This paper addresses contact sensing, i.e. the problem of resolving the location of a contact, the force at the interface and the moment about the contact normals. Called "intrinsic'' contact sensing for the use of internal force and torque measurements, this method allows for practical devices which provide simple, relevant contact information in practical robotic applications. Such sensors have been used in conjunction with robot hands to identify objects, determine surface friction, detect slip, augment grasp stability, measure object mass, probe surfaces, control collision and a variety of other useful tasks. This paper describes the theoretical basis for their operation and provides a framework for future device design.

As transportation infrastructure continues to age, new methods of non-contact monitoring should be evaluated and, if found suitable, employed for bridge monitoring and structural health assessment. This study highlights the use of infrasound monitoring, a geophysical technique utilizing acoustics below 20 Hz, as one possible solution for non-contact, non-line-of-sight infrastructure health monitoring. This dissertation focuses on the technique of infrasound for infrastructure monitoring (bridges are of primary interest) beginning with a literature review and an overview of current operational considerations for infrasound for infrastructure monitoring developed at the U.S. Army Engineer Research and Development Center. A meta-analysis of bridge vibrational characteristics was completed following identification of a gap in the knowledge base in this area. This completed meta-analysis compared vibrational characteristics across multiple bridge types and construction materials to determine applicability of infrasound for detection and monitoring of each bridge type. With these considerations in mind, an experimental series involving a steel, two-girder bridge in northern California was completed using infrasound to detect natural modes of the structure and validated by on-structure accelerometers. The non-contact nature of this structural assessment approach has potential to supplement traditional structural assessment techniques as affordable, remote, persistent monitoring of transportation infrastructure. Upon completion of the original experimental series, the data were used to investigate the possibility of wide area monitoring using infrasound, including possible limitations and boundaries. Overall implications for use of this technology are also discussed for a multiple infrastructure types.

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.