Tal Shany

Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls

The rapid aging of the worlds population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves telehealth applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving microelectromechnical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of body-fixed sensors is the monitoring of human movement. An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure. The importance of these applications is considerable in light of the global demographic trends and the resultant rise in the occurrence of injurious falls and the decrease of physical activity. The potential of using such monitors in an unsupervised manner for community-dwelling individuals is immense, but entails an array of challenges with regards to design c onsiderations, implementation protocols, and signal analysis processes. Some limitations of the research to date and suggestions for future research are also discussed.

Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults

The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.

A small-scale randomised controlled trial of home telemonitoring in patients with severe chronic obstructive pulmonary disease

Introduction This was a pilot study to examine the effects of home telemonitoring (TM) of patients with severe chronic obstructive pulmonary disease (COPD). Methods A randomised controlled 12-month trial of 42 patients with severe COPD was conducted. Home TM of oximetry, temperature, pulse, electrocardiogram, blood pressure, spirometry, and weight with telephone support and home visits was tested against a control group receiving only identical telephone support and home visits. Results The results suggest that TM had a reduction in COPD-related admissions, emergency department presentations, and hospital bed days. TM also seemed to increase the interval between COPD-related exacerbations requiring a hospital visit and prolonged the time to the first admission. The interval between hospital visits was significantly different between the study arms, while the other findings did not reach significance and only suggest a trend. There was a reduction in hospital admission costs. TM was adopted well by most patients and eventually, also by the nursing staff, though it did not seem to change patients’ psychological well-being. Discussion Ability to draw firm conclusions is limited due to the small sample size. However the trends of reducing hospital visits warrant a larger study of a similar design. When designing such a trial, one should consider the potential impact of the high quality of care already made available to this patient cohort.

Validation of an accelerometer-based fall prediction model

Falls are a common and serious problem faced by older populations. There is a growing interest in estimating the risk of falling for older people using body-worn sensors and simple movement tasks, allowing appropriate fall prevention programs to be administered in a timely manner to the high risk population. This study investigated the capability and validity of using a waist-mounted triaxial accelerometer (TA) and a directed routine (DR) that includes three movement tasks to discriminate between fallers and non-fallers and between multiple fallers and non-multiple fallers. Data were collected from 98 subjects who were stratified into two separate groups, one for model training and the other for model validation. Logistic regression models were constructed using the TA features from the entire DR and from each single DR task, and were validated using unseen data. The best models were obtained using features from the alternate step test to classify between fallers and non-fallers with κ = 0.34-0.41, sensitivity = 68%-71% and specificity = 63%-73%. However, the overall validation performances were poor. The study emphasizes the importance of independent validation in fall prediction studies.

Telehealth technologies for managing chronic disease - experiences from Australia and the UK

In developed countries, chronic disease now accounts for more than 75% of health care expenditure and nearly an equivalent percentage of disease-related deaths [1]. The burden of chronic disease (often, but not exclusively, associated with ageing) includes congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), hypertension and diabetes. Over the past several decades there has been an epidemiological shift in disease burden from acute to chronic diseases that has rendered acute care models of health service delivery inadequate to address population health needs.