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Many real-life scenarios can benefit from both physical proximity and natural gesture interaction. In this paper, we explore shared collocated interactions on unmodified wearable devices. We introduce an interaction technique which enables a small group of people to interact using natural gestures. The proximity of users and devices is detected thr...
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... However, the implementation of this framework requires a hardware architecture based on fixed devices (e.g., a Kinect Depth sensor and a client-server architecture) for allowing the server to process the proxemic information from appliances. This solution does not offer the mobility and portability required for implementing proxemic interactions on mobile devices or wearable technologies [6]. With the current Proximity Toolkit version, it is not possible to obtain proxemic information from the new smartphone's sensors capabilities and ensuring that users can use proxemic mobile applications on their smartphones in any place. ...
... Figure 3.5 shows the architecture of Vicon motion capturing system. The movement also allows gesture recognition through smartphones or wearable technologies by employing Mobile Sensors [6,43], such as accelerometer, gyroscope, and magnetometer which are generally incorporated in modern mobile devices . Orientation is defined in Chapter 2.3.6. ...
... 6 shows FAMA's code which is based on AlteBeacon protocol for both create alert in proxemic interaction zones and obtain emergency identification. ...
Smart environments are currently overpowering traditional Human-Computer Interaction (HCI) approaches for people’s everyday life applications. Proxemic interaction is an emerging area for improving HCI experiences in such environments, causing the so-called proxemic environments to emerge. Proxemic interactions establish how the five dimensions (i.e., Distance, Identity, Location, Movement, and Orientation – DILMO) can be used to implement interactions between people and digital devices. Current studies in this area are focused on developing proxemic applications with a specific task and toolkits that allow developers to obtain DILMO information from a wide range of sensors. However, there exists a notable lack of general approaches capable of supporting the whole implementation process from the modelling of proxemic environments to represent general proxemics behaviours and finalizing with the development of mobile applications. To help the integration of proxemic capabilities in HCI, we propose an approach for modelling proxemic environments based on a graphical Domain-Specific Language (DSL). The DSL allows designers to express proxemic interactions for modelling proxemic environments and supports the development process.We also provide an API in a framework for mobile devices based on Android operating system. This API implements high-level primitives (DILMO) permitting to provide proxemic interactions on widespread mobile devices. We have developed an API that is feasible for developing proxemic mobile applications.We applied the proxemic interactions in order to develop an architecture that can support mobile applications in the health sector. We propose interpersonal distances and proxemic dimensions (i.e., Distance, Identity, and Orientation - DIMO) to implement HCI with mobile devices that encourage touchless interactions. Our goal was to promote mobile apps’ development with proxemic HCI, supported in a proposed architecture, to stop the spreading of nosocomial infection. To illustrate our proposal’s usability, we developed two prototypes of applications for mobile devices as a proof-of-concept, using several combinations of proxemic DILMO dimensions to model proxemics HCI that allowed flexible interaction between people and mobile devices.
... In this context, solutions such as Toolkit [7] and ProximiThings [8](for proxemic interaction in the Internet of Things) have been proposed to support the development of proxemic interaction. However, existing tools and frameworks present limitations for implementing proxemic interaction in mobile technologies because they require special hardware devices connected to the system (e.g., a Kinect Depth sensor, which must be installed on a PC for sensing proxemic information). ...
... Velocity changes are calculated in order to respond to the user's behavior. The movement also allow gesture recognition through smartphones or wearable technologies by employing motion sensors [8]. FAMA, a first aid mobile app, identifies potential rescuers as they move towards the injured person's proxemic zones [24]. ...
... Table 1 summarizes the sensors used by previous proposals to obtain proxemic information. Vicon/OptiTrac Motion Capture [3,11,18,25,29,31] Leap Motion [12] LV-MaxSonar-EZ1 [14] SHARP GP2Y0A02YK0F [32] Mobile Sensors (accelerometer, gyroscope, magnetometer) [2,8,30] Bluetooth BLE [2,20,21,22,24] Wi-fi RSSI signal [26] Mobile computer vision [17] Computer vision is frequently employed to obtain almost the whole DILMO proxemic dimensions using a Kinect depth camera. Kinect depth camera is powerful and low cost sensor that is frequently used in previous proposal for sensing proxemic interaction. ...
Traditional Human-Computer Interaction (HCI) is being overpowered by the widespread diffusion of smart and mobile devices. Currently, smart environments involve daily day activities covered by a huge variety of applications, which demand new HCI approaches. In this context, proxemic interaction, derived from the proxemic theory, becomes an influential approach to implement new kind of Mobile Human-Computer Interaction (MobileHCI) in smart environments. It is based on five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO). However, there is a lack of general and flexible tools and utilities focused on supporting the development of mobile proxemic applications. To respond to this need, we have previously proposed a framework for the design and implementation of proxemic applications for smart environments, whose devices interactions are defined in terms of DILMO dimensions. In this work, we extend this framework by integrating a Domain Specif Language (DSL) to support the designing phase. The framework also provides an API, that allows developers to simplify the process of proxemic information sensing (i.e., detection of DILMO dimensions) with mobile phones and wearable sensors. We perform an exhaustive revision of relevant and recent studies and describe in detail all components of our framework.
... Velocity changes are calculated in order to respond to the user's behavior. The movement also allow gesture recognition through smartphones or wearable technologies by employing motion sensors [2]. FAMA, a first aid mobile app, identifies potential rescuers as they move towards the injured person's proxemic zones [29]. ...
... However, the implementation of this framework requires a hardware architecture based on fixed devices (e.g., a Kinect Depth sensor and a client-server architecture) for allowing the server to process the proxemic information from appliances. This solution does not offer the mobility and portability required for implementing proxemic interactions on mobile devices or wearable technologies [2]. With the current Proximity Toolkit version, it is not possible to obtain proxemic information from the new smartphone's sensors capabilities and ensuring that users can use proxemic mobile applications on their smartphones in any place. ...
Understanding traditional culture is important. Various methods are used to achieve better cross-cultural understanding, and certain researchers have studied human behavior. However, behavior does not always represent a culture. Therefore, our study aims to understand Japanese greeting culture by classifying it through machine learning. Following are our study contributions. (1) The first study to analyze cultural differences in greeting gestures based on the politeness level of Japanese people by classifying them. (2) Classify Japanese greeting gestures eshaku, keirei, saikeirei, and waving hand. (3) Analyze the performance results of machine and deep learning. Our study noted that bowing and waving were the behaviors that could symbolize the culture in Japan. In conclusion, first, this is the first study to analyze the eshaku, keirei, saikeirei, and waving hand greeting gestures. Second, this study complements several human activity recognition studies that have been conducted but do not focus on behavior representing a culture. Third, according to our analysis, by using a small dataset, SVM and CNN methods provide better results than k -nearest neighbors ( k -NN) with Euclidean distance, k -NN with DTW, logistic regression and LightGBM in classifying greeting gestures eshaku, keirei, saikeirei, and waving hand. In the future, we will investigate other behaviors from different perspectives using another method to understand cultural differences.
