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

Design of Wheelchair Robot for Active Postural Support (WRAPS) for Users with Trunk Impairments

People with severe trunk impairments cannot maintain or control upright posture during sitting or reaching out with the upper body. Passive orthoses are clinically available to support the trunk and promote the use of upper extremities in this population. However, these orthoses only rigidly position the torso on a wheelchair but do not facilitate movement of the trunk. In this dissertation, we introduce a novel active-assistive torso brace system for upperbody movements by a subject while seated. We have named this system as Wheelchair Robot for Active Postural Support (WRAPS). We propose designs of two robots, one for the pelvis and the other for the trunk. Each of the two devices has a parallel chain architecture to accommodate the range of motion (ROM), respectively for the pelvic and thoracic segments. The first thoracic robot was designed for the upper trunk motion relative to the pelvis. It has a 2[RP]S-2UPS architecture which provides four degrees-of-freedom (DOFs) to the end-effector placed on the upper trunk. The second is a pelvic robot which is designed to orient the pelvic segment relative to the seat. It has a 3-DOF [RRR]U-2[RR]S architecture, coupled with translation to accommodate pelvic movements relative to the seat. These robot architectures are synthesized based on human movement data. WRAPS can modulate the displacement of both the pelvic and the thoracic segments. Additionally, the forces can be applied on the torso through the end-effectors of these robots. Each of the robot prototypes was evaluated with able-bodied subjects to assess the device wearability, kinematic performances, and control system

Correction of Kinematic Data from a Self-Initiated Prone Position Crawler Trainer for Infants with Cerebral Palsy

In recent years small, inexpensive inertial measurement units (IMUs) have been used clinically to monitor human body joint angles in order to assess the progression of, or recovery from, various diseases affecting movement, including Cerebral Palsy, Parkinson’s Disease, and stroke. A representation of kinematic movement of a joint can be calculated by changes in the orientations of a pair IMUs placed on a limb above and below the joint. However, errors in the kinematic representation can occur if the IMUs are not correctly aligned with the kinematic model or if the initial position of the joint is not precisely known. In addition, due to the sensitivity of the IMUs to magnetic field distortions caused by nearby metal structures and electrical cables, the sensor coordinate frames can drift over time. This thesis describes a new approach, the Anatomical Constraint Method (ACM), for reducing the effect of alignment and calibration errors using an algorithm to find a three degree of freedom correction for each IMU that minimizes the average extent to which the calculated joint angles exceed the expected anatomical range of motion. In addition, the algorithm reduces the effect of IMU drift by computing a correction for overlapping time intervals and using spherical linear interpolation to estimate continuous, time-dependent corrections. When the algorithm is applied to data from a network of IMUs designed to track the crawling movement of infants, results show a substantial improvement in the correlation of the kinematic representation of movement with recorded video images

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Use of Kinematics to Minimize Construction Workers' Risk of Musculoskeletal Injury

Construction work requires more repetitive and highly physical effort than, for example, office work. Despite technological advancements in construction, the human factor is still an essential part of the industry. Hence, the need to maintain a healthy work environment is a shared interest between workers and industry. This thesis addresses the problem of cumulative injuries among construction workers, with emphasis on masons, and examines ways to improve safety and productivity simultaneously. Vision-based motion capture and sensor-based joint angle measurement techniques were tested against a state-of-the-art Optotrak system. Results show that the overall error in joint angle measurements was 10 deg for vision-based approaches compared to 3 deg for optical encoders. Moreover, a noninvasive fatigue detection method was developed by applying time-delay embedding and phase-space warping (PSW) techniques to a single joint angle, exerted force, and electromyography (EMG) data. Results indicate that the method can detect a slowly changing variable, fatigue in a limb, from a single kinematic signal, limb exerted force, or its EMG signals. Furthermore, twenty one masons distributed in four experience categories, ranging from novice to expert, took part in a study to evaluate safety and productivity in masonry work using inertial measurement units (IMUs). The study hypothesized that masons adopt safer and more productive work techniques with experience and that these techniques can be identified and used to train novice workers. Results indicate that journeymen appear to develop more productive and safer work techniques compared to other groups. On the other hand, the three-years experience group was found to sustain the highest joint compression forces and moments. Results also show that a clear distinction exists between expert and inexpert mason motion patterns. Support Vector Machine (SVM) classifiers were able to identify these differences with an accuracy of %92.04 in 13 seconds using a linear kernel. The thesis findings justify exploration of sensor fusion techniques to combine direct and indirect motion capture systems. The findings also suggest that PSW can be used in applications such as rehabilitation to access information about patient status hidden in the full-chain kinematics using a single kinematic signal. Finally, findings show the potential for training apprentices to excel in all three aspects: proficiency, productivity, and ergonomic safety by following the example of experts

Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products