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

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

The objective of this study was to evaluate the sensitivity and specificity of a smartphone-based fall detection application when different smartphone models are worn on a belt or in a trouser pocket. Eight healthy adults aged between 18 and 24 years old simulated 10 different types of true falls, 5 different types of falls with recovery, and 11 daily activities, five consecutive times. Participants wore one smartphone in a pocket that was attached to their belt and another one in their trouser pocket. All smartphones were equipped with a built-in accelerometer and the fall detection application. Four participants tested the application on a Samsung S3 and four tested the application on a Samsung S3 mini. Sensitivity scores were .75 (Samsung S3 belt), .88 (Samsung S3 mini trouser pocket), and .90 (Samsung S3 mini belt/Samsung S3 trouser pocket). Specificity scores were .87 (Samsung S3 trouser pocket), .91 (Samsung S3 mini trouser pocket), .97 (Samsung S3 belt), and .99 (Samsung S3 mini belt). These results suggest that an application on a smartphone can generate valid fall alarms when worn on a belt or in a trouser pocket. However, sensitivity should be improved before implementation of the application in practice.

Quantifying the energy expended during physical activity is an important metric to evaluate the quality and progress of individual training. There are several methods to estimate the energy expenditure using accelerometers, the most common are based on calculating counts per minute from the accelerometer signal to determinate the activity intensity in terms of metabolic equivalents (METs). This paper compares three methods to estimate the energy expenditure, the first has been proposed in a previous study and the last two are based on linear regressions derived from the data collected, one using speed, and the other using the feature root mean square (fRMS) of the magnitude of the accelerometer signal. These models were compared with indirect calorimetry outputs of energy expenditure during an incremental speed treadmill protocol. No statistically significant differences (p>0.05) were found between the indirect calorimetry and the model derived using the RMS feature, obtaining a normalized error of 20% for the METs estimation. In conclusion, this was found to be the most suitable method to estimate the energy expenditure from accelerometer data collected using a smartphone placed in the belt

Objetivo: Descrever a qualidade de sono de estudantes do curso de Medicina numa instituição de ensino superior de Teresina-PI e correlacioná-la com dados epidemiológicos. Métodos: O estudo é do tipo transversal analítico, realizado através de questionário autoaplicável online contendo o validado Índice de Qualidade do Sono de Pittsburgh (PSQI), além de dados sócio-demográficos adicionais. Resultados: A amostra foi composta por 151 alunos. De acordo com o PSQI, 79,48% (n = 120) dos participantes apresentavam qualidade ruim no padrão do sono. Houve significância estatística apenas entre sono de qualidade ruim e uso de medicações (p<0,05). Conclusão: Percebe-se que, apesar da extensa literatura abrangendo a qualidade de sono nessa população, pouco se faz para combater esse sério problema. A má qualidade de sono continua sendo negligenciada, principalmente pelos próprios estudantes, que não se reconhecem como população de risco e não buscam, na maioria das vezes, a correção de hábitos ou atitudes prejudiciais que comprometem a qualidade do sono.