THE ACCURATE FALLING DETECTION SYSTEM FOR THE ELDERLY (AFDE)

Transcription

1 THE ACCURATE FALLING DETECTION SYSTEM FOR THE ELDERLY (AFDE) Jie Ni Dorette Ong 1, Huijie Zhang 1, Vwen Yen Alwyn Lee 2, Clifton Phua 2, Kelvin Sim 2, Yan Liu 3 and Xian Zhang 3 1 Raffles Girls School (Secondary), 20 Anderson Road, Singapore Institute of Infocomm Research, A*STAR, 1 Fusionopolis Way, Singapore School of Computing, National University of Singapore, 21 Lower Kent Ridge Road, Singapore ABSTRACT Statistics have shown that falls are the leading cause of accidental deaths among people over the age of 75 and 55% of all falls take place in homes with most falls occurring in the bathroom. In this research, the aim of this project is to come up with a system that is able to detect falls in the washrooms, so that immediate medical aid can be provided so as to alleviate the aftereffects of the fall. Microsoft Visio was used to map the logic of the system which was then used in the creation of the rules for the system. Drools Rules is used to create a set of rules that gets input from the sensors detecting the motions of the patients, so as to infer the activities that are ongoing in order to check for errors. With input from the sensors, the set of rules should be able to determine the activity happening and possible errors that the input could imply. Simulations of real-li (Learn Not To Fall, 2009) scenarios were carried out twice. Errors and limitations of the system were noted during the simulation to further debug and improve the rules for a more robust and efficient system. 1. INTRODUCTION Statistics have shown that falls are the leading cause of accidental deaths among people over the age of 75 and 55% of all falls take place in homes with most falls occurring in the bathroom. Over a third of the elderly are admitted to the hospital due to falls that take place in bathrooms. Of all the elderly that are hospitalized due to falls, 50 percent of them fail to survive a year. Many elderly increase their degree of injury due to fainting in the Correspondence to : Jie Ni Dorette Ong (miss_genie@hotmail.com) 83

2 bathroom and not being able to get medical help in time. During the fall, patients may suffer from shock or unconsciousness and it is important for immediate medical aid to reduce the aftereffect. The main purpose of this research is to come up with a system that is able to detect falls so as to immediately alert the nurses or caregivers. This is done by creating a list of Drools Rules. These rules are debugged, improved and enhanced via simulations of real-life scenarios. Drools Rules is used to create a set of rules that gets input from the sensors detecting the motions of the patients, so as to infer the activities that are ongoing in order to check for errors. In this case, there is the error to warn the caregivers when the patient has fallen down. This system aims to help the elderly who live independently improve their quality of life and relieve the caregivers. 2. MATERIALS AND METHODS The tools used are Java and Drools Rules. Java language is used when programming for class files. Drools Expert is a type of software that is used in creating Jboss rules. These two are simulated through Eclipse, an integrated development environment for coding. The rules are then debugged via simulation with the sensors. Figure 1. Flowchart depicting the logic of the system The sensors used in the simulation are the Passive Infrared Sensor (PIR) and the Ultrasound Sensor (US). A flowchart was mapped with Microsoft Visio to show the series of how the logic of the falling detection system. 84

3 2.1. Reasoning Engine APEC Youth Scientist Journal Vol. 4 / No.1 The reasoning engine consists of a list of Drools rules written through Drools Expert software as well as different class files, for example Contextual Reasoning, Person, Sensor, etc. Each rule consists of two components, the requirements for the rule to fire and the consequences after the rule fires. rule " Suspected Fall " duration when $s1 : Sensor ( id == "pirwashroom", pirstate == Sensor.pir_state.FIRING) $s2 : Sensor ( id == "uswashroom", usstate!= Sensor.us_state.NORMAL) $p : Person(occupancy == Person.person_occupancy.OCCUPIED) then $p.setactivity (Person.person_activity.FALLING); Figure 2. An example of a rule The first component of the rule consists of conditions that have to be fulfilled before the system recognises the second component. For example, in this rule, Suspected Falling, the Passive Infrared Sensor, pirwashroom must be in the firing state and the Ultrasound Sensor uswashroom must not be in normal state, but in caution or danger state for the rule to fire. This condition must hold for 10mins before the rule fires. When the rule fires, the person occupying the washroom will then have the activity as falling. There are two sensors used in this system, mainly the Ultrasound Sensor (US) and the Passive Infrared Sensor (PIR). The Passive Infrared Sensor detects activities of infrared sources and through detection of movement, determines whether there is any presence within the room or area. Its two states are Silent and Firing. The Ultrasound Sensor detects the height of the nearest object or person nearest to it. Its three states are Normal, Danger and Caution. The Ultrasound Sensor works by detecting its distance from the object closest to it. (Tallest object) When the distance increases, that means that the person in the room is close to the ground. This may be due to the person bending to pick something up, or bending to wash his/her face, or falling. The Ultrasound Sensor takes a reading at a rate of 600 times per minute, or 10 times per second. Caution mode is activated when the person lowers at a slow speed. (Increasing distance from sensor; the difference between readings are not that great.) This mode is maintained when the person is about 110cm to 60 cm off the ground. Danger mode is activated when the person lowers at a great speed. (Increasing distance form sensor; the difference between readings are great.) 85

4 This mode is maintained when the person is less than 60 cm off the ground Environmental Set-up There are two rounds of simulation, one that ensure that the forms of the rules are correct and another to test out the actual scenario. The simulations to test out the actual scenario were done in a handicapped toilet, with the layout as shown below. There were two parts to this simulation. Firstly, different scenarios were tested systematically, with four possible events, mainly, bending to use the tap, sitting on the toilet seat, falling and nurse intervention. Then, simulations based on free scenarios were tested to ensure that the system would work regardless of the actions in the washroom. Video recordings and logs were recorded of the simulation for later verification. The logs recorded down the readings of the sensors and were then compared to the video recordings and the GUI to check whether the rules were correctly implemented. This is done by cross-referencing the video recordings of the simulations, video recordings of the GUI and the log of the readings to check if the actions match the conclusions on the GUI. The log is used to check with the simultaneous actions of the person in the video. The section shown below describes the action of the person going into the washroom :43: pirwashroom 485 SILENT uswashroom 145 CAUTION :43: pirwashroom 485 SILENT uswashroom 145 CAUTION :43:01.28 pirwashroom 915 FIRING uswashroom 145 CAUTION :43: pirwashroom 915 FIRING uswashroom 135 NORMAL :43: pirwashroom 915 FIRING uswashroom 126 NORMAL :43: pirwashroom 915 FIRING uswashroom 119 NORMAL Figure 3. A section of the log showing the sensor states The Passive Infrared Sensor is located in the bottom right-hand corner of the washroom to ensure maximum coverage of the area and reduce blind spots. Should the setup of the bathroom be different, the PIR sensor will be placed in an empty corner facing either the sink or the toilet bowl. The Ultrasound Sensor is located on the ceiling in the middle of the washroom to provide maximum coverage. 86

5 Figure 4. Diagram of Washroom used in simulation 2.3. Person The person in this scenario has three possible activities, Check, Falling and Bending. Falling refers to the suspected falling rule where there is a sudden drop in height when the person has moved from a higher height to a lower height by possibly bending or really falling. In the system, the person is classified to have fallen but should the person get up after the fall, it is classified as bending. In this case, Bending refers to the two cases, one where the person does not fall but is bending to use the sink or sitting on the toilet seat and the other is the false alarm when the person has supposedly fallen down but managed to get up showing that it was not really a fall. Bending is classified broadly as the cases where there is a sudden increase in height when the person moves from a lower height to a higher height. Check is an activity to check if the person is suspected to have fallen or has left the room. When the person leaves the washroom, there is a sudden drop in height and this may be taken as a fall. To prevent the system from wrongly determining an exit from the washroom as a fall, there needs to be a fall check to see if there is anyone present in the room. 87

6 3. RESULTS Entering Washing Sitting on Toilet Fallen Down Exits Washroom Washroom Hands st Test Actions (Chronological order) Fallen Correct? Other remarks Alert?.. No N/A... Yes Patient bent too low... No N/A... Yes Patient was able.... Yes Help rendered to Patient.... Yes Patient was able..... Yes Patient was able..... Yes Patient was able 2 nd Test Actions (Chronological order) Fallen Correct? Other remarks Alert?.. No N/A... No N/A... No N/A... Yes Patient was able.... Yes Patient was able.... Yes Patient was able..... No System showed no one in room..... Yes Patient was able 88

7 3 rd Test Actions (Random order) Fallen Correct? Other remarks Alert?.. No N/A... No N/A... No N/A... Yes Patient was able.... Yes Patient was able.... Yes Patient was able..... Yes Patient was able..... Yes Patient was able Table 5. A table of the actions and alerts during simulation Analysis of Data A total of 30 trials were done, the first two in chronological order and the third one in a random order. Overall, the 30 trials had an accuracy of 28/30, which is an accuracy rate of 93.3% approximately. In terms of fall detection, 14 out of 15 experiments had the fall detected and the 15 experiments had an accuracy rate of 93.3%. 4. DISCUSSION During simulation, there were two main testing avenues, mainly the false alarms and the falling error. False alarms can arise from bending, sitting on the toilet seat and leaving the washroom. During troubleshooting, some errors were found and debugged. Duration played a key role in preventing false alarms. For example, to prevent the false alarm where the system claimed that the person had fallen when the person had actually exited the washroom, there was a time buffer created. This was done by checking if the Passive Infrared Sensor was still firing, if it was still firing for more than 10 minutes, then the person would have fallen down, if it was silent for more than 10 minutes, then it is classified as a false alarm. Without the buffer, the system would be too sensitive and inaccurate. 89

8 Similarly, during the 1 st test, there was an error for the action when the person entered the washroom and washed his hands before leaving the washroom. This was due to a duration issue as the person in the scenario has washed his/her hands for a longer duration and had triggered the alert since the ultrasound had detected a change in state from Normal to Danger for a period of time. To solve this problem, the buffer time allowed for the system to trigger the alert was increased to 10 minutes so that should the person want to wash his/her hands, he/she would have probably finish washing before 10 minutes and not trigger the alert. Another error in the 2 nd test, for the action where the person entered the washroom, sat on the toilet seat and fell down before exiting, was due to a sensor error where the Passive Infrared Sensor was unable to detect the presence of the person in the washroom. This resulted in an error where the system showed that no one was in the washroom. 5. LIMITATIONS 5.1. Ultrasound Sensor and the inaccuracy of distances Since the Ultrasound Sensor measures the linear distance between the Sensor and the person, the data would be skewed if the person was in a corner of the room as the distance would be greater. For example, though the height of the object from the ground is the same, the perceived distance of the person from the Ultrasound sensor is greater in the corner, thus skewing the results. This could result in the ultrasound sensor sending out wrong signals to the system, as the state of the sensor could be wrong. 90

9 5.2. The lack of a Bathing area In a real-life situation, there is a bathing corner in the washroom, and therefore the toilet which the experiment was carried out in is not completely applicable to the washroom of the nursing home Sensitivity of the Passive Infrared Sensor The PIR Sensor is unable to detect breathing movements, and thus is only in Firing mode if there is significant movement within the room. Thus, often when if the patient is in the room, the sensor may send out an incorrect signal and affect the accuracy of the rules. For example, if the patient happens to knock his/her head during the fall and becomes unconscious, the PIR Sensor will report as Silent mode and thus the fall alert will not appear. The accuracy of the sensor can be improved if the sensor is changed to the Infrared Heat Detection System. However, this sensor is expensive, with costs varying from ten thousand to twenty thousand $US. This is impractical in real-life washrooms in nursing homes, and thus the PIR Sensor is chosen instead. 6. FURTHER IMPROVEMENTS/ PLANS FOR EXPERIMENTATION Since the layout of the washroom in I2R is different from the layout in the PeaceHaven Home, this could affect the accuracy of the rules. For further experimentation, simulation could be carried out in the actual washroom. Further experimentation could also be done to observe the behavior and reactions to the system of the elderly or those living in the home. The sensitivity of the Passive Infrared Sensor can be improved in the real-life scenario by purchasing another type of the Passive Infrared Sensor. (Infrared Heat Detection System) This way, even slight movements like breathing can be detected and thus even falls which render the patients unconscious are detected. However, since the Infrared Heat Detection System is expensive, one way of adjusting is to reduce the bar for the Firing Mode. This in turn causes the system to have a longer recovery time. Therefore, with further experimentation, a balance between precision and time can be reached. 91

10 7. CONCLUSION During the mentorship, time was spent on creating flowcharts to check the logic in the system and creating the Drools Rules. We learnt how to set up and use the sensors through the mentor s guidance. Simulations were carried out and video recordings of the simulations were taken down for checking of results. Data was also taken from the log collated by the system in order for the checking and confirmation of results. The system may not be perfect but is workable and is an efficient system. Nurse Intervention In the previous experiment, the system functions with two characters (Patients) in the Washroom or Bedroom, namely Person A and Person B. These two people carry out activities such as walking in the bedroom, lying on the bed, sitting on the bed and opening the cupboard. To maintain order and protect the privacy of the patients in the nursing home, an auto-correction activity is set up so reminders will be made when patients make erroneous activities. This system has been improved in this project extension by the addition of a new character into the system, namely the nurse. The nurse is able to appear in the room and intervene with the patients activities should errors occur. 8. MATERIALS AND METHODS The tools used are Java and Drools Rules, and the rules are tested on the XMPP Interface through Spark. After the rules are created, they are systematically simulated with a scenario that covers all possible actions to ensure accuracy of rules. Any inefficiencies or errors within the system are corrected. The system is put in a "Hang" mode when the nurse enters the room, in which case no errors are brought up. During this time, the nurse may carry out some actions and remind the patients on their errors and possibly correct it. When the nurse leaves the room, the system goes out of the "Hang" mode and is able to show existing errors. An original design of the rules did not feature the Hang mode, but instead simply cancelled out all existing errors. However, this couldn t work as the errors appear repetitively and thus the system can only erase the errors one at a time. Thus a more practical solution was implemented to ensure that errors can only appear when the nurse is not in the room. 92

11 Description The purpose of the Hang status is to enable the system to automatically detect and deactivate the errors in the system temporarily during the span of time when the nurse is in the room. Therefore, in the case that the nurse is unable to remove all the errors in the system, the error alerts will still be activated following the nurse s absence. This will ensure that the system, being able by itself, retains its smooth sequence of events and any possible crashes in the system is avoided and human intervention is not required. 9. REFERENCES 1. Magnolia, SBS, JBoss EAP, and RHEL (2011),Drools Expert Jboss Community. Last retrieved 18 July Bath Fixers, Inc. (2008, June 12). Bath Fixer. Retrieved Jan 4, 2012, from Bath Fixer Bathroom Solution to Improve your Quality of Life: 3. Learn Not To Fall. (2009, March 2). Learn Not To Fall. Retrieved Dec 28, 2012, from Falls and Injury Statisics for Seniors and Elderly: Jie Ni Dorette Ong Huijie Zhang 93