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.

Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and transportation. The development of Human Activity Recognition (HAR) algorithms involves the collection of a large amount of labelled data which should be annotated by an expert. However, the data annotation process on large datasets is expensive, time consuming and difficult to obtain. The development of a HAR approach which requires low annotation effort and still maintains adequate performance is a relevant challenge. We introduce a Semi-Supervised Active Learning (SSAL) based on Self-Training (ST) approach for Human Activity Recognition to partially automate the annotation process, reducing the annotation effort and the required volume of annotated data to obtain a high performance classifier. Our approach uses a criterion to select the most relevant samples for annotation by the expert and propagate their label to the most confident samples. We present a comprehensive study comparing supervised and unsupervised methods with our approach on two datasets composed of daily living activities. The results showed that it is possible to reduce the required annotated data by more than 89% while still maintaining an accurate model performance.

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.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Falls are a major health problem among older adults. About a third of community-dwelling adults aged 65 years or over fall each year. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling and changes in function of musculoskeletal and sensory systems, associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless falls can be prevented by screening to detect risk factors. Objective: To understand if FallSensing screening tool is able to determine the fall risk of community-dwelling adults aged 50 years or over. Methods: FallSensing screening tool is a technological solution for fall risk screening, including a software, a pressure platform and two inertial sensors. The screening software includes questions about sociodemographic and health and lifestyle behaviors, a detailed explanation about procedures to accomplish six functional tests (Grip Strength, Timed Up and Go, 30 seconds Sit-to-Stand, Step test, 4 Stage Balance test “modified” and 10 meters Walking Speed) and three questionnaires concerning environmental risk, activities and participation profile related to mobility and self-efficacy for exercise. The questions registers information about demographic and anthropometric data (age, sex, height and weight), Fear of Falling, history of fall (previous 12 months), health conditions, medication, sedentary lifestyle, upper extremities assistance to stand from a chair, living settings, alcohol habits, self-perceived health and unintentional or involuntary weight lost. We will recruit community-dwelling adults aged 50 years or over, independent to be standing and walking with or without walking aids in different regions of continental Portugal. In order to be a representative sample of Portuguese population, a minimum of 385 participants is needed. Results: The development of the FallSensing screening tool started in October 2015, and the screening of participants started in June 2016 and continues by the time of submission. A set of seniors’ university, parish councils, physical therapy clinics and other community facilities have consented to participate in the study. Conclusions: In order to develop a multifactorial screening tool for fall risk in community-dwelling adults, it is essential to identify key risk factors for falls and select the best method to evaluate them. FallSensing screening tool, gathers a multifactorial assessment, designed and studied through the best evidence, which seek to identify factors that influence the fall risk. Understanding these factors, will allow that preventive strategies could be implemented and decrease falls rates.

Strategies for fall risk assessment are currently not multifactorial neither implemented as a regular assessment of health status in clinics or hospital. The reason could be related with a lack of an easy to implement, complete and objective test to assess elderly's fall risk level. More recently, inertial wearable sensors have been used in combination with standard tests to evaluate the performance of the person during each phase of the test in an objective way. This paper proposes a methodology for collecting and analyzing the Timed-Up and Go (TUG) test instrumented with wearable inertial sensors. An automatic algorithm to segment the TUG test into three components was implemented prior to feature extraction. Overall, features from the walking and first turning phases of the tests could provide meaningful information to differentiate groups of high and low fall risk.