Implementation and Testing of a Secure Fall Detection System for Body Area Networks Stevan Marinkovic, Riccardo Puppo, Roberto Lan Cian Pan and Emanuel Popovici Abstract—As the average age of the people in the Western world increases, so does the number of orthopaedic medical treatments due to falls. A method for better treatment of this type of injuries consists of early and accurate fall detection. We present an implementation and test of the system for accurate fall detection. First, a number of tests were performed to get thresholds for distinguishing falls from fall-like or normal behaviour. Then, the algorithm was implemented and tested on Biomobius, a technology platform, which allows researcher to rapidly develop sophisticated technology solutions for biomedical research. Sensors for gathering the acceleration data were part of the Tyndall Mote, a wireless sensor platform designed at Tyndall National Institute, Cork. Data is transmitted wirelessly to the PC. Therefore, since security has to be also considered in such medical systems, an encryption system is also implemented and tested on a low power microprocessor.
I. Introduction According to many recent studies, such as [1], by 2050 the human population will probably be larger by 2 to 4 billion people and older than in the 20th century. With the rise in the average age, rise will also happen in the number and cost of medical treatments. A big portion of these medical treatments in elderly population will be due to fall related injuries as reported in [2]. A method for better treatment of this type of injuries consists of early and accurate fall detection, and raising the alarm if the person is immobilized by the fall. This can be done by monitoring systems in the form of Body Area (sensor) Networks (BAN), where patients are monitored constantly at home for any unusual scenarios that may arise. Wireless Body Area Networks (WBAN) are specific types of networks where medical data is transmitted wirelessly from sensors attached to one’s body. There are number of possible sensors that can form a WBAN, such as EEG, ECG, EMG sensors, temperature, pulse rate sensors etc. If a network is to be used for fall detection, the best device that can be used is the accelerometer. Today’s accelerometers have a reasonably low power consumption and price and therefore can be used in practical networks for fall detection. Accelerometer is used as a component to detect movement (as slight S. Marinkovic and E. Popovici are with the Department of Microelectronic Engineering, University College Cork, Ireland (
[email protected],
[email protected]). R. Puppo and L. C. Pan are with the Dipartimento di Ingegneria Biofisica ed Elettronica, Universit degli studi di Genova, Genova, Italy. (
[email protected],
[email protected])
changes in acceleration), hits (as sudden abrupt changes in acceleration), as well as the general position of the device, based on gravity. This makes this component perfect for fall detection, and a variety of algorithms were developed to use this data and to detect falls. Examples of those are [3–8]. Also, there are systems that implement various fall detection strategies using accelerometers. Two such systems are presented in [9] and [10]. We developed a system for fall detection using the algorithm presented in [11]. The hardware used for sensing the acceleration and encrypted wireless communication to the monitoring station is a 25mm Tyndall Mote (Fig.1) [12]. This mote is a full independent, battery operated system, consisted of number of layers. For the project, sensor layer, communication layer and processing layer were used. The platform used for implementation of the fall detection algorithm and decryption of the data is Eyesweb [13], an open software platform developed by InfoMus Lab at the University of Genoa. Graphical User Interface (GUI), is Biomobius [14], which is a platform built on EyesWeb. The full system consists of: a 3-axis accelerometer, which is contained within the 25mm Mote, the communications radio link between the Mote and the receiver mote, the receiver mote, linked to the PC through serial cable and the PC itself to perform the data processing and fall detection. Since this system is used to transmit confident medical data, security issues in the wireless transmission were also considered, therefore a private key XXTEA algorithm for data encryption was also implemented. II. Fall Detection A fall can be defined as unexpected brisk movement to the ground due to a body imbalance. The fall can provide sometimes a loss of consciousness, fractures and leading to morbidity and mortality especially in the elderly. Furthermore, elderly persons face additional risks if they are unable to move and cannot call for appropriate medical attention as soon as possible. The ADXL330 Accelerometer used in the system has an accuracy of ± 10% and a range of ± 3g. The device is also particularly useful for a long-term wearable sensor application as it requires a low operating voltage of only 1.8V and 180µA.
A. Measurements For the implementation of the fall algorithm is important to define what is a fall, and distinguish it from regular behaviour or fall-like behaviour. To understand how the acceleration and orientation of the accelerometer changes as a person falls two studies must be conducted. The first is to analyse non-fall conditions in order to determine a basis for acceptable acceleration values indicative of normal behaviour. The second is to test these conditions by invoking fall like behaviour to test the bounds of normal activity. First, the non-fall conditions were tested. Three situations were analysed. Scenarios that were used are walking, sitting down quickly and Standing. Analysis were performed by calculating the change in acceleration for each recorded point in the window of 10 seconds. The largest change in acceleration occurred during the Quick Sitting test and represented an absolute change in acceleration of up to 0.6g. This value will be used as a threshold for determining if the delta acceleration values are indicative of a fall or of normal activity. Fig.2 shows the all three axis accelerations during a standing, walking and quick sitting session, respectively, within a 10 seconds window. In the first graph, the standing session, the values are close to zero (norm averaging ±0.03g) and the peaks are only due to body vibrations and noise. Board angle stays the same for the whole duration of the test. Second graph shows the accelerations measured during the walking test. It shows the periodic behaviour as expected, with norm peaks of around ±0.1g during normal walking or around ±0.3g during a faster step or foot knock on the floor. Board angle does not change much from the standing test. Finally, third graph shows the accelerations during the series of quick sit downs and stand ups. One can note that the values are somewhat higher than of the fast walking, and can go from 0.3g up to 0.55g in the vertical axis. Therefore, the threshold to distinguish fall or a hard knock (fall-like behaviour) from a normal behaviour is set to be 0.6g. One can note that in all three cases, DC value of all axes is zero. This is because the FIR high pass filter was used to cut off the accelerometer’s DC values due to gravitation. Basically to distinguish a fall, only the change in acceleration is considered, and the DC values are used to determine the board orientation. The mote (Fig.1) is capable of handling a sample rate up to 250 Hz, however this rate is not necessary for this application and a reduction in the sampling rate will reduce the power consumption of the system. It was found that the time required for a person to fall straight to the ground, as if their feet were to slip out from underneath them can be approximated to 0.41 seconds. If one assumes that this is the quickest path to the ground then one should report data at a sample rate higher then
Fig. 1. Tyndall 25mm node
2.44 Hz. Of course, this is an absolute minimal value of the sample rate and for a reasonable application one should have at least 20-30Hz sample rate per channel. B. Fall detection algorithm We implemented and tested the algorithm described in [11]. The structure of the algorithm, implemented on [13] can be seen on Fig.3. The algorithm detects a fall using the threshold acceleration value. If the threshold value is crossed an event happened, which will be referred as impact. The algorithm first waits for two seconds after the first impact, and resets the waiting time if multiple impacts happen. Once the system is in a steady state (two seconds have passed without impact), it calculates the board position one second before the impact, and two seconds after the last impact, and decides if it is ”fall” or ”fall like” behaviour. Data is filtered using the FIR high pass filters to extract the body accelerations. The cut-frequency depend on the sampling frequency and it can be changed as a parameter within Eyesweb application. Setting thresholds for each of the three axes of measurement does not work well, because it does not cover all the possible directions of impact in a uniform way. To consider the acceleration uniformly, the norm of the three axes can be taken, which is the magnitude of acceleration in three-dimensional space where three acceleration values are on the 3d axes. The norm is calculated at the rate that the accelerometers are sampled and when it exceeds a threshold (0.6g) then it is possible that a fall has occurred. The norm is calculated as follows: q |a| = a2x + a2y + a2z (1) where ax , ay and az are filtered acceleration values. Once a high acceleration value has been found it is necessary to check backwards over the previous 3 seconds in order to determine if a high change in acceleration has occurred and if this behaviour is voluntary. To distinguish the will of movements the algorithm checks the previous three seconds; if at least one more peak is
Fig. 2. Measured accelerations during a 10 second sitting, walking and quick sitting test
present, which differs from the current one for a delta acceleration threshold, it decides for a voluntary dynamic behaviour. If instead there are no more peaks in the previous three seconds except the high delta acceleration overcome the threshold it is detected as a fall like behaviour. After the fall-like behaviour is detected, then the orientation of the device is monitored for 5 seconds after the high acceleration is detected, and the average is calculated. The board position (board angle), is calculated using the DC components of the signal, using the 3-dimensional space trigonometry and DC acceleration values for every axis (their difference from 1g). The threshold for this orientation is set to an average of 60 degrees, representing a person inclined at such an angle that is unlikely to be reached standing. Therefore person is most likely laying on the floor. If the situation happens, the fall is declared. Fig.4 presents the operation of the algorithm, as seen in Biomobius program. The graph represents the mean value of the acceleration. The first text box shows if it is walking (1) or risky behaviour (2). The second window shows the status, if the fall was detected or it is just a fall like behaviour and the third one displays the board frontal angle and the last one displays lateral angle. First graph shows a dynamic patient condition: the patient is walking or doing a periodic activity. In this case the algorithm perceives high acceleration values but acceleration samples are repetitive. When monitoring within 3 seconds window it easy to see that there are more than one peak for each 3 seconds interval. So even if the delta acceleration threshold could be overcomed, algorithm decides that this is a normal voluntary activity. Second graph shows a real fall. In this case the patient fell unbalancing his body backward. Algorithm detected the peak (acceleration higher than 0.6 g), perceived that is the only one in the previous three seconds (considering an high delta acceleration value, higher than 1.2 g). Then, the board angle is detected, and it differed more than 60 degrees from before the acceleration peak. Using this information, a
Fig. 3. Fall detection algorithm
fall is declared. Third graph displays a patient brisk movement that cause high acceleration and delta acceleration values which overcome the fixed thresholds. The movement was a small jump. Board inclination was measured for the next 5 seconds, and the algorithm shows risky, fall-like behaviour, since the threshold of 60 degrees was not crossed. III. Security of Data Due to sensitivity of the personal information transferred in the Body Area Networks, it is necessary for the data to be encrypted. But, for the system to be feasible, the encryption and decryption algorithms should work on a low power, not computationally powerful microprocessor. We implemented a private key XXTEA algorithm , the third version of TEA (Tiny Encryption Algorithm), as a security mechanism, using the template given in [15] which is not as computationally expensive, and can be run on the Tyndall Mote microcontroller (ATmega128). One version of the algorithm was also implemented in Biomobius, on the PC. Measured time for the algorithm to encrypt 36 bytes of accelerometer data is 2.82ms using an 8MHz clock.
Fig. 4. Fall detection algorithm as seen in GUI
IV. Conclusion We implemented and tested the low power fall detection system, along with the efficient encryption algorithm. The results show that it is feasible to accurately detect falls, if the proper study is conducted to distinguish non-fall from a fall behaviour. Measurements confirmed that the acceleration threshold for distinguishing non-voluntary impact that happens if a person falls, from voluntary is 0.6g, but for the accurate detection one has to consider also the body angle change, as well as the acceleration history from before and after the impact. Finally, encryption algorithm was implemented on a low power microprocessor, and it was proven that it is practically possible to have a fully working wireless system that securely transfers medical data and detects falls.
[4]
[5]
[6]
[7]
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Acknowledgment The authors would like to thank the Tyndall National Institute, Cork, Ireland, for providing the equipment for implementation and testing. The work was supported through the Erasmus Programme and the SFIEEDSP for Mobile Digital Health project, grant number: 07/SRC/I1169
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