Falls are considered the main cause of fear and loss of independence among the elderly population and are also a major cause of morbidity, disability and health care utilization. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a unobtrusive smartphone based fall detection system that uses a combination of information derived from machine learning classification applied in a state machine algorithm. The data from the smartphone built-in accelerometer is continuously screened when the phone is in the user's belt or pocket. Upon the detection of a fall event, the user location is tracked and SMS and email notifications are sent to a set of contacts. The accuracy of the fall detection algorithm here proposed is near 97.5% for both the pocket and belt usage. In conclusion, the proposed solution can reliably detect fall events without disturbing the users with excessive false alarms, presenting also the advantage of not changing the user's routines, since no additional external sensors are required

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge

Twin pregnancies complicated by GDM might be associated with pre-pregnancy maternal obesity and are at increased risk of RDS and non-significant increased risk of perinatal death.

Monitoring physical activity and energy expenditure is important for maintaining adequate activity levels with an impact in health and well-being. This paper presents a smartphone based method for classification of inactive postures and physical activities including the calculation of energy expenditure. The implemented solution considers two different positions for the smartphone, the user's pocket or belt. The signal from the accelerometer embedded in the smartphone is used to classify the activities resorting to a decision tree classifier. The average accuracy of the classification task for all activities is 99.5% for the pocket usage and 99.4% when the phone is used in the belt. Using the output of the activity classifier we also compute an estimation of the energy expended by the user. The proposed solution is a trustworthy smartphone based activity monitor, classifying the activities of daily living throughout the entire day and allowing to assess the associated energy expenditure without causing any change in user's routines

Objective: To analyze whether maternal age at first pregnancy and parity are mediators of the association between early menarche and metabolic syndrome in a sample of middle-aged and older women. Methods: Cross-sectional study of 428 women (40 to 80 y), who had experienced a pregnancy in their lifetime, was performed between 2014 and 2016. Age at first pregnancy, parity, and early menarche were self-reported. Metabolic syndrome was assessed using the criteria described by the National Cholesterol Education Program's Adult Treatment Panel III. The association between metabolic syndrome and early menarche was assessed by logistic regression analysis. The mediating role of age at first pregnancy and multiparity in the relationship between early menarche and metabolic syndrome was assessed through mediation analysis, adjusted for covariates. Results: According to adjusted logistic regression models, early menarche was associated with higher odds of prevalent metabolic syndrome (OR: 2.26; 95% CI: 1.15-4.46). Mediation analysis showed a significant direct effect of early menarche on metabolic syndrome (β: 0.808; 95% CI: 0.107-1.508). Of the two mediators tested, age at first pregnancy was significant (β: 0.065; 95% CI: 0.004-0.221), ie, participants with and without early menarche differ, on average, by 0.879 SDs in the log odds of MetS (total effect), of which 0.065 SDs (8%), on average, would be attributable to the effect of early menarche on age at first pregnancy (indirect effect), which, in turn, affects MetS. Conclusions: Age at first pregnancy may partially contribute to the association between early menarche and metabolic syndrome among middle-aged and older women who had experienced a pregnancy over their lifetime.