Search for: All records

In multiplayer collaborative games, players need to coordinate their actions and synchronize their efforts effectively to succeed as a team; thus, individual differences can impact teamwork and gameplay. This article investigates the effects of cognitive styles on teams engaged in collaborative gaming activities. Fifty-four individuals took part in a mixed-methods user study; they were classified as field-dependent (FD) or independent (FI) based on a field-dependent–independent (FD-I) cognitive-style-elicitation instrument. Three groups of teams were formed, based on the cognitive style of each team member: FD-FD, FD-FI, and FI-FI. We examined collaborative gameplay in terms of team performance, cognitive load, communication, and player experience. The analysis revealed that FD-I cognitive style affected the performance and mental load of teams. We expect the findings to provide useful insights on understanding how cognitive styles influence collaborative gameplay. 

Global investments in artificial intelligence (AI) and robotics are on the rise, with the results to impact global economies, security, safety, and human well-being. The most heralded advances in this space are more often about the technologies that are capable of disrupting business-as-usual than they are about innovation that advances or supports a global workforce. The Future of Work at the Human-Technology Frontier is one of NSF’s 10 Big Ideas for research advancement. This panel discussion focuses on the barriers and opportunities for a future of human and AI/robot teaming, with people at the center of complex systems that provide social, ethical, and economic value. 

Gesture recognition devices provide a new means for natural human-computer interaction. However, when selecting these devices to be used in games, designers might find it challenging to decide which gesture recognition device will work best. In the present research, we compare three vision-based, hand-gesture devices: Leap Motion, Microsoft’s Kinect, and Intel’s RealSense. The comparison provides game designers with an understanding of the main factors to consider when selecting these devices and how to design games that use them. We developed a simple hand-gesture-based game to evaluate performance, cognitive demand, comfort, and player experience of using these gesture devices. We found that participants preferred and performed much better using Leap Motion and Kinect compared to using RealSense. Leap Motion also outperformed or was equivalent to Kinect. These findings were supported by players’ accounts of their experiences using these gesture devices. Based on these findings, we discuss how such devices can be used by game designers and provide them with a set of design cautions that provide insights into the design of gesture-based games. 

Composite wearable computers combine multiple wearable devices to form a cohesive whole. Designing these complex systems and integrating devices to effectively leverage their affordances is nontrivial. To inform the design of composite wearable computers, we undertook a grounded theory analysis of 84 wearable input devices drawing from 197 data sources, including technical specifications, research papers, and instructional videos. The resulting prescriptive design framework consists of four axes: type of interactivity, associated output modalities, mobility, and body location. This framework informs a composition-based approach to the design of wearable computers, enabling designers to identify which devices fill particular user needs and design constraints. Using this framework, designers can understand the relationship between the wearable, the user, and the environment, identify limitations in available wearable devices, and gain insights into how to address design challenges developers will likely encounter. 

Gesture recognition devices provide a new means for natural human-computer interaction. However, when selecting these devices for games, designers might find it challenging to decide which gesture recognition device will work best. In the present research, we compare three vision-based, hand gesture devices: Leap Motion, Microsoft's Kinect, and Intel's RealSense. We developed a simple hand-gesture based game to evaluate performance, cognitive demand, comfort, and player experience of using these gesture devices. We found that participants' preferred and performed much better using Leap Motion and Kinect compared to using RealSense. Leap Motion also outperformed or was equivalent to Kinect. These findings suggest that not all gesture recognition devices can be suitable for games and that designers need to make better decisions when selecting gesture recognition devices and designing gesture based games to insure the usability, accuracy, and comfort of such games. 

Composite wearable computers consist of multiple wearable devices connected together and working as a cohesive whole. These composite wearable computers are promising for augmenting our interaction with the physical, virtual, and mixed play spaces (e.g., mixed reality games). Yet little research has directly addressed how mixed reality system designers can select wearable input devices and how these devices can be assembled together to form a cohesive wearable computer. We present an initial taxonomy of wearable input devices to aid designers in deciding which devices to select and assemble together to support different mixed reality systems. We undertook a grounded theory analysis of 84 different wearable input devices resulting in a design taxonomy for composite wearable computers. The taxonomy consists of two axes: TYPE OF INTERACTIVITY and BODY LOCATION. These axes enable designers to identify which devices fill particular needs in the system development process and how these devices can be assembled together to form a cohesive wearable computer. 

Search and rescue (SAR), performed to locate and save victims in disaster and other scenarios, primarily involves collaborative sensemaking and planning. To become a SAR responder, students learn to search within and navigate the environment, make sense of situations, and collaboratively plan operations. In this study, we synthesize data from four sources: (1) semi-structured interviews with experienced SAR professionals; (2) online surveys of SAR professionals; (3) analysis of documentation and artifacts from SAR operations on the 2017 hurricanes Harvey and Maria; and (4) first-person experience undertaking SAR training. Drawing on activity theory, we develop an understanding of current SAR sensemaking and planning activities, which help explore unforeseen factors that are relevant to the design of training systems. We derive initial design implications for systems that teach SAR responders to deal with mapping in the outdoors, collecting data, sharing information, and collaboratively planning activities.