The main objective of this activity was to develop an European online PHR database system as an alternative to the already existing US-based ones from Google (Google Health) and Microsoft (MS Health Vault) with advanced functionalities and fully compliant with European standards. Such a system complying with the latest HL7 standard would allow seamless integration with hospital and private medical assistance service systems. Having WSDL web services as the main programming interface, it simplifies and enables the development of secure third party systems, services and applications built upon the core PHR database. A significant innovation is the API programming layer and similar scripting device integration interface allowing to avoid pre-installation of the device support for each medical devices within the device layer of the core system before users can use the device to upload measurement data to their PHR account. Furthermore, the PHR therapy definition has been extended to include physical training/exercises prescribed by the physician in the same way that other medicines are prescribed. The added advantage of such an approach is to provide data to physicians for assessing the effectiveness of the exercises against the changing patient’s condition. In order to do this reliably, the correctness of performing such exercises needs to be carefully monitored against predefined templates (temporal and spatial record of body movements and their frequency/retentiveness, treated each as a different therapy type).

State-of-the-Art: EHR platforms have been developed through various European activities, ranging from LinkCare platform promoted by the LinkCare Alliance [9] or the intLIFE PHR platform [10] from Intracom, developed alongside the ICT-PSP-NEXES project [11]. The popularity of EHR/PHR systems has given rise to the development of open-source platform too [12–14]. Their capabilities and features can closely compete with commercial and proprietary implementation, except when it comes to interoperability and flexibility in developing add-on services and applications.

In order to enable the tracking, the correctness of performed exercises automatically without the constant assistance of the physicians, an automated means of tracking and comparing patient’s body movement against correct ones (templates) has been developed. Although many methods are in existence, most of them employ elaborate sets of wearable sensors and/or costly visual observations. In our project, we adopted initially visual-light scanning methods allowing computationally effective and cheap dynamic 3D object (in our case patient’s body) tracing. The idea was similar to laser scanning employed in the Kinect game interfaces for Xbox (now also available for Windows operating systems on PCs), but using specially designed images projected on the person. The part of the R&D work was to determine if shifting to light wavelengths just beyond visible spectrum (infrared and ultraviolet) would allow achieving necessary accuracy and performance. Considering extremely small sizes of such sensors (less than 5 × 5 mm each) a development of lightweight wireless energy-autonomous (employing energy harvesting technologies) were possible, leading to the target sensor node form factor of a small sticker. Considering a vast amount of products appearing in the recent years, the Strokeback project has also taken advantage of such products, among them the Leap Motion IR sensor [15], Emotiv EPOCH EEG sensor [16], Myo EMG sensor [17], etc.

State-of-the-Art: The need to monitor movement and activity both within and outside the laboratory has been the focus of considerable research over the last decade. Body-worn sensors such as goniometers [18], pressure tubes [19], gyroscopes [20] and accelerometers [21–24] have been used in a range of activities and clinical conditions [22, 24–29] and for control of Functional Electrical Stimulation (FES) in walking and upper limb movement [29–31] and in standing with spinal cord injured patients [32–34]. However, body-worn sensors are often bulky and require associated instrumentation and accurate placement of the sensor elements. Nevertheless, such studies have already demonstrated the feasibility and potential benefits of long-term monitoring of movement [35, 36] and what is needed now is a shift in approach to make this feasible and usable in clinical practice and particularly in the home environment for rehabilitation. Such an approach has already been embraced by the textile industry. The so-called smart textiles have been developed to measure vital signs, such as the Electrocardiogram (ECG), and have even been commercialised in, for example, the ProArmBand from SenseWear™ [37] or various systems from Vionetics [38]. Predictions are that wearable systems become a part of routine clinical investigations [39]. Movement and muscle activity are, however, more challenging than vital signs and, in addition, processing, storing, transmitting and using the data poses an even greater challenge. The SMART project (funded by the UK Engineering and Physical Sciences Research Council through the EQUAL initiative) has explored how technology can be used in the home to support rehabilitation following stroke. Devices have been developed that are attached to the wrist and upper arm using clothing resembling sportswear to give a measure of movements made by the patient in activities prescribed by the therapist. Data are recorded and displayed on a computer or TV screen. Joint angles have also been measured using conductive fibres integrated into fabric which change their resistance due to bending as the joint angle varies [32] and by covering the fabric with an electrically conductive elastomer with piezo-resistive properties so that when deformed acts as a strain sensor able to detect movement [40].

Muscle activity poses problems for measurement since it has been well known for many years [41] that the EMG reflects effort rather than output and so becomes an unreliable indicator of muscle force as the muscle becomes fatigued. Consequently measurement of force, in addition to the EMG activity, would be a considerable step forward in assessing the effectiveness of rehabilitation strategies and could not only indicate that fatigue is occurring, but also whether the mechanism is central or peripheral in origin [42]. Similarly, conventional surface EMG measurement requires accurate placement of the sensor over the target muscle, which would be inappropriate for a sensor system integrated within a garment for home use. Electrode arrays are, however, now being developed for EMG measurement and signal processing is used to optimise the signal obtained.

Some of the building blocks for a body sensor network of the form proposed are therefore already in place. Existing commercial systems provide basic information about activity such as speed and direction of movement and postures. Providing precise information about performance, for example, relating movement to muscle activity in a given task and detecting deviations from normal, expected patterns or subtle changes associated with recovery, requires a much higher level of sophistication of data acquisition and processing and interpretation. The challenge is therefore to design and develop an integrated multimodal system along with high-level signal processing techniques and optimisation of the data extracted. The Kinect sensor has potential for use in haptic interfacing [43] and software support has already been offered, while by Open Kinect Community [44] and the official Kinect SDK programming support offered by Microsoft [45].

Beyond State-of-the-Art: The existing techniques for taking measurements on the human body are generally considered to be adequate for the purpose but are often bulky in nature and cumbersome to mount, e.g. electro-goniometers, and they can also be expensive to implement, e.g. Vicon camera system [46]. Their ability to be used in a home environment is therefore very limited. This proposal addresses these deficiencies by extending the state-of-the-art in the following areas:

The central aspect of the BAN is the data processing and handling. This is realised by the processing logic that is physically represented by the distributed system of nodes interconnected by (wireless) network means. All the features of this underlying system, like data handling middleware, compression, data aggregation or fusion, networking protocols and hardware architecture, help reaching the goals set by the primary aspect on different layers. Due to the small size of the network there is not really a need for sophisticated routing mechanisms. Thus, the MAC protocol becomes the central part of the networking.

State-of-the-Art: The wireless medium can be multiplexed in code (CDMA), space (SDMA), time (TDMA) and frequency (FDMA). Time multiplexing is best suited to be used for coordination of the communication between nodes in a single BAN. The main MAC protocols for BANs use TDMA and the access is random [47], contention-based [48–51] and or scheduled [52–67]. The schedule of the access can also be created as a result of a contention phase. The main advantages of schedule-based protocols is that they provide more predictable communication latency important for Quality of Service (QoS) and allow duty-cycling, i.e. saving energy by switching the radio off while there is no communication planned for a particular node.