The quality of life of the elderly is highly correlated with their mobility. Reduced mobility creates serious risk of fall. Falls in the elderly are defined as “unintentionally coming to the ground, or some lower level because of sudden loss of balance, loss of consciousness, sudden onset of paralysis as in stroke or an epileptic seizure” [
1]. Falls are a major concern among the elderly because of their negative impact on a person’s physical and physiological state. Falls are the third leading cause of worldwide chronic disability [
2], and a study found that approximately 81%–98% of hip fractures are caused by falls in the elderly [
3]. Falls not only create serious threats to health among the elderly, but the treatment costs and resources required to address them also exert a heavy economic burden on the society. The total costs resulting from falls were in the range of 0.85 to 1.5 percent of the total healthcare costs in the United States, Australia, the United Kingdom and the European Union (EU) in 2009 [
3]. These costs are expected to further increase, as there will be an estimated 88.5 million individuals aged 65 and older in the United States alone in 2050, a 120% increase over the elderly population in 2010 [
4]. The risks of falling and of fall related injury increase with the person’s age because of loss of agility, vision loss, and medication side-effects [
5]. In addition to physical damage and their high costs, falls create fear of falling again by reducing self confidence among the elderly, particularly while walking on uneven pathways or a wet floor. The consequences of falls also include increased risks of pneumonia, pressure ulcers, and even death [
6]. Therefore, the assessment of mobility and early recognition of individuals prone to falls are the key strategies to reduce fall related injuries and prevent their consequences.
The Centers for Disease Control and Prevention (CDC) created the STEADI (Stopping Elderly Accidents, Deaths & Injuries) tool kit for health care providers [
7]. The CDC recommends the evaluation of gait, strength, and balance using three tests: the Timed Up and Go (TUG) test [
8], the 30-Second Chair Stand Test (30SCS) [
9], and the 4-Stage Balance Test (4SBT) [
10]. These tests are generally performed in a clinical setting, thus limiting the frequency of testing and increasing the total test costs. Because of the demographic changes, there will be an increasing number of geriatric patients in the near future and probably fewer professionals would be available to assess the risk of fall in clinical settings. Therefore researchers have attempted to bring these assessment tests to patients’ homes [
11]. In the new era of health monitoring, wireless sensing, mobile and cloud computing technologies support the development of mobile health applications. These technologies have been proven to effectively monitor the activities of daily living [
12,
13,
14]. Automatic activity recognition and quantification systems that utilize inertial sensors are proposed for long-term health and fitness monitoring [
15,
16,
17], assessment of mobility in the elderly and people with Parkinson’s disease [
18,
19], automatic fall detection [
20,
21], and rehabilitation [
22,
23]. An instrumented Timed Up and Go (iTUG) test has been recently introduced and proven to be sensitive to pathologies [
24,
25] and useful in fall risk prediction [
26]. Approaches for automatic activity recognition used by researchers vary in number, type, and placement of utilized sensors, as well as in processing of recorded signals. While some researchers used multiple sensors for automatic activity recognition [
27,
28,
29], an increasing number of projects use a single inertial sensor [
30,
31,
32,
33] usually placed on the subject’s chest. The rapid proliferation of smartphones and continual growth in smartphone capabilities have opened up new opportunities for health monitoring applications. Modern smartphones include a number of built-in inertial and environmental sensors that can be utilized in health and fitness monitoring applications. Modern smartphones typically include accelerometers, gyroscopes, magnetometers, barometers, and humidity sensors. With growing data processing and communication capabilities, smartphones enable the development of innovative mobile health (mHealth) applications.
In this paper, we introduce a suite of smartphone applications for mobility assessment. The suite includes applications for automating and quantifying standard mobility tests recommended by the Centers for Disease Control and Prevention (CDC): Timed Up and Go (TUG), 30-Second Chair Stand Test (30SCS), and the 4-Stage Balance Test (4SBT). The names of the applications are sTUG (Smart TUG) Doctor, 30SCS and 4SBT, respectively. The applications record and process the signals from the smartphone’s accelerometer, gyroscope, and magnetometer sensors to extract the parameters that quantify individual phases of the tests. The applications offer an affordable solution for quantifying mobility of the elderly with an immediate feedback and automated logging. The test procedures require minimum setup that includes a chair, a floor marker at the distance of three meters from the chair, and an inexpensive instrumentation with a smartphone placed on the chest or belt running the mobility assessment applications. The applications are quite easy to use and can be used in both ambulatory and clinical settings. For example, in ambulatory settings elderly subjects can periodically use applications to quantify their mobility and possibly detect early signs of mobility deterioration. The tests can be self-guided or conducted with a help of caregiver. In clinical settings, healthcare professionals can use applications to assess mobility of multiple patients. With automatic updates to an mHealth server over the Internet and services provided by the mHealth web portal, healthcare professionals can gain insights into mobility of tested subjects over time. For example, they can assess the impact of therapeutic interventions, e.g., impact of drugs, by analyzing the parameters from multiple tests performed in a single day. Next, healthcare professionals and researchers can monitor and evaluate evolution of disease by analyzing the trends in the parameters collected over longer periods of time.
The rest of the paper is organized as follows.
Section 2 describes system architecture of the mobility assessment framework.
Section 3 describes TUG test procedure and application including signal processing and parameter extraction.
Section 4 and
Section 5 describe the 30SCS and 4SBT applications, respectively, including parameters and signal processing.
Section 6 describes the results from preliminary tests performed on the geriatric as well as healthy individuals.
Section 7 concludes the paper.