The Internet of Things (IoT) is the logical further development of today's Internet. Technological advancements lead to smart objects being capable of identifying, locating, sensing and connecting and thus leading to new forms of communication between people and things and things themselves. Ambient Assisted Living (AAL) encompasses technical systems to support elderly people in their daily routine to allow an independent and safe lifestyle as long as possible. Keep In Touch (KIT) uses smart objects and technologies (Near Field Communication and Radio Frequency Identification) to facilitate telemonitoring processes. Closed Loop Healthcare Services take use of KIT technology and are capable of processing relevant data and establishing communication channels between elderly people and their environment and different groups of care-givers (physicians, relatives, mobile care providers). The combination of KIT technology (smart objects) and Closed Loop Healthcare Services results in an applied IoT infrastructure for AAL scenarios. Already applied IoT and AAL applications in telemonitoring and medication intake compliance projects show that these applications are useful and accepted by the elderly and that the developed infrastructure enables a new form of communication between people and people, people-to-people (P2P) communication. The personal communication between elderly people, their environment and relevant groups of care givers is an important aspect in AAL. Through the combination of KIT and Closed Loop Healthcare, a central AAL paradigm can be realized through the IoT, where the elderly live in their homes with smart objects, thus smart homes, communicating to the outside world in an intelligent and goal-orientated manner.

Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available.

Utilization of information and communication technologies such as mobile phones and wireless sensor networks becomes more and more common in the field of telemonitoring for chronic diseases. Providing elderly people with a mobile-phone-based patient terminal requires a barrier-free design of the overall user interface including the setup of wireless communication links to sensor devices. To easily manage the connection between a mobile phone and wireless sensor devices, a concept based on the combination of Bluetooth and near-field communication technology has been developed. It allows us initiating communication between two devices just by bringing them close together for a few seconds without manually configuring the communication link. This concept has been piloted with a sensor device and evaluated in terms of usability and feasibility. Results indicate that this solution has the potential to simplify the handling of wireless sensor networks for people with limited technical skills.

Patients with psoriasis who are being treated with biologics require intensive monitoring. However, the monitoring tool teledermatology is n...

Life expectancy is rising in most parts of the world as is the prevalence of chronic diseases. Suboptimal adherence to long‐term medications is still rather the norm than the exception, although it is well known that suboptimal adherence compromises the therapeutic effectiveness. Information and communications technology provides new concepts for improving adherence to medications. These so‐called telehealth concepts or services help to implement closed‐loop healthcare paradigms and to establish collaborative care networks involving all stakeholders relevant to optimising the overall medication therapy. Together with data from Electronic Health Records and Electronic Medical Records, these networks pave the way to data‐driven decision support systems. Recent advances in machine learning, predictive analytics, and artificial intelligence allow further steps towards fully autonomous telehealth systems. This might bring advances in the future: disburden healthcare professionals from repetitive tasks, enable them to timely react to critical situations, and offer a comprehensive overview of the patients' medication status. Advanced analytics can help to assess whether patients have taken their medications as prescribed, to improve adherence via automatic reminders. Ultimately, all relevant data sources need to be collated into a basis for data‐driven methods, with the goal to assist healthcare professionals in guiding patients to obtain the best possible health status, with a reasonable resource utilisation and a risk‐adjusted safety and privacy approach. This paper summarises the state‐of‐the‐art of telehealth and artificial intelligence applications in medication management. It focuses on 3 major aspects: latest technologies, current applications, and patient related issues.

Poor patients' adherence to intake of prescribed medication has been identified as a serious problem in the treatment of chronically ill patients. Technical solutions are needed to measure and - if necessary - to increase the patients' adherence. A telemonitoring solution was developed to record a patient's medication intake based on smart blisters and mobile phones with NFC functionality. The components allowed recording of drug type, timestamp, and dosage of pills taken. The system's usability and technical feasibility was evaluated in the course of an application study. Over a period of 13 months 59 patients suffering from diabetes were monitored. 1,760 blisters were handed out to these patients and 14,843 takeout events were recorded and transmitted via mobile phone. Results indicate the feasibility of this concept to monitor adherence. Although the system still needs to be optimized for routine use it shows the potential for targeting the problem of poor patient adherence by NFC enabled devices.