The goal of this article is to investigate the use of communication robots as tools to increase the safety of first responders (specifically, law enforcement officers (LEOs)) during social interactions or first-response situations. This article functions as the first stage of our investigation into communication robots for use by first responders. The LEOs in our study were provided with a mobile communication robot to determine if such robots could be of use in the public domain for the benefit of both civilians and LEOs. As part of our study, LEOs participated in training sessions where they worked with nonweaponized teleoperated communication robots and completed pretest-posttest surveys. The surveys captured LEOs’ perceptions and attitudes towards the robot and examined the impact of robot design features on LEOs’ acceptance of the communication robot. This article discusses the significance and potential use of communication robots as tools in the future of law enforcement. Our future works include conducting additional studies involving affected stakeholders (e.g., citizen communities) and assessing/mitigating any potential negative impact of communication robots.

To assist and protect citizen communities and police officers, robots have been developed for situational responses (e.g., explosive ordinance disposal). However, the robots used by law enforcement are typically expensive, can be difficult to operate, and do not readily facilitate communication between individuals. Recent research by the authors examined how communication impacts trust between robots and humans in the context of law enforcement. Using a mobile communication robot, law enforcement officers (LEOs) reported high levels of trust because the robot provided near face-to-face interaction using screens, microphones, and speakers. This paper seeks to expand upon the previous findings by discussing public perception, privacy, safety, and ethical considerations as they pertain to communication robots utilized in law enforcement. In the following, the authors explain their primary research thrusts and provide their plan for expanded stakeholder involvement for future research. For the future work, the authors will work with stakeholders to develop ethically grounded communication robots and accompanying education programs that enhance communication, trust, transparency, and accessibility between LEOs and citizens communities.

The past decade has seen great advancements in speech recognition for control of interactive devices, personal assistants, and computer interfaces. However, deaf and hard-of-hearing (HoH) individuals, whose primary mode of communication is sign language, cannot use voice-controlled interfaces. Although there has been significant work in video-based sign language recognition, video is not effective in the dark and has raised privacy concerns in the deaf community when used in the context of human ambient intelligence. RF sensors have been recently proposed as a new modality that can be effective under the circumstances where video is not. This article considers the problem of recognizing a trigger sign (wake word) in the context of daily living, where gross motor activities are interwoven with signing sequences. The proposed approach exploits multiple RF data domain representations (time-frequency, range-Doppler, and range-angle) for sequential classification of mixed motion data streams. The recognition accuracy of signs with varying kinematic properties is compared and used to make recommendations on appropriate trigger sign selection for RF-sensor-based user interfaces. The proposed approach achieves a trigger sign detection rate of 98.9% and a classification accuracy of 92% for 15 ASL words and three gross motor activities.

Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hard-of-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, 0.93 trigger recognition rate.

Recent advances in robotics have accelerated their widespread use in nontraditional domains such as law enforcement. The inclusion of robotics allows for the introduction of time and space in dangerous situations, and protects law enforcement officers (LEOs) from the many potentially dangerous situations they encounter. In this paper, a teleoperated robot prototype was designed and tested to allow LEOs to remotely and transparently communicate and interact with others. The robot featured near face-to-face interactivity and accuracy across multiple verbal and non-verbal modes using screens, microphones, and speakers. In cooperation with multiple law enforcement agencies, results are presented on this dynamic and integrative teleoperated communicative robot platform in terms of attitudes towards robots, trust in robot operation, and trust in human-robot-human interaction and communication.