## 2 Related Work
IVAs need to sense the users (i.e., measure a set of physical and behavioral parameters in order to estimate some specific aspects of their state of mind toward the task at hand and (possibly) the environment), with the ultimate objective of improving users' productivity, comfort and satisfaction or, in some cases, to allow them to interact with the environment when other means of interaction are lacking or insufficient [27]. To support such sensing abilities, current technologies exploit cameras, eye-gaze tracking devices, microphones, MEMS (3D acceleration sensors) and, in some cases, heart rate and blood oxygen saturation measurements from smartbands and smartwatches. Video data streams can be processed by AI algorithms to recognize the user (to differentiate between different users) and extract head poses and facial expressions [28] and, in some cases, track the eye gaze instead of using specialized eye-tracking hardware [29], recognize hand gestures and estimate and track full body poses and motions [30] when needed. Current applications of AI-based computer vision, such as person detection, face detection and recognition, gesture recognition and pose detection, have been considered for years to be too demanding for handheld and wearable devices and were initially delegated to remote servers following a cloud-based paradigm. Recent improvements in the computational power of handheld and wearable devices and in the efficiency and reliability of AI algorithms allowed the execution of AI-based applications locally on a mobile device [12; 31]. Local computation in turn reduces the amount of data transmitted from and to the mobile device, thus reducing power consumption and privacy-related risks. Moreover, local computation does not rely on wireless networks, which in some cases may be intrinsically unreliable. In the context depicted, this work describes the architecture of a software platform named TFTH, which is aimed at developing AI-based applications capable of sensing and interacting with users, promoting their autonomy and productivity and running on mobile and handheld devices. Compared with the existing approaches in the literature, the innovative aspects proposed here are summarized as follows:* The proposed solution operates locally on a handheld device and does not require remote (e.g., cloud-based) services for its core functions, such as face recognition, face detection, eye tracking, speech recognition and speech synthesis;
* The proposed solution does not require specialized hardware, such as eye gaze trackers, stereo cameras or dedicated processors (e.g., Intel Movidius) for data analysis. The proposed platform has been used to develop the MICA pilot application, aimed at supporting users to focus their attention on a sustained visual task, such as reading. To assess the effectiveness of the MICA, in this work, we intend to answer the following research question:  
* Does the MICA improve the user's visual attention when reading from a handheld digital device? We adopt a sustained visual attention model based on the assumption that the level of attention on a visual task is proportional to the percentage of time the user is looking at the object of the task. For example, when reading a text, the level of attention to the reading task is proportional to the percentage of time the user spends looking at the text being read.