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

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

The past years have witnessed a boost in fall detection-related research works, disclosing an extensive number of methodologies built upon similar principles but addressing particular use-cases. These use-cases frequently motivate algorithm fine-tuning, making the modelling stage a time and effort consuming process. This work contributes towards understanding the impact of several of the most frequent requirements for wearable-based fall detection solutions in their performance (usage positions, learning model, rate). We introduce a new machine learning pipeline, trained with a proprietary dataset, with a customisable modelling stage which enabled the assessment of performance over each combination of custom parameters. Finally, we benchmark a model deployed by our framework using the UMAFall dataset, achieving state-of-the-art results with an F1-score of 84.6% for the classification of the entire dataset, which included an unseen usage position (ankle), considering a sampling rate of 10 Hz and a Random Forest classifier.

Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user’s waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level.

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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

Background According to the United Nations, it is estimated that by 2050, the number of people aged 80 years and older will have increased by 3 times. Increased longevity is often accompanied by structural and functional changes that occur throughout an individual’s lifespan. These changes are often aggravated by chronic comorbidities, adopted behaviors or lifestyles, and environmental exposure, among other factors. Some of the related outcomes are loss of muscle strength, decreased balance control, and mobility impairments, which are strongly associated with the occurrence of falls in the elderly. Despite the continued undervaluation of the importance of knowledge on fall prevention among the elderly population by primary care health professionals, several evidence-based (single or multifaceted) fall prevention programs such as the Otago Exercise Program (OEP) have demonstrated a significant reduction in the risk of falls and fall-related injuries in the elderly within community settings. Recent studies have strived to integrate technology into physical exercise programs, which is effective for adherence and overcoming barriers to exercise, as well as improving physical functioning. Objective This study aims to assess the impact of the OEP on the functionality of home-dwelling elderly using a common technological platform. Particularly, the impact on muscle strength, balance, mobility, risk of falling, the perception of fear of falling, and the perception of the elderly regarding the ease of use of technology are being examined in this study. Methods A quasi-experimental study (before and after; single group) will be conducted with male and female participants aged 65 years or older living at home in the district of Porto. Participants will be recruited through the network COLABORAR, with a minimum of 30 participants meeting the study inclusion and exclusion criteria. All participants will sign informed consent forms. The data collection instrument consists of sociodemographic and clinical variables (self-reported), functional evaluation variables, and environmental risk variables. The data collection tool integrates primary and secondary outcome variables. The primary outcome is gait (timed-up and go test; normal step). The secondary outcome variables are lower limb strength and muscle resistance (30-second chair stand test), balance (4-stage balance test), frequency of falls, functional capacity (Lawton and Brody - Portuguese version), fear of falling (Falls Efficacy Scale International - Portuguese version), usability of the technology (System Usability Scale - Portuguese version), and environmental risk variables (home fall prevention checklist for older adults). Technological solutions, such as the FallSensing Home application and Kallisto wearable device, will be used, which will allow the detection and prevention of falls. The intervention is characterized by conducting the OEP through a common technological platform 3 times a week for 8 weeks. Throughout these weeks, the participants will be followed up in person or by telephone contact by the rehabilitation nurse. Considering the COVID-19 outbreak, all guidelines from the National Health Service will be followed. The project was funded by InnoStars, in collaboration with the Local EIT Health Regional Innovation Scheme Hub of the University of Porto. Results This study was approved on October 9, 2020 by the Ethics Committee of Escola Superior de Enfermagem do Porto (ESEP). The recruitment process was meant to start in October, but due to the COVID-19 pandemic, it was suspended. We expect to restart the study by the beginning of the third quarter of 2021. Conclusions The findings of this study protocol will contribute to the design and development of future robust studies for technological tests in a clinical context. Trial Registration ISRCTN 15895163; https://www.isrctn.com/ISRCTN15895163 International Registered Report Identifier (IRRID) PRR1-10.2196/25781