Multimodality in Group Communication Research

Team interactions are often multisensory, requiring members to pick up on verbal, visual, spatial and body language cues. Multimodal research, research that captures multiple modes of communication such as audio and visual signals, is therefore integral to understanding these multisensory group communication processes. This type of research has gained traction in biomedical engineering and neuroscience, but it is unclear the extent to which communication and management researchers conduct multimodal research. Our study finds that despite its' utility, multimodal research is underutilized in the communication and management literature's. This paper then covers introductory guidelines for creating new multimodal research including considerations for sensors, data integration and ethical considerations.Comment: 27 pages, 3 figure

A portable audio/video recorder for longitudinal study of child development

Collection and analysis of ultra-dense, longitudinal observational data of child behavior in natural, ecologically valid, non-laboratory settings holds significant promise for advancing the understanding of child development and developmental disorders such as autism. To this end, we created the Speechome Recorder - a portable version of the embedded audio/video recording technology originally developed for the Human Speechome Project - to facilitate swift, cost-effective deployment in home environments. Recording child behavior daily in these settings will enable detailed study of developmental trajectories in children from infancy through early childhood, as well as typical and atypical dynamics of communication and social interaction as they evolve over time. Its portability makes possible potentially large-scale comparative study of developmental milestones in both neurotypical and developmentally delayed children. In brief, the Speechome Recorder was designed to reduce cost, complexity, invasiveness and privacy issues associated with naturalistic, longitudinal recordings of child development.National Institutes of Health (U.S.) (Grant R01 2DC007428)Nancy Lurie Marks Family Foundatio

Automated Collection Of Honey Bee Hive Data Using The Raspberry Pi

In recent years beekeepers have faced significant losses to their populations of managed honey bees, a phenomenon known as Colony Collapse Disorder (CCD). Many researchers are studying CCD, attempting to determine its cause and how its effects can be mitigated. Some research efforts have focused on the analysis of bee hive audio and video recordings to better understand the behavior of bees and the health of the hive. To provide data for this research, it is important to have a means of capturing audio, video, and other sensor data, using a system that is reliable, inexpensive, and causes minimal disruption to the bees’ behavior. This thesis details the design and implementation of a data collection system, known as BeeMon, which is based around the Raspberry Pi. This system automatically captures sensor data and sends it to a remote server for analysis. With the ability to operate continuously in an outdoor apiary environment, it allows for constant, near real-time data collection. The results of several years of real world operation are discussed, as well as some research that has used the data collected

Audio-Based Productivity Forecasting of Construction Cyclic Activities

Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most studies with these technologies have aimed to activity identification or to identifying active and idle times. Given that most actions performed with construction machinery involve cyclic activities, cycle time estimation is much more relevant. In this study, hardware and software requirements were optimized toward that goal. This approach had three facets: first, signal spectral analysis was performed through the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) for comparison; second, audio and active sensor data have been submitted to a machine learning framework for activity classification accuracy comparison; and, third, Bayesian statistical models were used to include historical data for cycle time estimation enhancement. As a result, audio signals have been used along with a Markov-chain-based filter to achieve cycle time estimation with an accuracy of over 81% for up to five days of single-machine operation