Fall detection algorithm in energy efficient multistate sensor system Gundars Korats, Janis Hofmanis, Aleksejs Skorodumovs, Egils Avots
Abstract— Health issues for elderly people may lead to different injuries obtained during simple activities of daily living (ADL). Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. Many fall detection systems are proposed but only recently such health care systems became available. Nevertheless sensor design, accuracy as well as energy consumption efficiency can be improved. In this paper we present a single 3-axial accelerometer energy-efficient sensor system. Power saving is achieved by selective event processing triggered by fall detection procedure. The results in our simulations show 100% accuracy when the threshold parameters are chosen correctly. Estimated energy consumption seems to extend battery life significantly.
I. INTRODUCTION Falling is known as potentially dangerous event especially for the elderly people. According to the [1], it is estimated that over a third of adults older than 65 years, fall each year, making it the leading cause of the injury or even death for that age group. As part of the falls are with uncontrolled nature caused by some specific disease, the injured person can lay on the ground unconsciousness risking not to receive required medical help. Different solutions were developed by researchers over the last decade to avoid such dangerous threats. Chen [2] and Lindemann [3] proposed similar studies which use noninvasive accelerometer sensor worn on the waist or integrated into the hearing aid housing. The proposed methods are in fact very simple: the impact is detected using simple empirically adjusted threshold. In the case of no activity, the orientation is calculated to analyze the posture of the patient. If the impact is detected and posture does not change, the localization begins. This simple approach is with low computational cost but with difficult empirical choice of the parameters, required to detect the event of the fall. Later Kangas in [4] attached a tri-axial accelerometer to the waist, wrist and head. The measurements of standardized falls was collected together with daily living activities (ADL) (e.g., walking, walking on the stairs, picking up objects from the floor). Then the performance were compared using four different low-complexity algorithms. First two algorithms are based on impact detection + posture analysis. Latter and more advanced ones are based on start of fall detection + velocity + fall impact detection + posture analyzing. A more advanced threshold-based low cost fall detection algorithm was proposed by Li in [5] and later also by Gjoreski in [6]. In addition to impact detection they both tried to recognize some basic postures by imposing additional All authors are with Ventspils University College, 101 Inzenieru iela, LV-3601, Ventspils, Latvia [email protected]
978-1-4244-9270-1/15/$31.00 ©2015 IEEE
angular rate. Li in his paper detects the impact and analyzes the present posture. If the transition between the two postures was intentional, the fall is detected. Whereas Gjoreski is focusing on the fall pattern meaning that only after the decrease (fall) follows huge peak (impact). In many studies accelerometer sensors are used as a single sensor system which has a limitation of activity type recognition. To overcome this difficulty, complex classifiers on multisensor data can improve the overall recognition accuracy. Gao in his research [7] shows the performance of SVM, KNN, Naive Bayes and decision tree classifiers one data acquired using a wearable multiple sensor system where sensors are distributed on different body locations. The results shows that all classifiers perform better when multiple sensors are used instead of a single sensor system. The drawback of such approach is the implementation and computation cost of such complex classifiers. In this paper we focus on fall detection procedure without any particular interest of specific ADLs recognition. The main motivation of this work is to develop efficient single sensor threshold based fall detection algorithm using single tri-axial accelerometer with very low power consumption. II. PROBLEM DESCRIPTION A. Hardware The spatial dimensions of the sensor should be as minimal and modest as possible taking into account the complexity of device (including sensors, controllers and message transfer) and keeping high energy efficiency. To achieve that, we use ultra low-power high performance three-axis ”nano” 3-axis accelerometer LIS3DSH www.st.com. The advantage of this accelerometer is that it embeds two state machines which are able to run a user defined program. This allows to partly eliminate the need for micro-controllers which typically are the main energy consumers and wake the system in active mode only at some predefined trigger events. The built test device is presented in Figure 1 with integrated USB port for raw data collection used for testing and validation of the algorithm. Two operational modes will obtain the accelerometer data with different sampling frequencies thus further reducing the energy consumption separating sleep mode from active mode. We address here the problem of the choice of optimal signal sampling frequency. Existing technologies allows to split the event detection problem in separate tasks by performing simple background monitoring and switching to energy consuming calculations only at triggered event. Thus the implementation of optimal algorithms and parameters of such decision system must be adopted and validated.
4974
Fig. 1: Fall detection test device with LIS3DSH accelerometer (dimensions ∼ 3 × 3cm)
experience unintentional fall on the ground. Instantaneous orientation vector is given as follows: rt = acctx , accty , acctz (3) where t denotes the time instance. This yields a possibility to track the sensor orientation change over time and monitor the wearers activity. For stability reasons one might calculate mean orientation vector r¯∆t instead of rt where r¯∆ t represents the mean vector over multiple sample points. C. Signal power estimate of total acceleration vector
B. Sensor placement Researchers in the last decade emphasize the problem of optimal sensor placement. Chen in [2] claims that for the sensors placed on the wrist due to the frequent and severe movements of the arm in everyday activities make it difficult to determine the performed activity by observing acceleration forces. Gao in [7] shows the efficiency of classification algorithms depending on sensor placement. From the his results we conclude that the best performance of single sensor setup is obtained when accelerometer is placed on the chest. Similar, for our experiments, we put the sensor in the front right pocket thus it is attached to the right shoulder and perform the tests only for corresponding setup.
Due to the fact that impulses contains high signal energy (broadband frequency activation), we propose to estimate the signal energy and use it as the indicator for the patient impact with the ground. Thus we compute the running estimate of the energy of the signal A by the Teager Energy Operator (TEO) given in equation (4). et = A(t)2 − (A(t + 1)A(t − 1))
The advantage of TEO is that we do not need to store large amount of previous data in order to do the classification. T EO tends to increase significantly if sharp peaks or impulses are present in signal (see an example in figure 2). In case of continuously growing acceleration as, for example, in the car or beginning of fast movement, the total acceleration vector may increase rapidly and reach the threshold whereas instant signal energy does not grow. In figure 3 fast rising and sitting example is presented of such case where fast rising is detected at beginning (0.6-0.7 seconds) and then sitting with impact. In this case energy estimate of rising is insignificant but total acceleration A reaches almost 3 G.
III. METHODS A. Total acceleration vector
A
In a very similar manner as in [8] and many other research, we use a total acceleration vector A: q A = acc2x + acc2y + acc2z (1) where x,y and z are acceleration in each directions. The LIS3DSH accelerometer provides its own total acceleration vector (8-bit value) for the built in state machine using an approximation formula: Aacc = (45a + 77b)/256
B. Orientation A way to describe the movements and transitions is to take into account the orientation change which in most fall cases changes significantly. For example, in case where a person is standing and due to the health issues faints and thus
6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5
1,000 900 800 700 600 500 400 300 200 100 0 −100 7.8
(2)
where a = |accx | + |accy | + |accz | and b = max(|accx | , |accy | , |accz |). This will give us information whether the user experiences acceleration changes in any direction and, thus, can be used to detect the impact without using any mathematical operation in micro-controller.
(4)
7.9 8 Seconds
TEO
To achieve high energy efficiency with no cost of accuracy, we propose a fall detection system which uses two different sampling frequencies. The low frequency data acquisition (sleep mode) in which the free fall is detected using simple empirical threshold method implemented in LIS3DSH state machine. After free fall detection the higher sampling frequency (active mode) is triggered and more detailed signal during possible fall is obtained and analyzed.
8.1
Fig. 2: Total acceleration and corresponding TEO values of fall event.
D. Fall detection algorithm In a very similar manner as shown in [9], we construct our fall detection method using simple threshold based structure. First of all device is in sleep mode and only sensor operates at low sampling frequency producing low resolution acceleration data Asleep . This frequency should
4975
Fig. 4: Fall detection routine
500 400
Run in Sleep mode (Sleep Fs)
300 200
no
TEO
A
5 4.5 4 3.5 3 2.5 2 1.5 1 0.5
Free Fall?
100 yes
0
1. Calculate past orientation 2. Set Active mode (Active Fs)
−100 0.6
0.7 Seconds
0.8
0.9 Calculate TEO for 1 sec
Fig. 3: Total acceleration and corresponding TEO values of fast rising and sitting on the ground.
be large enough to detect free fall pattern and trigger the active mode of higher sampling frequency and ”wake up” micro-controller to calculate more complex signal feature as T EO. Sleep mode is used also to compute mean orientation vector rs ¯ ∆ t. This mean orientation vector will be used to estimate the orientation change during the fall. Once free fall is detected and active mode is triggered, more detailed signal Aactive is obtained and processed by micro-controller. Also here mean orientation vector ra ¯ ∆ t is computed over some data window. Then, using simple cosine law the angle is computed: rs ¯ ∆t · ra ¯ ∆t cos Θ = ||rs ¯ ∆t || ||ra ¯ ∆t || Once impact is detected within one second after free fall detection, the orientation change is monitored for two seconds. In case of inactivity, fall is reported. Complete routine is presented in figure 4. The steps are following after start in sleep mode (Sleep F s): 1. Free Fall? - detect if A < T hr1 using state machine and start micro-controller. In micro-controller: 2. Calculate past orientation - Use whole FIFO buffer of data (32 saved sample records) and calculate past average orientation using (3). 3. Set Active mode (Active F s). 4. Calculate TEO for 1 sec calculate TEO index continuously for 1 seconds. 5. Impact detected? - At every new sample check if T EO > T hr2. 6. Calculate Orientation after 2 seconds - if impact detected, calculate orientation change after 2 seconds. 6. Orientation changed? And Patient inactive?: If past orientation has changed by 60 degrees and variation of accelerometer x, y and z values is less than T hr3. 7. Send event message: Micro-controller sends message of fall detection via communication module to the nearest transceiver (and go back to sleep mode). IV. SIMULATION SETUP The authors of this paper confirm that all experiments on human subjects were performed under the guidelines of the World Medical Association Declaration of Helsinki. We use
no
Impact detected?
yes Calculate Orientation for 2 seconds
no
Orientation changed? and Patient inactive?
yes Send event message
standard polyester mattress (90cm×200cm×15cm) to avoid any injuries that could be caused during the falls. One healthy volunteer agreed to perform multiple activities that are shown in table I. Each ADL was filmed and repeated multiple times TABLE I: Different ADLs and fall events ADL Walking Jumping Sitting up/down on the chair Sitting up/down on the floor
FALL Standing - front Standing - left & right side Sitting on the chair - backward Sitting on the chair - left& right side Knees - forward Knees - left& right side.
for validation purposes. Thus in our simulation 6 different ADL’s together with 9 different types of falls are performed. In simulation we used test device (presented in Figure 1) and collected raw accelerometer data with sampling frequency of 400 Hz for further analysis. The acquired data was further resampled and used for testing by presented algorithm with several Sleep and Active sampling frequency pairs. A. Performance measure As we are interested only in fall recognition, we can use standard binary classification methods. Thus to estimate the performance of fall detection system we estimate true positive (TP), false positive (FP), true negative (TN) as well as false negative (FN) values. Value is assumed as TP only if the fall is occurred and it is recognized. Otherwise it is
4976
classified as FN or false rejected event. Whereas correct rejections will increase TN. Previously described values allows us to perform a well known binary classification test by computing true positive rate (TPR), also known as sensitivity (5). TPR(%) =
TP TP + FN
(5)
We are also interested in true negative rate (6) or specificity (SPC) to estimate the accuracy of rejection. SPC(%) =
TN TN + FP
(6)
Both TPR and SPC will give us details of classification performance that will be analyzed in the next section. V. RESULTS We address here the problem of choosing the optimal sampling frequencies for both active and sleep state. Thus in this section we provide binary classification results among 65 test cases (39 falls and 26 ADL’s) using different sampling frequencies. We tuned TEO and rotation thresholds by running several empirical tests. Finally TEO threshold is fixed at 180 which was the smallest detected TEO value among empirical ”Knees-left& right side” fall tests (TABLE I). Orientation threshold is set to 60 degrees which from our tests was observed more than simple bending or stretching. In TABLE II we can see that indeed, sampling frequency has a strong impact on overall performance. We tested 15 combinations of sampling frequencies and, as expected, higher sampling rate yield better accuracy. One can notice TABLE II: Table of binary classification functions computed among 65 different test cases.
Sleep Fs
3 6 12 25 50
50 TPR / SPC 0.46 / 1.00 0.79 / 1.00 0.92 / 1.00 0.97 / 1.00 0.97 / 1.00
Active Fs 100 TPR / SPC 0.51 / 1.00 0.79 / 1.00 0.92 / 1.00 1.00 / 1.00 1.00 / 1.00
200 TPR / SPC 0.46 / 1.00 0.79 / 1.00 0.90 / 1.00 1.00 / 1.00 1.00 / 1.00
that the limit of perfect classification is achieved with sleep Fs ≥ 25 Hz and active Fs ≥ 100 Hz. Thus we can observe that 25 Hz is enough to detect the free fall whereas for impact detection much higher accuracy is needed. A very important aspect of this work is energy consumption. Using built test sensor we performed 2 hours of ADL’s including sitting, walking, running, climbing stairs, etc., and calculated that free fall trigger by state machine would happen in average 5 − 10% of active wearing time during the day. This is already a significant improvement of energy consumption because, for example, ATxmega32C4 microcontroller in active state consumes around 10mA meanwhile accelerometer consumes only around 5 − 10µA.
VI. CONCLUSIONS In this paper is proposed the design of single-sensor system which consists of instantaneous signal energy estimate using Teager Energy Operator. Energy consumption efficiency is achieved by switching between two different sampling frequencies using built in states machine in sensor. According to the hypothesis of this work, our results shows efficiency in both accuracy and energy consumption. From results we can find optimal sampling frequencies in order to achieve good fall detection accuracy. More subjects must be tested to evaluate the sensitivity on different body characteristics (weight, height, age). Moreover the optimal sensor placement should be investigated because in this work we strongly rely on similar to the standard position used in other research as, for example, in [2], [7]. A very important aspect is detection of free fall in order to trigger the active state only when it is needed. Simple free fall threshold might trigger the active state too often leading to higher energy consumption. Thus future work will focus on more reliable fall detection rather than simple threshold. The next step is to use already designed wireless device with installed battery and perform additional tests to estimate the accuracy in more realistic environment. In addition, more detailed energy consumption will be calculated to precisely predict the battery life. ACKNOWLEDGMENT This research is financed by European Regional Development Fund project 2.8 (No. L-KC-11-0006). Authors thank also Municipality of Ventspils City for additional support. R EFERENCES [1] M. C. Hornbrook, V. J. Stevens, D. J. Wingfield, J. F. Hollis, M. R. Greenlick, and M. G. Ory, “Preventing falls among community-dwelling older persons: results from a randomized trial,” The Gerontologist, vol. 34, no. 1, pp. 16–23, 1994. [2] J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy, “Wearable sensors for reliable fall detection.” Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 4, pp. 3551–4, Jan. 2005. [3] U. Lindemann, A. Hock, and M. Stuber, “Evaluation of a fall detector based on accelerometers: A pilot study,” Medical and Biological . . . , vol. 43, pp. 2–5, 2005. [4] M. Kangas, A. Konttila, P. Lindgren, I. Winblad, and T. J¨ams¨a, “Comparison of low-complexity fall detection algorithms for body attached accelerometers,” Gait and Posture, vol. 28, pp. 285–291, 2008. [5] Q. Li, J. a. Stankovic, M. a. Hanson, A. T. Barth, J. Lach, and G. Zhou, “Accurate, Fast Fall Detection Using Gyroscopes and AccelerometerDerived Posture Information,” 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 138–143, Jun. 2009. [6] H. Gjoreski, M. Luˇstrek, and M. Gams, “Accelerometer placement for posture recognition and fall detection,” Proceedings - 2011 7th International Conference on Intelligent Environments, IE 2011, pp. 47–54, 2011. [7] L. Gao, a. K. Bourke, and J. Nelson, “Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems,” Medical Engineering and Physics, vol. 36, no. 6, pp. 779–785, 2014. [8] R. Luque, E. Casilari, M.-J. Mor´on, and G. Redondo, “Comparison and Characterization of Android-Based Fall Detection Systems,” Sensors, vol. 14, pp. 18 543–18 574, 2014. [9] F. Bagal`a, C. Becker, A. Cappello, L. Chiari, K. Aminian, J. M. Hausdorff, W. Zijlstra, and J. Klenk, “Evaluation of accelerometer-based fall detection algorithms on real-world falls,” PLoS ONE, vol. 7, no. 5, pp. 1–9, 2012.
4977
Abstract— Health issues for elderly people may lead to different injuries obtained during simple activities of daily living (ADL). Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. Many fall detection systems are proposed but only recently such health care systems became available. Nevertheless sensor design, accuracy as well as energy consumption efficiency can be improved. In this paper we present a single 3-axial accelerometer energy-efficient sensor system. Power saving is achieved by selective event processing triggered by fall detection procedure. The results in our simulations show 100% accuracy when the threshold parameters are chosen correctly. Estimated energy consumption seems to extend battery life significantly.
I. INTRODUCTION Falling is known as potentially dangerous event especially for the elderly people. According to the [1], it is estimated that over a third of adults older than 65 years, fall each year, making it the leading cause of the injury or even death for that age group. As part of the falls are with uncontrolled nature caused by some specific disease, the injured person can lay on the ground unconsciousness risking not to receive required medical help. Different solutions were developed by researchers over the last decade to avoid such dangerous threats. Chen [2] and Lindemann [3] proposed similar studies which use noninvasive accelerometer sensor worn on the waist or integrated into the hearing aid housing. The proposed methods are in fact very simple: the impact is detected using simple empirically adjusted threshold. In the case of no activity, the orientation is calculated to analyze the posture of the patient. If the impact is detected and posture does not change, the localization begins. This simple approach is with low computational cost but with difficult empirical choice of the parameters, required to detect the event of the fall. Later Kangas in [4] attached a tri-axial accelerometer to the waist, wrist and head. The measurements of standardized falls was collected together with daily living activities (ADL) (e.g., walking, walking on the stairs, picking up objects from the floor). Then the performance were compared using four different low-complexity algorithms. First two algorithms are based on impact detection + posture analysis. Latter and more advanced ones are based on start of fall detection + velocity + fall impact detection + posture analyzing. A more advanced threshold-based low cost fall detection algorithm was proposed by Li in [5] and later also by Gjoreski in [6]. In addition to impact detection they both tried to recognize some basic postures by imposing additional All authors are with Ventspils University College, 101 Inzenieru iela, LV-3601, Ventspils, Latvia [email protected]
978-1-4244-9270-1/15/$31.00 ©2015 IEEE
angular rate. Li in his paper detects the impact and analyzes the present posture. If the transition between the two postures was intentional, the fall is detected. Whereas Gjoreski is focusing on the fall pattern meaning that only after the decrease (fall) follows huge peak (impact). In many studies accelerometer sensors are used as a single sensor system which has a limitation of activity type recognition. To overcome this difficulty, complex classifiers on multisensor data can improve the overall recognition accuracy. Gao in his research [7] shows the performance of SVM, KNN, Naive Bayes and decision tree classifiers one data acquired using a wearable multiple sensor system where sensors are distributed on different body locations. The results shows that all classifiers perform better when multiple sensors are used instead of a single sensor system. The drawback of such approach is the implementation and computation cost of such complex classifiers. In this paper we focus on fall detection procedure without any particular interest of specific ADLs recognition. The main motivation of this work is to develop efficient single sensor threshold based fall detection algorithm using single tri-axial accelerometer with very low power consumption. II. PROBLEM DESCRIPTION A. Hardware The spatial dimensions of the sensor should be as minimal and modest as possible taking into account the complexity of device (including sensors, controllers and message transfer) and keeping high energy efficiency. To achieve that, we use ultra low-power high performance three-axis ”nano” 3-axis accelerometer LIS3DSH www.st.com. The advantage of this accelerometer is that it embeds two state machines which are able to run a user defined program. This allows to partly eliminate the need for micro-controllers which typically are the main energy consumers and wake the system in active mode only at some predefined trigger events. The built test device is presented in Figure 1 with integrated USB port for raw data collection used for testing and validation of the algorithm. Two operational modes will obtain the accelerometer data with different sampling frequencies thus further reducing the energy consumption separating sleep mode from active mode. We address here the problem of the choice of optimal signal sampling frequency. Existing technologies allows to split the event detection problem in separate tasks by performing simple background monitoring and switching to energy consuming calculations only at triggered event. Thus the implementation of optimal algorithms and parameters of such decision system must be adopted and validated.
4974
Fig. 1: Fall detection test device with LIS3DSH accelerometer (dimensions ∼ 3 × 3cm)
experience unintentional fall on the ground. Instantaneous orientation vector is given as follows: rt = acctx , accty , acctz (3) where t denotes the time instance. This yields a possibility to track the sensor orientation change over time and monitor the wearers activity. For stability reasons one might calculate mean orientation vector r¯∆t instead of rt where r¯∆ t represents the mean vector over multiple sample points. C. Signal power estimate of total acceleration vector
B. Sensor placement Researchers in the last decade emphasize the problem of optimal sensor placement. Chen in [2] claims that for the sensors placed on the wrist due to the frequent and severe movements of the arm in everyday activities make it difficult to determine the performed activity by observing acceleration forces. Gao in [7] shows the efficiency of classification algorithms depending on sensor placement. From the his results we conclude that the best performance of single sensor setup is obtained when accelerometer is placed on the chest. Similar, for our experiments, we put the sensor in the front right pocket thus it is attached to the right shoulder and perform the tests only for corresponding setup.
Due to the fact that impulses contains high signal energy (broadband frequency activation), we propose to estimate the signal energy and use it as the indicator for the patient impact with the ground. Thus we compute the running estimate of the energy of the signal A by the Teager Energy Operator (TEO) given in equation (4). et = A(t)2 − (A(t + 1)A(t − 1))
The advantage of TEO is that we do not need to store large amount of previous data in order to do the classification. T EO tends to increase significantly if sharp peaks or impulses are present in signal (see an example in figure 2). In case of continuously growing acceleration as, for example, in the car or beginning of fast movement, the total acceleration vector may increase rapidly and reach the threshold whereas instant signal energy does not grow. In figure 3 fast rising and sitting example is presented of such case where fast rising is detected at beginning (0.6-0.7 seconds) and then sitting with impact. In this case energy estimate of rising is insignificant but total acceleration A reaches almost 3 G.
III. METHODS A. Total acceleration vector
A
In a very similar manner as in [8] and many other research, we use a total acceleration vector A: q A = acc2x + acc2y + acc2z (1) where x,y and z are acceleration in each directions. The LIS3DSH accelerometer provides its own total acceleration vector (8-bit value) for the built in state machine using an approximation formula: Aacc = (45a + 77b)/256
B. Orientation A way to describe the movements and transitions is to take into account the orientation change which in most fall cases changes significantly. For example, in case where a person is standing and due to the health issues faints and thus
6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5
1,000 900 800 700 600 500 400 300 200 100 0 −100 7.8
(2)
where a = |accx | + |accy | + |accz | and b = max(|accx | , |accy | , |accz |). This will give us information whether the user experiences acceleration changes in any direction and, thus, can be used to detect the impact without using any mathematical operation in micro-controller.
(4)
7.9 8 Seconds
TEO
To achieve high energy efficiency with no cost of accuracy, we propose a fall detection system which uses two different sampling frequencies. The low frequency data acquisition (sleep mode) in which the free fall is detected using simple empirical threshold method implemented in LIS3DSH state machine. After free fall detection the higher sampling frequency (active mode) is triggered and more detailed signal during possible fall is obtained and analyzed.
8.1
Fig. 2: Total acceleration and corresponding TEO values of fall event.
D. Fall detection algorithm In a very similar manner as shown in [9], we construct our fall detection method using simple threshold based structure. First of all device is in sleep mode and only sensor operates at low sampling frequency producing low resolution acceleration data Asleep . This frequency should
4975
Fig. 4: Fall detection routine
500 400
Run in Sleep mode (Sleep Fs)
300 200
no
TEO
A
5 4.5 4 3.5 3 2.5 2 1.5 1 0.5
Free Fall?
100 yes
0
1. Calculate past orientation 2. Set Active mode (Active Fs)
−100 0.6
0.7 Seconds
0.8
0.9 Calculate TEO for 1 sec
Fig. 3: Total acceleration and corresponding TEO values of fast rising and sitting on the ground.
be large enough to detect free fall pattern and trigger the active mode of higher sampling frequency and ”wake up” micro-controller to calculate more complex signal feature as T EO. Sleep mode is used also to compute mean orientation vector rs ¯ ∆ t. This mean orientation vector will be used to estimate the orientation change during the fall. Once free fall is detected and active mode is triggered, more detailed signal Aactive is obtained and processed by micro-controller. Also here mean orientation vector ra ¯ ∆ t is computed over some data window. Then, using simple cosine law the angle is computed: rs ¯ ∆t · ra ¯ ∆t cos Θ = ||rs ¯ ∆t || ||ra ¯ ∆t || Once impact is detected within one second after free fall detection, the orientation change is monitored for two seconds. In case of inactivity, fall is reported. Complete routine is presented in figure 4. The steps are following after start in sleep mode (Sleep F s): 1. Free Fall? - detect if A < T hr1 using state machine and start micro-controller. In micro-controller: 2. Calculate past orientation - Use whole FIFO buffer of data (32 saved sample records) and calculate past average orientation using (3). 3. Set Active mode (Active F s). 4. Calculate TEO for 1 sec calculate TEO index continuously for 1 seconds. 5. Impact detected? - At every new sample check if T EO > T hr2. 6. Calculate Orientation after 2 seconds - if impact detected, calculate orientation change after 2 seconds. 6. Orientation changed? And Patient inactive?: If past orientation has changed by 60 degrees and variation of accelerometer x, y and z values is less than T hr3. 7. Send event message: Micro-controller sends message of fall detection via communication module to the nearest transceiver (and go back to sleep mode). IV. SIMULATION SETUP The authors of this paper confirm that all experiments on human subjects were performed under the guidelines of the World Medical Association Declaration of Helsinki. We use
no
Impact detected?
yes Calculate Orientation for 2 seconds
no
Orientation changed? and Patient inactive?
yes Send event message
standard polyester mattress (90cm×200cm×15cm) to avoid any injuries that could be caused during the falls. One healthy volunteer agreed to perform multiple activities that are shown in table I. Each ADL was filmed and repeated multiple times TABLE I: Different ADLs and fall events ADL Walking Jumping Sitting up/down on the chair Sitting up/down on the floor
FALL Standing - front Standing - left & right side Sitting on the chair - backward Sitting on the chair - left& right side Knees - forward Knees - left& right side.
for validation purposes. Thus in our simulation 6 different ADL’s together with 9 different types of falls are performed. In simulation we used test device (presented in Figure 1) and collected raw accelerometer data with sampling frequency of 400 Hz for further analysis. The acquired data was further resampled and used for testing by presented algorithm with several Sleep and Active sampling frequency pairs. A. Performance measure As we are interested only in fall recognition, we can use standard binary classification methods. Thus to estimate the performance of fall detection system we estimate true positive (TP), false positive (FP), true negative (TN) as well as false negative (FN) values. Value is assumed as TP only if the fall is occurred and it is recognized. Otherwise it is
4976
classified as FN or false rejected event. Whereas correct rejections will increase TN. Previously described values allows us to perform a well known binary classification test by computing true positive rate (TPR), also known as sensitivity (5). TPR(%) =
TP TP + FN
(5)
We are also interested in true negative rate (6) or specificity (SPC) to estimate the accuracy of rejection. SPC(%) =
TN TN + FP
(6)
Both TPR and SPC will give us details of classification performance that will be analyzed in the next section. V. RESULTS We address here the problem of choosing the optimal sampling frequencies for both active and sleep state. Thus in this section we provide binary classification results among 65 test cases (39 falls and 26 ADL’s) using different sampling frequencies. We tuned TEO and rotation thresholds by running several empirical tests. Finally TEO threshold is fixed at 180 which was the smallest detected TEO value among empirical ”Knees-left& right side” fall tests (TABLE I). Orientation threshold is set to 60 degrees which from our tests was observed more than simple bending or stretching. In TABLE II we can see that indeed, sampling frequency has a strong impact on overall performance. We tested 15 combinations of sampling frequencies and, as expected, higher sampling rate yield better accuracy. One can notice TABLE II: Table of binary classification functions computed among 65 different test cases.
Sleep Fs
3 6 12 25 50
50 TPR / SPC 0.46 / 1.00 0.79 / 1.00 0.92 / 1.00 0.97 / 1.00 0.97 / 1.00
Active Fs 100 TPR / SPC 0.51 / 1.00 0.79 / 1.00 0.92 / 1.00 1.00 / 1.00 1.00 / 1.00
200 TPR / SPC 0.46 / 1.00 0.79 / 1.00 0.90 / 1.00 1.00 / 1.00 1.00 / 1.00
that the limit of perfect classification is achieved with sleep Fs ≥ 25 Hz and active Fs ≥ 100 Hz. Thus we can observe that 25 Hz is enough to detect the free fall whereas for impact detection much higher accuracy is needed. A very important aspect of this work is energy consumption. Using built test sensor we performed 2 hours of ADL’s including sitting, walking, running, climbing stairs, etc., and calculated that free fall trigger by state machine would happen in average 5 − 10% of active wearing time during the day. This is already a significant improvement of energy consumption because, for example, ATxmega32C4 microcontroller in active state consumes around 10mA meanwhile accelerometer consumes only around 5 − 10µA.
VI. CONCLUSIONS In this paper is proposed the design of single-sensor system which consists of instantaneous signal energy estimate using Teager Energy Operator. Energy consumption efficiency is achieved by switching between two different sampling frequencies using built in states machine in sensor. According to the hypothesis of this work, our results shows efficiency in both accuracy and energy consumption. From results we can find optimal sampling frequencies in order to achieve good fall detection accuracy. More subjects must be tested to evaluate the sensitivity on different body characteristics (weight, height, age). Moreover the optimal sensor placement should be investigated because in this work we strongly rely on similar to the standard position used in other research as, for example, in [2], [7]. A very important aspect is detection of free fall in order to trigger the active state only when it is needed. Simple free fall threshold might trigger the active state too often leading to higher energy consumption. Thus future work will focus on more reliable fall detection rather than simple threshold. The next step is to use already designed wireless device with installed battery and perform additional tests to estimate the accuracy in more realistic environment. In addition, more detailed energy consumption will be calculated to precisely predict the battery life. ACKNOWLEDGMENT This research is financed by European Regional Development Fund project 2.8 (No. L-KC-11-0006). Authors thank also Municipality of Ventspils City for additional support. R EFERENCES [1] M. C. Hornbrook, V. J. Stevens, D. J. Wingfield, J. F. Hollis, M. R. Greenlick, and M. G. Ory, “Preventing falls among community-dwelling older persons: results from a randomized trial,” The Gerontologist, vol. 34, no. 1, pp. 16–23, 1994. [2] J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy, “Wearable sensors for reliable fall detection.” Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 4, pp. 3551–4, Jan. 2005. [3] U. Lindemann, A. Hock, and M. Stuber, “Evaluation of a fall detector based on accelerometers: A pilot study,” Medical and Biological . . . , vol. 43, pp. 2–5, 2005. [4] M. Kangas, A. Konttila, P. Lindgren, I. Winblad, and T. J¨ams¨a, “Comparison of low-complexity fall detection algorithms for body attached accelerometers,” Gait and Posture, vol. 28, pp. 285–291, 2008. [5] Q. Li, J. a. Stankovic, M. a. Hanson, A. T. Barth, J. Lach, and G. Zhou, “Accurate, Fast Fall Detection Using Gyroscopes and AccelerometerDerived Posture Information,” 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 138–143, Jun. 2009. [6] H. Gjoreski, M. Luˇstrek, and M. Gams, “Accelerometer placement for posture recognition and fall detection,” Proceedings - 2011 7th International Conference on Intelligent Environments, IE 2011, pp. 47–54, 2011. [7] L. Gao, a. K. Bourke, and J. Nelson, “Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems,” Medical Engineering and Physics, vol. 36, no. 6, pp. 779–785, 2014. [8] R. Luque, E. Casilari, M.-J. Mor´on, and G. Redondo, “Comparison and Characterization of Android-Based Fall Detection Systems,” Sensors, vol. 14, pp. 18 543–18 574, 2014. [9] F. Bagal`a, C. Becker, A. Cappello, L. Chiari, K. Aminian, J. M. Hausdorff, W. Zijlstra, and J. Klenk, “Evaluation of accelerometer-based fall detection algorithms on real-world falls,” PLoS ONE, vol. 7, no. 5, pp. 1–9, 2012.
4977
