An In-Depth Investigation of the Effects of Work-Related Factors on the Development of Knee Musculoskeletal Disorders among Construction Roofers

Construction roofers have the uppermost likelihood of developing knee musculoskeletal disorders (MSDs). Roofers spend more than 75% of their total working time being restricted to awkward kneeling postures and repetitive motions in a sloped roof setting. However, the combined effect of knee-straining posture, roof slope and their association to knee MSDs among roofers are still unknown. This dissertation aimed to provide empirical evidence of the effects of two roofing work-related factors namely, roof slope and kneeling working posture, on the development of knee MSDs among construction roofers. These two factors were assessed as potential to increase knee MSD risks in roofing by evaluating the awkward knee rotations and heightened activation of knee postural muscles that might occur in sloped-shingle installation. Moreover, a novel ranking-based ergonomic risk analysis method was developed to identify the riskiest working phase in the sloped-shingle installation operation. In addition, a data fusion method was developed for treating multiple incomplete experimental risk related datasets that would affect the accuracy of risk assessments due to human and technology-induced errors during experimental data collection. The findings revealed that roof slope, working posture and their interaction have significant impacts on developing knee MSDs among roofers. Knees are likely to have increased exposure to MSD risks during placing and nailing shingles on sloped roof surfaces. The established data fusion method has been proven feasible in handling up to 40% missing data in MSD risk-related datasets. The contributions lie in enhanced understanding of the physical risk exposures of roofers\u27 knee MSDs and creation of the ranking-based ergonomic analysis method and the fusion method that will help improve the MSD risk assessment in construction. In the long run, these outcomes will help develop new knee joint biomechanical models, effective interventions, and education and training materials that will improve the workplace to promote health and safety of roofers

Accuracy Assessment and Improvement of FMCW Radar-based Vital Signs Monitoring under Practical Scenarios

Acquisition of human vital signs through radar is a very promising technology that can address the shortcomings of the traditional contact-based measurement devices and enable the move toward a contactless vital monitoring system. This research is focused on monitoring breath rate (BR) and heart rate (HR) via a frequency modulated continuous wave (FMCW) radar. Currently, the two approaches used for BR and HR estimation are filter-based and decomposition-based, such as variational mode decomposition (VMD) for high-quality signal separation. We propose an adaptive VMD (AVMD) to address the problem of setting the number of segmentation levels required by the VMD algorithm. Various experiments are conducted under practical scenarios in terms of distance, angle, posture, and activity as well as the existence of a nearby person and fan. We have made a comprehensive assessment of accuracy change and impact in these scenarios. The experimental results show clearly that the proposed AVMD gives higher accuracy compared to the filter-based and VMD-based. A real-time BR-HR monitoring system using the proposed AVMD and the TI’s IWR1843Boost radar board has been implemented to demonstrate its practical uses

Synergy-Based Human Grasp Representations and Semi-Autonomous Control of Prosthetic Hands

Das sichere und stabile Greifen mit humanoiden Roboterhänden stellt eine große Herausforderung dar. Diese Dissertation befasst sich daher mit der Ableitung von Greifstrategien für Roboterhände aus der Beobachtung menschlichen Greifens. Dabei liegt der Fokus auf der Betrachtung des gesamten Greifvorgangs. Dieser umfasst zum einen die Hand- und Fingertrajektorien während des Greifprozesses und zum anderen die Kontaktpunkte sowie den Kraftverlauf zwischen Hand und Objekt vom ersten Kontakt bis zum statisch stabilen Griff. Es werden nichtlineare posturale Synergien und Kraftsynergien menschlicher Griffe vorgestellt, die die Generierung menschenähnlicher Griffposen und Griffkräfte erlauben. Weiterhin werden Synergieprimitive als adaptierbare Repräsentation menschlicher Greifbewegungen entwickelt. Die beschriebenen, vom Menschen gelernten Greifstrategien werden für die Steuerung robotischer Prothesenhände angewendet. Im Rahmen einer semi-autonomen Steuerung werden menschenähnliche Greifbewegungen situationsgerecht vorgeschlagen und vom Nutzenden der Prothese überwacht

Channel State Information from pure communication to sense and track human motion: A survey

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology

Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals

AbstractThe classification of the bio-signal has been used for various purposes in the literature as they are versatile in diagnosis of anomalies, improvement of overall health and sport performance and creating intuitive human computer interfaces. However, automatic identification of the signal patterns on a streaming real-time signal requires a series of complex procedures. A plethora of heuristic methods, such as neural networks and fuzzy systems, have been proposed as a solution. These methods stipulate certain conditions, such as preconditioning the signals, manual feature selection and large number of training samples.In this study, we introduce a novel variant and application of the Collaborative Representation based Classification (CRC) in spectral domain for recognition of hand gestures using raw surface electromyography (EMG) signals. The CRC based methods do not require large number of training samples for an efficient pattern classification. Additionally, we present a training procedure in which a high end subspace clustering method is employed for clustering the representative samples into their corresponding class labels. Thereby, the need for feature extraction and spotting patterns manually on the training samples is obviated.We presented the intuitive use of spectral features via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set compared to the available methods. The worst recognition result which is the best in the literature is obtained as 97.3% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Predictive text-entry in immersive environments

Virtual Reality (VR) has progressed significantly since its conception, enabling previously impossible applications such as virtual prototyping, telepresence, and augmented reality However, text-entry remains a difficult problem for immersive environments (Bowman et al, 2001b, Mine et al , 1997). Wearing a head-mounted display (HMD) and datagloves affords a wealth of new interaction techniques. However, users no longer have access to traditional input devices such as a keyboard. Although VR allows for more natural interfaces, there is still a need for simple, yet effective, data-entry techniques. Examples include communicating in a collaborative environment, accessing system commands, or leaving an annotation for a designer m an architectural walkthrough (Bowman et al, 2001b). This thesis presents the design, implementation, and evaluation of a predictive text-entry technique for immersive environments which combines 5DT datagloves, a graphically represented keyboard, and a predictive spelling paradigm. It evaluates the fundamental factors affecting the use of such a technique. These include keyboard layout, prediction accuracy, gesture recognition, and interaction techniques. Finally, it details the results of user experiments, and provides a set of recommendations for the future use of such a technique in immersive environments

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Evaluation of Concavity Compression Mechanism as a Possible Predictor of Shoulder Muscle Fatigue

This study examined the lived experiences of American Muslim principals who serve in public schools post-9/11 to determine whether global events, political discourse, and the media coverage of Islam and Muslims have affected their leadership and spirituality. The aim of the study was to allow researchers and educators to gain an understanding of the adversities that American Muslims principals have experienced post-9/11 and to determine how to address these adversities, particularly through decisions about educational policy and district leadership. A total of 14 American Muslim school leaders who work in public schools post-9/11 across the United States participated in the study, and a phenomenological methodology was used to direct the data collection and coding. Edelman\u27s political spectacle theory served as the theoretical framework for the research. The findings yielded six themes of political climate, role of the media, inferior and foreign: being seen as the other, unconscious fear, spirituality, and education and communication over spectacle. Further, collective guilt and social responsibility emerged as two additional findings. The research suggests that political spectacle and its effects have a large impact on the lives of American Muslim principals, particularly in regard to their leadership and spirituality