Sacred Heart University From the SelectedWorks of Tolga Kaya 2015 From Pit to Long Lie : A Fall-detection Algorithm for Smart Phones

The elderly population of the world continues to grow and, therefore, creates a need for heightened safety measures. Smartphones have been broadly adopted throughout age categories and mobile applications are extremely useful during all types of daily activities; popular applications today not only target recreational uses, but also health tracking and awareness. As men and women over the age of 65 continue to lead active lives, a smartphone application that can detect falling incidents would be very useful. In this research project, a fall-detection application was developed that utilized the acceleration sensor embedded in most mobile devices. The application continuously monitored a person’s movement and checked multiple threshold points for a fall impact. Upon impact, phone settings were utilized to communicate with a contact for immediate assistance. The algorithm was successfully implemented, based on the Android platform and its accuracy was tested with eight subjects, who performed 135 fall experiments overall. The measured sensitivity was calculated to be 92% and the measured specificity was 100%. Introduction and Related Works The life expectancy of the elderly population, specifically regarding those over the age of 65, continues to increase, thanks to better healthcare technology and available treatment options [1]. The median age increased significantly from 29.5 to 37.2 in the time period from 1960 to 2010, resulting in a 13% increase of the overall elderly population in the U.S. [2]. Therefore, different challenges for the elderly population were introduced to health professionals; these are commonly related to fatigue and incidents in addition to particular illnesses [1], [3], [4]. Among these identified incidents, falls represent one of the most frequent cases that challenges the elderly population. Although it is difficult to define a specific fall event and collect the fall-related reports, various studies have shown that almost 30% of elderly persons fall at least once a year [1], [3], a number that corresponds to more than 10 million elderly in the U.S., who would experience a fall incident within a year [2]. The consequences from falling can be fatal in extreme cases or can lead to serious health problems [1], [3]. Thus, several studies have focused on the causes, consequences, and prevention methods of falls in the elderly population [5-7]. On the other hand, fall detection methods have been explored increasingly during the last two decades. Although several techniques are commonly used to detect a fall incident, studies are often classified into two main groups: accelerometer/gyroscope-based and video-based [8-11]. While accelerometer-based sensors typically have to be carried by the subject on certain locations of the body, such as one’s waist, knees, or head, video-based systems suffer from complicated installation procedures and privacy issues that stem from monitoring [8-12]. In this paper, the authors focus on the application of accelerometer-based sensors, which rely on fluctuations in tilt and motion to detect a fall incident. The majority of accelerometer-based fall detectors rely on 2D or 3D accelerometers and/or gyroscopes. In most cases, the location of the sensing device determines the accuracy of the system. Typically, multiple sensors in different regions of the body were employed in past studies and threshold-based fall detection algorithms were utilized [11], [1315]. However, these prior works relied on the sensor to be fixed on the waist, which limited the practical use of the system. Commercial sensor boards were also implemented as wearable fall and posture detectors [16], [17]. Although all these systems reached high sensitivity and specificity levels, they either utilized more than one worn sensor or a limited, fixed location of the sensor itself. Healthcare companies have also been working on efficient and affordable solutions that can help with a fall incident. Most of the products are simply push-button-activated emergency call systems in which users carry a wirelessenabled button, usually worn as a wristband, pendant, or belt clip, requiring button activation when the fall occurs. This signal simply activates a phone call to the healthcare unit and a 2-way telephone communication is established [18-20]. Sensitive floor mats have also been used to track a person and determine if he/she reached the bedroom door while traveling from the bed [21]. Some of the most common systems currently on the market are not phone-based applications and, hence, require a separate sensing system and charge a monthly fee. For example, The Brickhouse Alert Fall Monitoring System relies on a CST (Custom Sensors and Technology) sensing system with 24-hr service. Similarly, Link to Life Fall Detector, CST-L TL, and Prime Medical Alert Amber Select use the same technology [22], [23]. Each relies on a tilt sensor to detect a fall and has a device attached to the person, most commonly a pendantFROM PIT TO LONG LIE: A FALL-DETECTION ALGORITHM FOR SMARTPHONES ——————————————————————————————————————————————–———— Jeffry A. Pulcifer-Stump, Central Michigan University; Patrick Seeling, Central Michigan University; Janet L. Koch, Garden City High School; Steven Kettler, Alma High School; Tolga Kaya, Central Michigan University ——————————————————————————————————————————————————– FROM PIT TO LONG LIE: A FALL-DETECTION ALGORITHM FOR SMARTPHONES 23 ——————————————————————————————————————————————–———— ——————————————————————————————————————————————–———— 24 INTERNATIONAL JOURNAL OF MODERN ENGINEERING | VOLUME 15, NUMBER 2, SPRING/SUMMER 2015 style device. One of the most complete devices is Halo Monitoring, which uses an accelerometer sensor similar to this smartphone application (App). It is worn on either the chest or on a belt clip and tracks vital signs, sleeping patterns, and activity levels. Caregivers are notified by user preference but the systems require a landline or computer for tracking. While there are successful prototypes and products available today, almost all of the systems require significant attention by the user and feature rigorous installation procedures. An affordable and easy-to-use point-of-care solution for fall detection and notification has attracted significant interest. With recent advances in smartphone technology, researchers have been taking advantage of using embedded sensors that are readily available in the phone and have been developing algorithms for fall detection [24-27]. However, most of the algorithms currently require that the phone be kept in a certain orientation. Smartphone Apps such as T3Lab Fall Detection and Fade are free and utilize the accelerometer sensor built into the phone. The T3Lab App, which continuously runs in the background of the smartphone, allows an alarm to sound when a fall is detected. However, the App is very sensitive and can easily be false-triggered. The Fade App requires the user to turn it on and off and has a recovery feature that allows for movement beyond a potential fall impact. With an aging population that features increasing numbers of adults over the age of 65 that remain independently living in their homes, the need for reliable fall detection is apparent. The convenience of a fall-detection App downloaded to one’s smartphone creates a sense of security, as daily activities are carried out without the need for any extra equipment. In this paper, the authors propose an Androidbased smartphone as a platform for fall detection. The proposed algorithm is based on multiple thresholds including free fall, impact, orientation cross, and a long lie. The algorithm is able to successfully detect an occurring fall, regardless of the device’s orientation and placement. The proposed approach enables reliable and accurate fall incident monitoring through the utilization of smartphone applications by the elderly, which allows them to live independently in their own homes. Furthermore, the authors focus on the materials and methods of the fall-detection algorithm and test protocol. Fall Detection Algorithm The proposed algorithm was developed and tested using an HTC 4G LTE smartphone, which utilizes the Android 4.1. 1 (“Jelly Bean”) operating system. The phone itself represents the current mid-level range of smartphones available to users, with an approximate price of $200 with carrier subventions. The program’s offline data storage system allows gathering of data in real time during program execution, with data processing available at a later time. A more in-depth description of the general Android application environment, which also features a simple interface suitable for a target population group and safeguards for real-life scenarios (e.g., setting a personal calling number instead of 911) can be found in the study by Oner et al. [27]. Acceleration data were collected using the device’s embedded accelerometer sensor in the fastest sampling mode, which was 50 samples per second (i.e., one sample every 20 ms). The overall geometric average of the acceleration points, A, in each direction was calculated using Equation (1):