel processing technique for JAEs that quantify on the structural change of the knee from injured athletes and human lower-limb cadaver models. Second, we quantified whether JAEs can detect the increase in the mechanical stress on the knee joint using an unsupervised graph mining algorithm. Lastly, we quantified the directional bias of the load distribution between medial and lateral compartment using JAEs. Understanding and monitoring the quantitative usage of knee loads in daily activities can broaden the implications for longitudinal joint health monitoring.Ph.D