Ambient Assisted Living (AAL) is an emerging multi-disciplinary field aiming at exploiting information and communication technologies in personal healthcare and telehealth systems for countering the effects of growing elderly population. AAL systems are developed for personalized, adaptive, and anticipatory requirements, necessitating high quality-of-service to achieve interoperability, usability, security, and accuracy. The aim of this paper is to provide a comprehensive review of the AAL field with a focus on healthcare frameworks, platforms, standards, and quality attributes. To achieve this, we conducted a literature survey of state-of-the-art AAL frameworks, systems and platforms to identify the essential aspects of AAL systems and investigate the critical issues from the design, technology, quality-of-service, and user experience perspectives. In addition, we conducted an email-based survey for collecting usage data and current status of contemporary AAL systems. We found that most AAL systems are confined to a limited set of features ignoring many of the essential AAL system aspects. Standards and technologies are used in a limited and isolated manner, while quality attributes are often addressed insufficiently. In conclusion, we found that more inter-organizational collaboration, user-centered studies, increased standardization efforts, and a focus on open systems is needed to achieve more interoperable and synergetic AAL solutions.

Background: Pedometers are considered desirable devices for monitoring physical activity. Two population groups of interest include patients having undergone surgery in the lower extremities or who are otherwise weakened through disease, medical treatment, or surgery procedures, as well as the slow walking senior population. For these population groups, pedometers must be able to perform reliably and accurately at slow walking speeds. The objectives of this study were to evaluate the step count accuracy of three commercially available pedometers, the Yamax (Tokyo, Japan) Digi-Walker ^® SW-200 (YM), the Omron (Kyoto, Japan) HJ-720 (OM), and the Fitbit (San Francisco, CA) Zip (FB), at slow walking speeds, specifically at 1, 2, and 3 km/h, and to raise awareness of the necessity of focusing research on step-counting devices and algorithms for slow walking populations. Materials and Methods: Fourteen participants 29.93 ±4.93 years of age were requested to walk on a treadmill at the three specified speeds, in four trials of 100 steps each. The devices were worn by the participants on the waist belt. The pedometer counts were recorded, and the error percentage was calculated. Results: The error rate of all three evaluated pedometers decreased with the increase of speed: at 1 km/h the error rates varied from 87.11% (YM) to 95.98% (FB), at 2 km/h the error rates varied from 17.27% (FB) to 46.46% (YM), and at 3 km/h the error rates varied from 22.46% (YM) to a slight overcount of 0.70% (FB). Conclusions: It was observed that all the evaluated devices have high error rates at 1 km/h and mixed error rates at 2 km/h, and at 3 km/h the error rates are the smallest of the three assessed speeds, with the OM and the FB having a slight overcount. These results show that research on pedometers' software and hardware should focus more on accurate step detection at slow walking speeds.

Population aging and increasing pressure on health systems are two issues that demand solutions. Involving and empowering citizens as active managers of their health represents a desirable shift from the current culture mainly focused on treatment of disease, to one also focused on continuous health management and well-being. Current developments in technological areas such as the Internet of Things (IoT), lead to new technological solutions that can aid this shift in the healthcare sector. This study presents the design, development, implementation and evaluation of a platform called Common Recognition and Identification Platform (CRIP), a part of the CareStore project, which aims at supporting caregivers and citizens to manage health routines in a seamless way. Specifically, the CRIP offers sensor-based support for seamless identification of users and health devices. A set of initial requirements was defined with a focus on usability limitations and current sensor technologies. The CRIP was designed and implemented using several technologies that enable seamless integration and interaction of sensors and people, namely Near Field Communication and fingerprint biometrics for identification and authentication, Bluetooth for communication with health devices and web services for wider integration with other platforms. Two CRIP prototypes were implemented and evaluated in laboratory during a period of eight months. The evaluations consisted of identifying users and devices, as well as seamlessly configure and acquire vital data from the last. Also, the entire Carestore platform was deployed in a nursing home where its usability was evaluated with caregivers. The evaluations helped assess that seamless identification of users and seamless configuration and communication with health devices is feasible and can help enable the IoT on healthcare applications. Therefore, the CRIP and similar platforms could be transformed into a valuable enabling technology for secure and reliable IoT deployments on the healthcare sector.

Background: Patients with hypertension or receiving blood pressure (BP)-lowering treatment are often required to self-measure their BP in a dedicated self-measurement room before consultation. Current praxis does not guarantee valid measurements, possibly leading to misdiagnoses or inappropriate antihypertensive medication. The aim of this study was to investigate patients' ability to correctly self-report and follow recommendations. Patients and Methods: We used a context-aware system to gather information on BP measurements and relevant context parameters. Patients were not informed that the system automatically collected behavior data and were instructed to self-report their measurements on a paper sheet as usual. We then compared the automatically recorded data with the self-reported data in order to detect any nonadherent reporting behavior. Also, we investigated the patients' ability to adhere to the measurement recommendations. Results: We found that (1) a third of all 113 participating patients failed to self-report measured BP data correctly and (2) none of the 642 measurements obtained adhered fully to the recommendations. Conclusions: Results indicate that context-aware technology may be useful for accurately modeling aspects of nonadherent patient behavior. This may be used to inform staff of the validity of the measurement and pinpoint patients in need of additional training or to design better aids to assist the patients. The developed system is generally applicable to other self-measurement environments, including the home setting and remote outpatient clinics, as it is built using telemedicine technology and thus well suited for remote monitoring and diagnosis.

Blood pressure self-measurement (BPSM) requires patients to follow a range of recommendations in order to be considered reliable for diagnostic use. We investigated currently used BPSM interventions at four medical clinics combined with an online questionnaire targeting BPSM users. We found that the participating healthcare personnel perceived BPSM as a relevant and useful intervention method providing that the recommendations are followed. A total of six challenges were identified: (1) existing devices do not guarantee that the recommendations are followed, (2) healthcare providers cannot verify whether self-monitoring patients follow the recommendations, (3) patients are not aware of all recommendations and the need to follow them, (4) risk of patient induced reporting bias, (5) risk of healthcare provider induced data-transfer bias, and (6) risk of data being registered as belonging to the wrong patient. We conclude that existing BPSM interventions could be significantly affected by user-induced bias resulting in an indeterminable quality of the measurement data. Therefore, we suggest applying context-aware technological support tools to better detect and quantify user errors. This may allow us to develop solutions that could overcome or compensate for such errors in the future.

Background: Preeclampsia is a serious medical disorder affecting pregnancy. Screening in early pregnancy can identify women at risk and enable effective prophylactic treatment. Accurate blood pressure (BP) measurement is an important element of the screening algorithm. Automated self-screening, while attending the first trimester ultra sound scan, using a BP self-measurement (BPSM) station, could be a low-cost alternative to office BP measurements (OBPM) on both arms performed by clinical staff, if the measurement quality can be ensured. Objectives: The aim of this study was to compare automated BPSM using a self-measurement station on one arm, with OBPM performed by clinical staff on both arms. Primary outcome was the difference in mean arterial pressure (MAP) between the two methods and secondary outcomes were safety and practicality issues. Methods: Pregnant women attending ultrasound-examination at 12 weeks gestational age were recruited and randomized to start with having two OBPMs taken on both arms by staff, using two standard validated automatic upper arm BP devices, or self-measuring using an automated BPSM station following a crossover study design. The BPSM station consists of a validated blood pressure device, and an add-on sensor system capable of registering blood pressure values, rest-time, back-supported, legs-crossed, and ambient noise-levels respectively, and providing interactive guidance during the measurement process, for supporting the self-measurement process. Results: A total of 80 complete BP measurement sets were obtained, for a total of 240 BPSM measurements and 320 OBPM measurements. We found no significant difference between the OBPM and BPSM methods (p=0.86) for mean arterial pressure (MAP). However, erroneous measurements were observed frequently during the experiment, mainly during the first of the 3 BPSM measurements (6%), secondary during the second BPSM measurement (3%). Only one data set (1%) was excluded due to OBPM errors. Conclusion: No significant difference in MAP between the two methods was found. Means for detecting and repeating erroneous BP measurements should be implemented. Measurement errors was found in 9 % of the measurement sets which is not acceptable for clinical use. Thus, several measures have been identified in order to properly identify and recover from such measurement errors in the future.