The pervasive computing paradigm

The pervasive computing paradigm is the combination of the environmental and wearable computing paradigms, which have typically been combined to overcome technical limitations like reducing the computational complexity, increasing the effectiveness, or resolving privacy issues (Rhodes et al., 1999). In fact, the environmental paradigm tends to have difficulties with privacy and personalization. For personalization, every time a person interacts with a new environment, it is necessary to transfer the personal data to the environmental system. This process is quite annoying and leads to the other problem. Sharing personal data with environmental infrastructure cannot guarantee a correct use of them. On the other hand, the wearable computing paradigm is perfect for personalization. Since the wearable system is always on the user the personla profile never needs to be transferred to a new environment. However, the wearable computing paradigm presents troubles with localized information, localized resource control and resource management between multiple people. The localized information implies that when some information is updated in a local environment, then every wearable system needs to be given the new information and it is not possible to access data networks everywhere, yet. The localized control implies that the wearable system should be able to control resource off the person’s body. This means using the hardware of the wearable device, which has limited capabilities in terms of computational power and energy consumption. That counts also for the resource management when multiple users are involved, in fact sharing information implies an intense hardware load and accessing to the control of the same local resource can create

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conflicts or an inadequate control. A pervasive computing paradigm that properly combines the environmental and wearable computing allows alleviating these issues (Rhodes et al., 1999; Carrino et al., 2011). In fact, the wearable system can contain the personal information allowing the user to access to it without having to enter it in every environmental system. At the same time, the wearable and environmental systems can share the computational load in order to optimize the resource management for an enhanced experience for the control of the localized resecources and without the necessity to update the wearable system in case of changes of the environmental system. Some examples of system using sensors distributed on the user and connected to the smart environment can be found in the literature (Neßelrath et al., 2011; Kivimaki et al., 2013). In this case, the computational part is completely delegated to the smart environment and the personalization is missing. In other systems, the gesture recognition was committed to the wearable device and the activation to the environment (Kühnel et al., 2011; Wu et al., 2010). In these examples, the personalization is maintened and also the local control and information but the computational part is competly demanded on the wearable device, which is not optimal. On all the previously mentioned systems that adopts the pervasive computing paradigm, the smart environment was deprived of sensors that were on the user. In the literature, it is possible to find some examples where the sensors are distributed in both the wearable and environmental systems. In (Budde et al., 2013), the user had to interact with the home appliances pointing at them and give a command. The pointing was detected through a 3D camera and the gesture recognition was managed by the environmental system; at the same time, the command was given through the smartphone. In this example, both the environmental and wearable systems were perceptual and that allowed to alleviate all the issues that usually should affect the two systems separately. However, in that system the interaction was split in two phases, where the first part was executed by the smart environment and the second one by the smartphone. It is possible to distribute sensors both

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on the user and in the environment to recognize the same command at the same time. For instance, in (Wenhui et al., 2009) it was presented a system that used intertial and electromyographic sensors worn by the user, and a camera in the environment to recognize the dynamic gestures performed by the user. This implementation of the pervasive computing paradigm required the development of advanced data fusion techniques. The sensors fusion allowed to achieve gesture recognition accuracies that are quite higher than the accuracies obtained using a single sensor type.

All the aforementioned systems presented in the last subsection show that a proper combination of the wearable and environmental paradigms allow to develop a system that can alleviate weaknesses due ot the adoption of a single paradigm. A system that is developed following the pervasive computing paradigms is composed of a wearable subsystem and an environmental subsystem. However, all the systems presented in this subsection need the simultaneous presence and functioning of the wearable and environmental subsystems limiting the user’s freedom and lacking of computational optimization. Indeed, in the discussed examples of pervasive computing systems if one of the two subsystems stops working, the functioning of the whole system is compromised. The pervasive computing paradigm that requires the simultaneous functioning of the wearable and environmental systems is called “complementary type”. Recent studies tried to introduce smarter architectures for the combination of the wearable and environmental paradigms that do not oblige the system to have a static composition. Roggen et al. presented an opportunistic paradigm for activity recognition that leavarages a system capable of optimizing the recognition methods in order to dynamically adapt to the available sensors data (Roggen et al., 2013). That allows creating reliable gesture and activity recognition applications despite the changing sensors availability. In this thesis, a novel paradigm called “synergistic paradigm” is presented to push further this concept. This paradigm allows developing a system that can dynamically recognize gestures depending on the availability of the wearable and

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environmental subsystems. The wearable and environmental subsystems can function independently but if combined they use a fusion engine, which allows increasing the gesture recognition accuracy. The opportunistic paradigm leverages a system that profits of the advantages coming from both the wearable and environmental subsystems combined and at the same time grants the gesture recognition accuracy being no lower than the best accuracy obtained with the single subsystem.