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The incidence of immune-mediated diseases (IMDs) is increasing rapidly in the developed countries constituting a huge medical, economic, and societal challenge. The exposome plays an important role since genetic factors cannot explain such a rapid change. In the Human Exposomic Determinants of Immune Mediated Diseases (HEDIMED) project, altogether 22 academic and industrial partners join their multidisciplinary forces to identify exposomic determinants that are driving the IMD epidemic. The project is based on a combination of data and biological samples from large clinical cohorts constituting about 350,000 pregnant women, 30,000 children prospectively followed from birth, and 7,000 children from cross-sectional studies. HEDIMED focuses on common chronic IMDs that cause a significant disease burden, including type 1 diabetes, celiac disease, allergy, and asthma. Exposomic disease determinants and the underlying biological pathways will be identified by an exploratory approach using advanced omics and multiplex technologies combined with cutting-edge data mining technologies. Emphasis is put on fetal and childhood exposome since the IMD disease processes start early. Inclusion of several IMDs makes it possible to identify common exposomic determinants for the diseases, thus facilitating the development of widely operating preventive and curative treatments. HEDIMED includes data and samples from birth cohorts and clinical trials that have used exposomic interventions and cell and organ culture models to identify mechanisms of the observed associations. Importantly, HEDIMED generates a toolbox that offers science-based functional tools for key stakeholders to control the IMD epidemic. Altogether, HEDIMED aims at innovations, which become widely exploited in diagnostic, therapeutic, preventive, and health economic approaches.

Background Greenspace has been associated with health benefits in many contexts. An important pathway may be through outdoor physical activity. We use a novel approach to examine the link between greenspace microenvironments and outdoor physical activity levels in the HEALS study conducted in Edinburgh (UK), the Netherlands, and Athens and Thessaloniki (Greece). Methods Using physical activity tracker recordings, 118 HEALS participants with young children were classified with regard to daily minutes of moderate to vigorous physical activity (MVPA); 60 were classified with regard to the metabolic equivalent task (MET)-minutes for each of the 1014 active trips they made. Greenspace indicators were generated for Normalised Difference Vegetation Index (NDVI), tree cover density (TCD), and green land use (GLU). We employed linear mixed-effects models to analyse (1) daily MVPA in relation to greenspace within 300 m and 1000 m of residential addresses and (2) trip MET-minutes in relation to average greenspace within a 50 m buffer of walking/cycling routes. Models were adjusted for activity, walkability, bluespace, age, sex, car ownership, dog ownership, season, weekday/weekend day, and local meteorology. Results There was no clear association between MVPA-minutes and any residential greenspace measure. For example, in fully adjusted models, a 10 percentage point increase in NDVI within 300 m of home was associated with a daily increase of 1.14 (95% CI − 0.41 to 2.70) minutes of MVPA. However, we did find evidence to indicate greenspace markers were positively linked to intensity and duration of activity: in fully adjusted models, 10 percentage point increases in trip NDVI, TCD, and GLU were associated with increases of 10.4 (95% CI: 4.43 to 16.4), 10.6 (95% CI: 4.96 to 16.3), and 3.36 (95% CI: 0.00 to 6.72) MET-minutes, respectively. The magnitude of associations with greenspace tended to be greater for cycling. Conclusions More strenuous or longer walking and cycling trips occurred in environments with more greenspace, but levels of residential greenspace did not have a clear link with outdoor MVPA. To build on our research, we suggest future work examine larger, more diverse populations and investigate the influence of greenspace for trip purpose and route preference.

BackgroundGait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer’s Disease and other dementias. It is not clear however, which gait features are most informative and how these measures relate to Alzheimer pathology. In this study, we used in-depth evaluation of gait in cognitively normal (CN) subjects, Mild Cognitive Impairment (MCI) patients, and dementia patients. We evaluated the associations between dynamics of gait, level of cognitive impairment and CSF biomarkers of Alzheimer’s pathology.MethodWe included CN participants (n=58), MCI patients (n=58) and participants with different types of dementia (n=26) of the multicenter PredictND cohort. Participants performed gait tests in two conditions: a normal walk task and a cognitively complex dual task. Gait was measured using tri-axial accelerometers attached to the fifth lumbar vertebra (L5). Following principal component analysis, calculated gait features were clustered into four domains: pace, rhythm, time variability and length variability. Associations between gait domains, clinical diagnosis and CSF biomarkers (Aβ42, total tau, pTau) were examined using linear mixed models, included an interaction term with condition (normal and dual walk task) and were corrected for age, sex and center.ResultDementia patients showed gait disturbances in the pace domain (pConclusionThese findings suggest that gait disturbances increase with level of syndrome diagnosis. Moreover, rhythm disturbances increase with higher levels of CSF total tau and pTau, providing evidence for a direct link with neurodegeneration. These results demonstrate that in-depth gait analysis with accelerometers might be a potential helpful tool to support clinical diagnosis or can be used as clinical outcome tool in clinical trials.

Background: the built environment in urban areas may have side effects on children's respiratory health, whilst less is known for adulthood.Aim: to assess the association between increasing exposure to grey spaces and allergic status in an adult general population sample.Methods: 2070 subjects (age range 15–84 yrs), living in Pisa/Cascina, Italy, were investigated in 1991–93 through a questionnaire on health status and risk factors, skin prick test (SPT), serum Immunoglobulins E (IgE), and serum antibodies to benzo(a)pyrene diol epoxide (BPDE)-DNA adducts. Land-cover exposure within a 1000 m buffer from each subject's home address was assessed through the CORINE Land Cover program (CLC 1990) within the FP7/HEALS project (2013–2018). Participants' residential addresses were geocoded and the proportion of surrounding grey spaces was calculated. Through logistic regression models, adjusting for potential confounding factors, the effect of a 10% increase in grey spaces exposure on allergic biomarkers/conditions was assessed; the relationship with serum antibodies to BPDE-DNA adducts positivity was also analyzed.Results: A 10% increase in grey spaces coverage was associated with a higher probability of having SPT positivity (OR 1.07, 95% CI 1.02-1.13), seasonal SPT positivity (OR 1.12, 1.05-1.19), polysensitization (OR 1.11, 1.04-1.19), allergic rhinitis (OR 1.10, 1.04-1.17), co-presence of SPT positivity and asthma/allergic rhinitis (OR 1.16, 1.08-1.25), asthma/allergic rhinitis (OR 1.06, 1.00-1.12), presence of serum antibodies to BPDE-DNA adducts positivity (OR 1.07, 1.01-1.14).Conclusions: grey spaces have adverse effects on allergic status and are related to a biomarker of polycyclic aromatic hydrocarbons exposure in adulthood. Thus, they may be used as a proxy of urban environmental exposure.

Background: A link between residential land cover and allergic symptoms has been suggested. Aim: To assess the influence of residential land cover on objective biomarkers of exposure and effect in a general population sample.Methods: 2070 subjects (age range: 15-84) living in Pisa, Italy, were investigated in 1991-93 through a questionnaire on health status and risk factors, blood analyses, skin prick test (SPT). Land-cover exposure within a 1000 m buffer from each subjects' home address was computed using data from the CORINE program (CLC 1990). The land coverage effects on positivity to SPT and to serum antibodies anti-benzo(a)pyrene diol epoxide (BPDE)-DNA adducts, a markers of polycyclic aromatic hydrocarbons exposure, were assessed using logistic regression models, adjusted for sex, age, BMI, education, smoking habits, occupational exposure, family history of allergic/respiratory diseases. Results: A 10% increase in agricultural space coverage was associated with lower risk of having BPDE-DNA (OR [95% CI]: 0.91 [CI 0.87-0.97]), overall SPT positivity (OR 0.93 [0.88-0.98]), seasonal SPT positivity (OR 0.89 [0.84-0.95]) and polisensitization (OR 0.87 [0.81-0.94]). A 10% increase in grey space coverage was associated with higher risk of having BPDE-DNA (OR 1.09 [1.03-1.15]), overall SPT positivity (OR 1.07 [1.02-1.13]), seasonal SPT positivity (OR 1.12 [1.05-1.19]) and polisensitization (OR 1.15 [1.06-1.23]). Conclusions: Our results provide evidence for a protective effect of agricultural space and an adverse effect of grey space on allergic status using biomarkers. Further, the validity of residential land cover as a proxy of air pollution exposure is suggested.

Background: A recent research has shown that residential land cover may affect health outcomes in children (Parmes, Envir Res 2019); the influence of residential land cover on respiratory and allergic health in adults, however, still needs to be clarified. Aim: To assess influence of residential land cover on allergic/respiratory outcomes in a general population sample. Methods: 2706 subjects (females: 55.4%; age range: 15-97) living in Pisa, Italy, were investigated in 1991-93 through a questionnaire on health status and risk factors. Land-cover exposures within a 1000 m buffer from each subjects' home address were computed using data from the CORINE program (CLC 1990). The associations of allergic rhinitis, asthma and chronic obstructive pulmonary disease (COPD) with land coverage were estimated using logistic regression models, adjusted for sex, age, body mass index, education, smoking habits, occupational exposure and family history of allergic/respiratory diseases. Results: A 10% increase in agricultural space coverage was associated with lower risk of having allergic rhinitis (OR [95% CI]: 0.95 [0.91-0.99]), asthma (OR 0.95 [0.90-0.99]) and COPD (OR 0.93 [0.89-0.97]) symptoms/diagnoses; vice versa, a 10% increase in grey space coverage was associated with higher risk of having allergic rhinitis (OR 1.04 [1.01-1.09], asthma (OR 1.05 [1.00-1.10]) and COPD (OR 1.08 [1.03-1.12] symptoms/diagnoses.Conclusions: Our results provide further evidence that living close to agricultural space may provide protective effects on allergic/respiratory health outcomes, while exposure to grey spaces may have adverse effects.

BACKGROUND: Gait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer's disease and other dementias. It is not clear, however, which gait features are most informative and how these measures relate to Alzheimer's disease pathology.OBJECTIVE: In this study, we tested if calculated features of gait 1) differ between cognitively normal subjects (CN), mild cognitive impairment (MCI) patients, and dementia patients, 2) are correlated with cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease, and 3) predict cognitive decline.METHODS: Gait was measured using tri-axial accelerometers attached to the fifth lumbar vertebra (L5) in 58 CN, 58 MCI, and 26 dementia participants, while performing a walk and dual task. Ten gait features were calculated from the vertical L5 accelerations, following principal component analysis clustered in four domains, namely pace, rhythm, time variability, and length variability. Cognitive decline over time was measured using MMSE, and CSF biomarkers were available in a sub-group.RESULTS: Linear mixed models showed that dementia patients had lower pace scores than MCI patients and CN subjects (p CONCLUSION: These findings suggest that gait - particularly measures related to pace and rhythm - are altered in dementia and have a direct link with measures of neurodegeneration.

Background/AimThe exposome includes urban greenspace, which may affect health via a complex set of pathways, including reducing exposure to particulate matter (PM) and noise. We assessed these pathways using indoor exposure monitoring data from the HEALS study in four European urban areas (Edinburgh, UK; Utrecht, Netherlands; Athens and Thessaloniki, Greece).MethodsWe quantified three metrics of residential greenspace at 50 m and 100 m buffers: Normalised Difference Vegetation Index (NDVI), annual tree cover density, and surrounding green land use. NDVI values were generated for both summer and the season during which the monitoring took place. Indoor PM2.5 and noise levels were measured by Dylos and Netatmo sensors, respectively, and subjective noise annoyance was collected by questionnaire on an 11-point scale. We used random-effects generalised least squares regression models to assess associations between greenspace and indoor PM2.5 and noise, and an ordinal logistic regression to model the relationship between greenspace and road noise annoyance.ResultsWe identified a significant inverse relationship between summer NDVI and indoor PM2.5 (−1.27 μg/m3 per 0.1 unit increase [95% CI -2.38 to −0.15]) using a 100 m residential buffer. Reduced (i.e., ConclusionsWe identified reduced indoor levels of PM2.5 and noise annoyance, but not overall noise, with increasing outdoor levels of certain greenspace indicators. To corroborate our findings, future research should examine the effect of enhanced temporal resolution of greenspace metrics during different seasons, characterise the configuration and composition of green areas, and explore mechanisms through mediation modelling.

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results ($p

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with the one obtained from standard electrocardiography (ECG).Methods: Twenty-eight sleep deprived patients were recorded for one night each through matrix of piezoelectric sensors, integrated into the mattress, through polysomnography (PSG) simultaneously. The two recording methods have been compared in terms of signal quality and differences in heart beat detection.Results: On average, coverage of 92.7% of the total sleep time was obtained for the bed sensor, testifying the good quality of the recordings. The average beat-to-beat error of the inter-beat intervals was 1.06%. These results suggest a good overall signal quality, however, considering fast heart rates (HR > 100 bpm), performances were worse: in fact, the sensitivity in the heart beat detection was 28.4% while the false positive rate was 3.8% which means that a large amount of fast beats were not detected.Conclusions: The accuracy of the measurements made using the bed sensor has less than 10% of failure rate especially in periods with HR lower than 70 bpm. For fast heart beats the uncertainty increases. This can be explained by the change in morphology of the bed sensor signal in correspondence of a higher HR.

Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.

In mobile devices there exist several in-built sensor units and sources which provide data for context reasoning. More context sources can be attached via wireless network connections. Usually, the mobile devices and the context sources are battery powered and their computational and space resources are limited. This sets special requirements for the context recognition algorithms. In this paper, several classification and automatic feature selection algorithms are compared in the context recognition domain. The main goal of this study is to investigate how much advantage can be achieved by using sophisticated and complex classification methods compared with a simple method that can easily be implemented in mobile devices. The main result is that even a simple linear classification algorithm can achieve a reasonably good accuracy if the features calculated from raw data are selected in a suitable way. Usually context recognition algorithms are fitted to a particular problem instance in an off-line manner and modifying methods for on-line learning is difficult or impossible. An on-line version of the Minimum-distance classifier is presented in this paper and it is justified that it leads to considerably higher classification accuracies compared with the static off-line version of the algorithm. Moreover, we report superior performance for the Minimum-distance classifier compared to other classifiers from the view point of computational load and power consumption of a smart phone.

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

We discuss health monitoring as a potential application field for wearable sensors. We present some usage models for health monitoring and discuss the technical requirements for the health-monitoring system based on wearable and ambient sensors, which measure health-related data in daily environments of the users or patients. The presentation is by no means complete, but it aims to give an idea of the system-level issues to be considered for real applications. The technology in this area is rapidly developing, and without doubt we will evidence emergence of these applications in the coming years in the market.

Assessment of heart rate variability from 24-h recordings requires a high-quality Holter, expensive equipment and multistage processing of recordings. We compared a new personal computer-based digital QRS detector system with a Holter recorder, and found the two methods to be equally accurate in time and frequency domain measures of heart rate variability.

A system to provide cardiac patients with the possibility to perform safe and beneficial exercise during their rehabilitation was developed within the EU project Heartcycle. Within the system, algorithms use physiological signals from a wearable device (embedded in a shirt) to guide the patient through the exercise. After having been technically validated, the system was deployed for clinical evaluation with 63 patients in Germany (21), Great Britain (25) and Spain (17). The paper describes the first findings of this evaluation study with respect to: technical feasibility, adherence of the patients to the prescribed exercise protocol, and changes in physiological observables as the patients follow their exercise plan. Technically, the system functioned well, and overall the adherence of those patients that actually started using the system was stable, or even improving, in the vast majority of cases. An increase was especially seen in adherence to the protocol set for the warm-up phase of the exercise. This is an important finding for a group of patients that is known to have declining adherence to rehabilitation recommendations in the long term. The recovery analysis showed that especially the recovery of the heart rate as measured in the first minute after the exercise end showed improvement over time.

Ability to do different daily activities defines partly one's independency. We study older adults' daytime sleep behaviour (napping) with respect to user's physical activity behaviour and physical functioning. The daytime sleep behaviour was measured via self-reported vigilance, actigraphy and bed sensor. Nineteen users took part in the study for three months. Especially actigraphy napping feature was found to have statistically significant correlation to number of steps, daily activity and self-reported exercise. According to the data, daily napping habits can also tell about the user's physical ability level.

We studied self-observations related to weight management recorded with a Wellness Diary application on a mobile phone. The data were recorded by 27 participants in a 12-week study, which included a short weight management lecture followed by independent usage of the Wellness Diary. We studied the validity of self-observed weight, and behavioural changes and weight patterns related to weight management success. Self-observed weight data tended to underestimate pre- and poststudy measurements, but there were high correlations between the measures (r ≥ 0.80). The amount of physical activity correlated significantly with weight loss (r = 0.44) as did different measures representing healthy changes in dietary behaviours (r ≥ 0.45). Weight changes and the weekly rhythms of weight indicated a strong tendency to compensate for high-risk periods among successful weight-losers compared to unsuccessful ones. These preliminary results suggest that the mobile phone diary is a valid tool for observing weight management and related behaviours.

Long-term monitoring of health is essential in many chronic conditions, but automatic monitoring is not yet utilized routinely with mental stress. Accelerometers, magnetometers, ECG, respiratory effort, skin temperature and pulse oximetry were used with 12 health volunteers in this study for monitoring 1) heavy mental load, 2) normal mental load, 3) walking, 4) running and 5) lying. Heavy mental load consisted of a 20-min IQ test and normal mental load was represented by reading a comic book. Automatic feature selection using sequential forward search was used to select the best features for classification of the five activities. Normalized heart rate, utilizing activity context information was found to be the most powerful feature for discriminating heavy mental load from normal. Classification accuracy for all 5 activities was 89% with a custom decision tree and with a k-nearest neighbor classifier and 85% with an artificial neural network.

Chronic conditions closely related to lifestyles are the major cause of disability and death in the developed world. Behavior change is the key to managing well-being and preventing and managing chronic diseases. Wellness diary (WD) is a mobile application designed to support citizens in learning about their behavior, and both making and maintaining behavior changes. WD has been found acceptable, useful, and suitable for long-term use as a part of an intervention. When used independently, however, it does not seem to have enough engaging and motivating features to support adoption and long-term commitment. The main improvement needs identified based on a review of WD-related studies were: personalization of the application to individual needs, increasing motivation during early use, maintaining motivation, and aiding in relapse recovery in long-term use. We present concepts to improve the personalization of WD as well as improvements to the feedback and interpretation of the self-observation data. We also present usage models on how this type of mobile application could be utilized.

Individual wellness comprises both psychological and physiological wellbeing, which are interrelated. In long-term monitoring of wellness, both components should be included. Work-related stress and burnout are persistent problems in industrial countries. Early identification of work-related stress symptoms and early intervention could reduce individual suffering and improve the working productivity and creativity. The goal of this study was to explore the relationship between physiological and psychological variables measured at home by the users themselves or automatically. In all, 17 (3 males and 14 females, age 40-62) people participating in a work ability rehabilitation program (due to work overload) were monitored for three months. Physiological and behavioral variables (activity, bed occupancy, heart rate (HR) and respiration during night, HR during day, blood pressure, steps, weight, room illumination, and temperature) were measured with different unobtrusive wireless sensors. Daily self-assessment of stress, mood, and behaviors (exercise, sleep) were collected using a mobile phone diary. The daily self-assessment of stress and the Derogatis stress profile questionnaire were used as reference for stress status. Results show modest, but significant pooled overall correlations between self-assessed stress level, and physiological and behavioral variables (e.g., sleep length measured with wrist-worn activity monitor: ? = ?0.22, p < 0.001, and variance of nightly bedroom illumination: ? = 0.13, p < 0.001). Strong, but sometimes conflicting correlations can be found at individual level, suggesting individual reactions to stress in daily life.

We developed a system consisting of both wearable and ambient technologies designed to monitor personal wellbeing for several months during daily life. The variables monitored included bodyweight, blood pressure, heart-rate variability and air temperature. Two different user groups were studied: there were 17 working-age subjects participating in a vocational rehabilitation programme and 19 elderly people living in an assisted living facility. The working-age subjects collected data for a total of 1406 days; the average participation period was 83 days (range 43–99). The elderly subjects collected data for a total of 1593 days; the average participation period was 84 days (range 19–107). Usage, technical feasibility and usability of the system were also studied. Some technical and practical problems appeared which we had not expected such as thunder storm damage to equipment in homes and scheduling differences between staff and the subjects. The users gave positive feedback in almost all their responses in a questionnaire. The study suggests that the data-collection rate is likely be 70–90% for typical health monitoring data.

The prevalence of lifestyle-related health problems is increasing rapidly. Many of the diseases and health risks could be prevented or alleviated by making changes toward healthier lifestyles. We have developed the Wellness Diary (WD), a concept for personal and mobile wellness management based on Cognitive-Behavioral Therapy (CBT). Two implementations of the concept were made for the Symbian Series 60 (S60) mobile phone platform, and their usability, usage, and acceptance were studied in two 3-month user studies. Study I was related to weight management and study II to general wellness management. In both the studies, the concept and its implementations were well accepted and considered as easy to use and useful in wellness management. The usage rate of the WD was high and sustained at a high level throughout the study. The average number of entries made per day was 5.32 (SD = 2.59, range = 0-14) in study I, and 5.48 (SD = 2.60, range = 0-17) in study II. The results indicate that the WD is well suited for supporting CBT-based wellness management.

The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.

Activity recognition with wearable sensors could motivate people to perform a variety of different sports and other physical exercises. We have earlier developed algorithms for offline analysis of activity data collected with wearable sensors. In this paper, we present our current progress in advancing the platform for the existing algorithms to an online version, onto a PDA. Acceleration data are obtained from wireless motion bands which send the 3D raw acceleration signals via a Bluetooth link to the PDA which then performs the data collection, feature extraction and activity classification. As a proof-of-concept, the online activity system was tested with three subjects. All of them performed at least 5 minutes of each of the following activities: lying, sitting, standing, walking, running and cycling with an exercise bike. The average second-by-second classification accuracies for the subjects were 99%, 97%, and 82 %. These results suggest that earlier developed offline analysis methods for the acceleration data obtained from wearable sensors can be successfully implemented in an online activity recognition application.

We study the possibility of applying an emerging RFID-based communication technology, NFC (Near Field Communication), to health monitoring. We suggest that NFC is, compared to other competing technologies, a high-potential technology for short-range connectivity between health monitoring devices and mobile terminals. We propose practices to apply NFC to some health monitoring applications and study the benefits that are attainable with NFC. We compare NFC to other short-range communication technologies such as Bluetooth and IrDA, and study the possibility of improving the usability of health monitoring devices with NFC. We also introduce a research platform for technical evaluation, applicability study and application demonstrations of NFC.

Sleep quality is one of the key elements of the human health status. By observing sleep patterns we can gain information about personal wellbeing. Consumer electronic sleep analysis solutions are now available for use in long-term conditions. In this study we compare different measures for total sleep time and sleep quality. We analyzed visually long- term sleep data collected with actigraphy, sleep logs and ambient sensors to gain more reliable results and compared these results to each single method's output. Correlations of visually analyzed total sleep time between actigraphy total sleep time (correlation coefficient (r) = 0.662, p0.01) and sleep log total sleep time (r = 0.787, p0.01) were high. Also comparison between subjective and objective sleep quality was analyzed and small, but significant correlation was found (r = 0.270, p0.01).

Automatic estimation of physical activity using wearable sensors can be used for promotion of a healthier lifestyle. In this study, accelerometers and gyroscopes attached to ankle, wrist and hip were used to estimate intensity of physical activity. The estimates are compared to metabolic equivalent (MET) obtained from a portable cardiopulmonary exercise testing system. Data from common everyday tasks and exercise were collected with 11 subjects. The tasks include, e.g., ironing, vacuuming, walking, running and cycling on exercise bicycle (ergometer). The strongest linear correlation with metabolic equivalent was obtained with the tri-axial accelerometer attached to the ankle (r=0.86).

Despite increasing possibilities for the citizen to play a more active role in personal health management, the use of Internet based health applications remains limited. Poor usability, limited personalization, and problems with security and accessibility often frustrate a continued use. The paper presents a possible solution by actively using wireless communications and ad-hoc networking techniques to minimize the user's efforts in using the application. Overweightness is a widespread and increasing problem in western countries. There are indications that self-monitoring combined with guidelines provide a good basis for personal weight management. The wireless wellness monitor implements a self-monitoring and guidance system using Bluetooth- and Jini-based networking. A scale, heart rate monitor, mobile terminal (personal digital assistant or WAP-enabled digital cellular phone), and home server communicate locally via Bluetooth. Internet based communications take care of remote use. The set-up allows us to investigate the behavior and use cases based on equipment and protocols that are expected to be in general use in the coming years. Thus, the system provides a useful test-bed for evaluating new techniques that may bring personal health management to a new level.

With the fast development of information and communications technology, devices with some computing power are becoming increasingly abundant in the average home. The trend for theses devices to also be provided with inter-communication functionality opens up new possibilities for health care. The availability of ubiquitous computing allows the individual to perform health-related measurements review data or consult guidelines independent of place or time, thus providing the individual with a better means to follow certain treatment programs. In this study, we set out to implement as a system based on ubiquitous computing for the application of weight management using behavioral feedback. The goals were a) to gain insights in the technical feasibility and caveats of such a system as a basis for setting up more advanced and generic infrastructures, and b) to study the degree to whether people are actually willing to use such a technology-laden system for their health management. The results lead us to the conclusion that such a system is possible with the current technology, although many issues need to be solved before generic architectures and implementations are a reality, and that many people, especially those who have serious weight problems, are eager to use such a system.

Regular aerobic exercise is a recommended treatment for elevated blood pressure (BP). However, making permanent lifestyle changes is not easy. Having personally relevant information about the treatment, about its effects and importance, is a precondition for motivation. Thus, the first step towards a successful lifestyle change is appropriate education. This paper describes a Sugeno-type Fuzzy Inference System (FIS) that predicts the effect of regular aerobic exercise on blood pressure based on the exercise dose variables, exercise frequency and intensity, as well as demographics (age, gender, ethnicity), and the baseline BP of a person. Since BP response to exercise varies largely between individuals, the system takes an initial step towards personalized prediction. Hence, the system can be used to educate a person about the benefits of exercise on BP in a personally relevant way, providing more accurate information than traditional education materials. Furthermore, preliminary validation results of the performance of the FIS are promising. The predictions comply with the findings of medical research for populations, though the individual-level validation remains still to be done.

This paper describes biosignal interpretation methods in an application to support a CAD (Coronary Artery Disease) patient during rehabilitation. The methods use physiological signals from a wearable device (embedded in a shirt) to guide the patient through safe and beneficial exercise. The paper describes the different design considerations of the processing chain: signal quality verification; on-line analysis and feedback; recovery analysis and exercise summary analysis. The system components have been validated: differences in heart rate between the device and the reference were small, electrocardiograph (ECG) signal quality estimation algorithm had high sensitivity and specificity, and the on-line feedback algorithms perform well enough for use during exercise in practice. A study regarding the clinical usefulness of the overall system is carried in 2012 in the context of the EU project Heartcycle.

The growing population of overweight and obese people calls for developing new methods and tools for independent weight management. Wellness Diary (WD), a cognitive behavioural treatment (CBT) based weight management application, was designed and implemented for Series 60 mobile phone platform. WD is a tool for recording CBT related self-observations and giving feedback on them. A three-month user study with 29 volunteer users was launched to study the acceptance and effect of the concept. The users recorded on average 5.6 (SD 0.59) self-observations to WD per day, and the recording activity was maintained at a high level throughout the study. The average weight change in the group was -1.4 kilograms (range: -12.0 - +1.5 kg, SD = 2.7 kg). The users found WD simple and easy-to-use, yet useful in weight management. The results indicate high level of acceptance for the approach and concept. The weight management results were encouraging and suggest that wellness diary concept supports success of CBT based weight management.

Exposure to urban greenspace may affect health via a complex set of pathways, including lessened exposures to air pollution and enhanced opportunity for physical activity. The HEALS study included personal monitoring of mothers of young children to pilot novel devices and collect environmental exposure data from individuals in four European cities. The data collected over ~1-week periods thus present an opportunity to assess these two important pathways for which greenspace may benefit health.We include three metrics of greenspace exposure (Normalised Difference Vegetation Index [NDVI], tree canopy density, and proximity to green land use) and will track objective measurements of physical activity through personal GPS data, including the duration engaging in active, e.g., walking, and passive, e.g., driving, transport. Fitbit units worn by study participants recorded steps per minute. Indoor PM2.5 and noise levels were collected from participants’ homes, and we are exploring the use of the Data Integration Model for Air Quality to estimate outdoor air pollutant concentrations.Based on the Edinburgh participants (n=29), preliminary results suggest no associations between residential greenspace metrics with indoor PM2.5, noise levels, or indicators of physical activity. However, using the GPS data, mean NDVI levels demonstrated significant positive correlations with the overall distance of walking or running (r=0.46; p=0.02) and also overall steps (r=0.41; p=0.04) during the monitoring period. No such associations were identified with tree canopy densities. We will expand the analysis to incorporate covariates of individual participants and data from the other cities to refine these early results.