Providing feedback mainly involves presenting visual or auditory cues to the user. These cues need to be perceived and understood and then the user is capable of reacting to them. While for most information retrieval tasks this works fine, several other tasks can be realized implicitly. Thus, the user just passively is involved in the interaction. One of the key technologies for allowing implicit feedback is electrical muscle stimulation (EMS). EMS allows computing systems to actuate parts of the user`s body.
Pedestrian navigation systems require users to perceive, interpret, and react to navigation information. This can tax cognition as navigation information competes with information from the real world. We propose actuated navigation, anew kind of pedestrian navigation in which the user does not need to attend to the navigation task at all. An actuation signal is directly sent to the human motor system to influence walking direction. To achieve this goal we stimulate the sartorius muscle using electrical muscle stimulation. The rotation occurs during the swing phase of the leg and can easily be counteracted. The user therefore stays in control. We discuss the properties of actuated navigation and present a lab study on identifying basic parameters of the technique as well as an outdoor study in a park. The results show that our approach changes a user’s walking direction by about 16°/m on average and that the system can successfully steer users in a park with crowded areas, distractions, obstacles, and uneven ground.
The human body reveals emotional and bodily states through measurable signals, such as body language and electroencephalography. However, such manifestations are difficult to communicate to others remotely. We propose EmotionActuator, a proof-of-concept system to investigate the transmission of emotional states in which the recipient performs emotional gestures to understand and interpret the state of the sender. We call this kind of communication embodied emotional feedback, and present a prototype implementation. To realize our concept we chose four emotional states: amused, sad, angry, and neutral. We designed EmotionActuator through a series of studies to assess emotional classification via EEG, and create an EMS gesture set by comparing composed gestures from the literature to sign-language gestures. Through a final study with the end-to-end prototype interviews revealed that participants like implicit sharing of emotions and find the embodied output to be immersive, but want to have control over shared emotions and with whom.This work contributes a proof of concept system and set of design recommendations for designing embodied emotional feedback systems.