SAA, 1st version - Net!Works

eMobility – Strategic Applications AgendaHealth and InclusionApplications include the determination of critical heart conditions, including seizures. A wireless device that isattached to the patient‘s body can automatically send the necessary information to a receiver (for example amonitoring unit in a hospital), that can take immediate action in case of a serious situation. At this moment theapplication is primarily foreseen in post-care situations, although the next goal would be to use the data forprevention purposes.A patient status monitoring module that collects patient data and determines the patient status has to beconsidered in order to utilize context data for system adaptation. The patient status can be determined through anumber of health sensors (e.g. heart rate and body temperature sensors) and corresponding vital signals.Several research topics can be envisaged in this emerging area: information about the context (e.g., patientstate, location) can in fact be exploited for system optimization.As an example, the operation of compression of medical multimedia signals can be performed according to thepatient status, e.g. higher compression can be considered if the patient is in a non-critical status, whereas incritical conditions detailed information need to be transmitted, at the expenses e.g. of redundancy for securitypurposes.Joint compression and coding of different medical signals (e.g. cardiac ultrasonography with ECG and respiratorypattern) and compression taking context into account can be performed; similarly detection of portions of interest(in space and time) in a medical video sequence can be performed with the support of context information(collected e.g. through medical sensors).Data protection, scheduling and security can be also adapted to the context (location and status of the patient).Examples of exploitation of context information in the healthcare area include [61].II.6.2. Content awarenessVideo compression and transmission systems have mostly developed without taking the content of thetransmitted information into account. In recent years, approaches of joint source and channel coding have takeninto account some information about the content of the source [62], such as source sensitivity information (thesensitivity of the different bits representing the source to channel errors) in order to perform unequal errorprotection or the source a-priori information (e.g. statistical information about the source such as the prevalenceof bits equal to one or equal to zero in its binary representation).In the case of telemedical applications, specific application requirements are present, as well as specificcharacteristics of the transmitted data. The characteristics of medical video sequences, both in terms of statisticsand semantic, can be taken into account to envisage medical video specific error resilient tools. In particular,regions of interest (ROI) can be considered for the compression and protection strategy.As a simple example, the region of interest can be compressed without loss of data and/or can be highlyprotected through error correcting codes or prioritization techniques when transmitted.The suitable tools of the recent video coding standards (e.g. the possibility to identify video objects in MPEG-4)can be exploited in order to select and separately manage the regions of interest.The concept of content-awareness is tightly linked to the need to enable an acceptable perceptual quality of theservice.Transmission systems are classically developed with the target of maximizing the data throughput. However, inparticular for multimedia data transmission, the final target is the maximization of end-to-end quality [44] [62].Such quality is usually expressed through a single quality index, representing the distortion between the receivedand the transmitted signals. However, such metrics (as MSE, PSNR) often fail in actually representing the qualityperceived by the user and objective metrics well representing the perceived quality have been developed [64].Such metrics are based on the knowledge of the human visual system (HVS) and are often derived afterextensive subjective quality assessment tests (mean opinion score, MOS).Such novel quality metrics do not always represent a correct index in the case of medical images, since thecontent of the image/video sequence is not considered in the quality assessment procedure.25