Afective human robot interaction (HRI) is quite complex since the robot interacts not only with the human but also with the environment. Providing robots with emotional intelligence is critical in this field but also achieving public acceptance and trust from the public when using robots is another challenge. Robots should infer and interpret human emotions and behave in a trusted way ensuring safety. Since afective HRI aims at the system development that use emotions, it requires knowledge from fields like computer science, psychology, and cognitive science. An afective autonomous robot interacts with humans using afective technologies to detect emotions. Despite the fact that a typical robot-platform has embedded several attributes like perception, decisions, and actions it is quite dificult to detect human emotions as well as to behave in a re-assuring manner.
The impact of afective computing and robots can be examined in the context of a smart house application. In a smart house [1] there is interaction with a wide variety of smart devices to robotic mechanisms. Such interactions have altered the objective of the house itself as a prime place to relax and unwind. Adding several smart devices inside our environment without any synchronization between them or planning regarding their integrated use can have a negative impact, manifested mainly as anxiety, stress and even insecurity. On the other hand, a properly scheduled and coordinated environment or, equivalently, a smart house ecosystem can significantly reduce stress and in general contribute to a higher quality of life. This happens only when the individual smart devices of a house ecosystem are working seamlessly and coordinated in the background taking into consideration the house occupants and not vice versa.
occupants of a smart house is that of calmness, defined
as the state of mind having low arousal and valence [2].
Since calmness implies relatively low brain activity, it
can be clearly identified using EEG [ 3, 4, 5] or through
measurements related to ego-sensor data (smartwatch
[6], smartphone [
CEUR
Workshop Proceedings (CEUR-WS.org) ing, light on/of state, loudspeaker music, et al.).
When it comes to afective computing considerations, the principal concern is for designing and building systems and environments where the HRI is smooth and human centered [12]. This includes building machines that can sense and react to human emotions but also to be reassuring, trusted and be considered safe by the public.
Our work presents the creation of an integrated environment that provides the foundation for a Smart house computing experimental platform. The experimental study enhances the frequent operations encountered in a smart house by monitoring its state using a wireless sensor network [13] and mobile robots [14]. This work describes the developed HRI testbed shown in Figure 1, indicating the following technologies that have been integrated to the Smart house platform: • A Media Server attached to a dedicated computer (Intel i7-NUC).
ings. • A supervising Data server (Intel i7-NUC) running Ubuntu 16.04 which infers the human’s calm state based on a 10 second sliding window of EEG read• The human brain activity is measured using an inexpensive yet reliable portable EEG-device. In this study the users’ brain activity is used to validate the efect of various stimuli in a smart home towards the achieved calmness. An Emotiv EPOC+ EEG device [15] that transmits brain signals using Bluetooth to a computer is used. It can measure the brain waves of a human wearing the device and can transmit whether the user’s emotion state.
• A suite of sensors that monitor the environment’s status (sound, CO, humidity, temperature, et.al); these sensors are wirelessly connected to the supervised Data serer.
Moreover, a Google hub device acts as a data query
and actuation server and sends event-like (on/of)
commands to: a) a heat adjustment device (air cooler) for
regulating the temperature, b) smart power outlets that
• A smartwatch (Samsung Galaxy Watch Active 2) connect WiFi RGB-light bulbs and other devices that
running Tizen OS, which measures the heart rate afect the surrounding illuminance, and c) a
Bluetoothof its user every 10 sec. enabled loudspeaker device for playing streaming audio.
• An attention inference device in the form of an Finally, a mobile ground robot (Robotis’ Turtlebot
Android application running on a smartphone 3 [
Odroid XU-4 embedded microcontroller that monitors the power spectrum of the surrounding sound (over a 10 sec sliding window) and wire- 3. Afective computing for robot lessly transmits its normalized values [0 (noise- applications less) up to 1 (loud)] to the server, • A spherical camera (Ricoh Theta V) that streams Humans living in an environment can perform percepvideo at 4K-resolution to the data server; this cam- tual, spatial, motor, and cognitive activities. In real life era is mounted on the mobile robot and monitors the surrounding space. these activities are interleaved creating complex real life situations. We generated several scenarios that consist of diferent combinations of such activities, executed the scenarios in our smart house platform prototype and checked the human reaction using the EEG. Our initial results show that the user’s emotions (calmness) are strongly influenced by the scheduling of the activities.
More experiments need to be conducted to examine how user behaviour is influenced in diferent situations like simultaneous processing of clues, situations with low arousal and high arousal etc.
Several research directions can be followed based on Figure 2: Commercial Social Robots. the above platform. An interesting problem to examine is the use of AI based scheduler trained to the needs of the user. The problem of smart home scheduling has been examined mainly in the context of controlling appliances assessments, or psychometric tests, or ongoing studies for eficient energy consumption [ 19]. involving the Negative Attitudes toward Robots Scale
Social robots, shown in Figure 2 have been used for a (NARS) will be used to evaluate the HRI. Figure 3 indivariety of applications. In [20], the major fields of appli- cates a mobile robot in our smart house that moves away cations for social robotics that include companionship, from the human’s Field of View in order not to afect healthcare, education, are investigated. Furthermore, the NARS. incorporation of social attributes to the HRI under the social efects of these robots are highlighted.
For example in the education field social robots have been introduced for children education. In [21] social robots introduce a new perspective in understanding children learning. Robots are equipped with several sensors and data analysis of the collected data during their interaction with children can provide insights on the learning process. An interesting result on HRI in the case of children is presented in [22] where the authors use a NAO humanoid robot to a handwriting partner to teach children how to write.
In some cases the results of the use of social robots are not so encouraging. Such a case can be seen in [23] the authors examined the literature on using social robots for mental health interventions i.e. for improving depression and concluded that the research results have low internal and external validity. HRIs in social robotics can be remote or proximate. The problem of proximate inter- Figure 3: Robot’s maneuver to decrease NARS actions afects the Traits, Attitudes, Moods and Emotions (TAME) of humans. Examples of proximate activities between humans and robots can be as simple as the handover of an item or as complicated as a joint surgery.
Human expectations and build of trust when considering 4. Conclusions robot errors is of paramount importance as explained in [24]. It is evident that in the field of HRI, there is a challenge
In our ongoing research, we are interested in proxi- that needs to be addressed on how to add characterismate HRI [25], where humans interact with colocated tics and emotional intelligence to machines and environrobots. This interaction afects the sociability because of ments so that the interactions with the humans to be the robot’s functionality. Proximate HRI includes social, intuitive, smooth, natural and trusted. This paper preemotive, and cognitive capabilities of this interaction. sented the development of a platform that encompasses
The robot’s architecture is modified to account for the several application fields and identifies future research underlying afective models. Inhere, the TAME frame- issues related to machines, emotional intelligence and work [26, 27] is adopted to facilitate the overall HRI. Self trust.