ON DEVELOPING A REAL-TIME FALL DETECTING AND
PROTECTING SYSTEM USING MOBILE DEVICE
Bing-Shiang Yang, Yu-Ting Lee, and Cheng-Wei Lin
Biomechanics and Medical Application Laboratory, Department of Mechanical Engineering,
National Chiao Tung University, 1001 University Road, Hsinchu, ROC

Trip and slip falls frequently cause injuries in workplace and to the elderly during
activities of daily living (ADLs). In recent years, several fall detecting algorithms
have been developed using inertial measurement units (IMU). Although highsensitivity of fall detections could be reached based on self-initiated or pretending
falls, and signals resulting from fall impact, real-time fall detecting, i.e. after fallinitiation and before the impact, and hence reducing fall impact or serious injuries,
has not been achieved. The goal of this study was to develop an algorithm, using
the sensing signals from IMU of mobile devices (smartphones), to identify fallinitiation, to provide sufficient lead time to trigger protection devices before impact,
and to demonstrate the feasibility of real-time fall detecting and protecting using amobile device.

Introduction
According to previous research, 15.9% of the elderly over 65 years old and 20.8% of the elderly
over 80 years old experienced unexpected falls in 2008 (Stevens et al., 2008). Falling in theelderly could result in variety of physical injuries, such as bruises and strains, and psychologicaltraumas, such as self-restriction of activities due to fear of falling (Arfken et al., 1994; Salkeld
et al., 2000). Without doubt, fall-related issues, such as fall detection, prevention,protection…etc. are worthy of attention.
Previous research defined falls as certain part of body, e.g. hand, touches the ground orreaches beneath the level of knees (Zhang et al., 2006). Further research synoptically classified
falls into trip, slip, step down and faint, among which trip and slip are the most common(Smeesters et al., 2001). In order to detect falls, several detection methods have been developed.
Motion capture systems were extensively utilized in biomechanics fields and early research offall detections. However, more recent studies substituted motion capture systems for wearable
IMU (inertial measurement unit) systems because of their convenience and better feasibility,and analyzed the detection accuracy by the concepts of sensitivity and specificity (Lord &Colvin, 1991). Nowadays, prevalence of commercialized mobile devices, like smartphones andpad computers, have drawn tremendous attention and allowed sensors embedded to be moreavailable with lower cost.
The purpose of this study was to develop an algorithm, using IMU of smartphone, toidentify fall initiation and trigger a protecting device to reduce fall injuries and to demonstrate
the feasibility of real-time fall detecting and protecting using a mobile device.

- 151 -Material and Method
Subjects

To develop the detecting algorithm, twelve healthy subjects (height: 1.69 ± 0.03 m; weight:67.33 ± 7.52 kg; age: 24.30 ± 2.02 yrs.) were recruited to perform ADLs, unexpected slip andtrip falls. To verify whether the developed algorithm and threshold were feasible, other thirty
healthy subjects (height: 1.69 ± 0.04 m; weight: 63.17 ± 7.37 kg; age: 24.3 6 ± 2.04 yrs) wererecruited to perform unexpected slip and trip during level walking. All subjects had no previous
history of musculoskeletal injuries that would jeopardize the performance of tasks, and wereprovided written informed consent to participate in this study, which has been approved by theInstitutional Review Board of Mackay Memorial Hospital.

Experimental Environment

All subjects were asked to perform unexpected falls in a designed experimental path, whichcontains mechanisms on floor to induce trip falls, by wooden plates suddenly popping up frombeneath, and oiled floor surface to increase risk of slip falls.

Hardware

The IMU comprise of a triaxial accelerometer (g sensor) and gyroscope embedded in asmartphone (Samsung Galaxy S2 i9100, Samsung, Korea), operating with Android 2.3.3(Google Inc, USA) system. Due to the combination of different sensors and algorithm
requirements, a compromise on sampling rate, 88-95 Hz for triaxial accelerometer and 92-100
Hz for gyroscope, was reached in this study. Middle of chest was chosen to be the location ofsmartphone in this study, as shown in Figure 1 (Shany et al., 2012).

Figure 1. Position and orientation of IMU

ADLs and unexpected falls test

Twelve subjects were asked to perform each group of ADLs in a randomized order, shown in
Table 1, for ten times.
Group 1x Seatedx Sit to standx Squattingx Squat to standGroup 2

Table 1. Tasks of ADLs testx Getting on bed from standing uprightx Getting on bed with self-selected strategy
x Getting up from bed- 152 -Group 3x Walkingx Going upstairs
x Going downstairs
x Jogging

For unexpected falls, subjects were asked to perform level walking in designed experiment
path with unexpectedly induced slip and trip conditions. To avoid learning effect, each trial
would be ceased right after first trip or slip occurred. During the process, 3D accelerations andangular velocities of the body were recorded simultaneously by the smartphone. The data wereused for developing the fall detecting algorithm.

Real-time test

Thirty subjects were asked to perform unexpected slip and trip, which induced by themechanisms and oiled floor surface in designed experiment path, to verify the sensitivity of thealgorithm and lead time, defined in data processing below. During the process, acceleration andangular velocity of the body were analyzed by algorithm instantly to detect whether a falloccurred.

Data Processing and Statistical Analysis

Acceleration values of all three directions and angular velocities of pitch and roll weresuperposed since rotation in vertical axis was not significant when falling. Kinematically,
downward movements, like falling, show increasing acceleration, whereas upward movements,
like jumping, show decreasing acceleration, in vertical direction. Thus, minimum superposed
acceleration of triaxial acceleration in each task was extracted for fall detection.
To assess system accuracy, we quantified sensitivity, defined as the probability of an actual
fall occurred given that the detector recognized a fall, and specificity, defined as the probabilityof an actual ADL occurred given that the detector recognized an ADL, using the data from theabove-described experiment.
Delay time was defined as the allowed duration between threshold detection based on
acceleration (minimum superposed acceleration was reached) and threshold detection based on
angular velocity (maximum superposed angular velocity was reached).
Lead time, defined as the duration from an actual fall was detected to impact occurred, wastaken into account to verify whether protection devices can be activated in time.

Results
For ADLs and unexpected fall test, acceleration and angular velocity in each task of the twelve
subjects were shown as box-plot in Figure 2.
The range of trip and slip was 1.90 ± 0.90 m/s2. Therefore, the threshold between ADLs anddetected fall was chosen to be 4.60 m/s2, which included 99.7% of falls, assuming theprobability of falls were normally distributed. The threshold of angular velocity was chose to be
3.6 rad/s2. If the superposed acceleration or angular velocity exceeded the chosen thresholds
during any task, an alarm will be displayed on the smartphone, and a fall was consideredidentified.

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Figure 2. Minimum superposed acceleration and maximum superposed angular velocity ofADLs and unexpected fall test
As shown in Figure 2 acceleration part, going downstairs and jogging, which involved inrelatively large motion in the vertical direction, might confuse the algorithm and result in low
specificity. However, by using two type of sensors (accelerometer and gyroscope) to detected
fall collaboratively with OR logic gate, specificity can be increased significantly as compared tousing one type of sensor alone. Table 2 presents the specificity of fall detector during ADLs testwith triaxial accelerometer and gyroscope respectively and collaboratively. For collaborative
detection using both triaxial accelerometer and gyroscope, a 255ms delay time was chosen
according to our previous study (Lin, 2013).
Table 2. Specificity during ADLs test with sensors respectively (Acc: accelerometer only;Gyro: gyroscope only) and collaboratively (Both: using both accelerometer and gyroscope)Group 1Acc.
Gyro.
BothSquatting
79.17%
100.00%100.00%Squat to stand79.17%
99.16%
99.17%
Seated84.17%
86.67%
98.33%
Sit to stand83.33%
90.00%
96.67%
Group 2
Acc.
Gyro.
Both
Getting on bed from standing upright75.83%
38.00%
79.17%
Getting up from bed
73.33%
65.00%
86.33%
Getting up from bed with self-selected strategy 56.67%
17.50%
60.00%
Group 3Acc.
Gyro.
BothWalking
84.17%
95.00%
100.00%Going downstairs
17.50%
84.16%
84.16%
Going upstairs
63.33%
76.67%
82.50%
Jogging
0.00%
6.67%
6.67%
After setting the thresholds and delay time, the establishment of algorithm of our system
was completed. Figure 3 illustrates the step-by-step structure of the fall detecting algorithm.

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Figure 3. Structure of the algorithm
Sensitivity and specificity of real-time test, which has been performed by thirty subjects, areshown in Table 3. Furthermore, to verify that our system was capable of providing sufficientlead time for protection devices, results are shown in Table 4.
Table 3. Sensitivity, specificity of our system during real-time test
Actual fall++95.24%
5.75%
Detecting results
4.76%
94.25%
Table 4. Lead time of each slip and trip during real-time test
Lead time (ms)Average
S.D.
Max
Min
Trip
173
53249
101
Slip (lying down)
157
18178
135
Slip (sitting)
95
28
132
51

Discussion
This study demonstrated the feasibility of developing a fall detecting system with IMU
embedded in smartphones to achieve real-time fall recognition and to provide sufficient leadtime to trigger protection devices.
Certain activities in ADLs, such as getting on bed from standing upright and jogging, would
significantly confuse the algorithm with falls and lead to low specificity of the system.
Furthermore, although high sensitivity and specificity were achieved in this study, variousADLs, which were excluded in this study, might lead to misjudgments.
Commercialized protection devices, such as airbags, need approximate 35ms to achieve fullprotection function to reduce impact. Our results show sufficient lead time for protection
devices to fully activate, if airbags or similar protecting devices are used, in both trip and slip
falls, although protecting device has not been integrated into our system yet. Furthermore, withadvance of micro-electro-mechanical techniques, high precision and accuracy and, as a result,better sensitivity may be achieved.
Due to safety concerns, elderly subjects were excluded in our experiment during thedeveloping process. Young and healthy subjects might not be able to represent certain group ofpeople who actually need fall detectors. Therefore, our future studies will focus on theindividualization of the detecting algorithm to make fall detector more suitable for different
users.

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