Fall Detection and Monitoring System For Elderly Person

Fall Detection and Monitoring System For Elderly Person Snehal S. Chougule 1 and Prof. A. B. Kanwade 2 1 Department of Electronics and Telecommunication Engineering SITS, Narhe, Pune-41 2 Savitribai Phule University of Pune, India Abstract Falls in the elderly have every so often been serious medical and social problems. Fall detection is a foremost task in the public healthcare area, particularly for the elderly and consistent system is a need to alleviate the sound effects of falls. An effective fall detection system is essential to bring critical support and to considerably reduce the medical care costs related with falls. The persons above 70 years of age were dead produced by fall incident. Hence we need consistent user based fall detection system. This fall detection system is proposed for elderly person monitoring which is based on smart sensors worn on the body and working through home networks. The key part of this system is ARM 7 Microcontroller. To detect the unintentional falls, a small low power tri-axial accelerometer is used and to detect heart rate cardio tachometer sensor is used. This specified 3-axis accelerometer sensor is used to measured acceleration of the body in all the three axes. By using information composed from an accelerometer and cardio tachometer the effects of falls can be noted. The system will also send the location of the person using GPS. Location information, which gives the information of longitude and latitude. The fall detection can be described as the rapid change from the front, right, left, back fall position information. The family members know how to, with permission, access this information and consider the safety of the elderly person. The system will contact the emergency center instantly through text message using GSM modem. Keywords component, Fall detection system; ARM controller, tri-axial accelerometer; cardio tachometer; Elderly monitoring. I. INTRODUCTION Falls avoidance is a task to senior population. The number of fall happenings increase in level as the number of senior adults increase in various countries all over the world. Incident of some fall injuries, such as cracks and spine injury, have evidently developed during the previous three years. Falls effects maximum of the times major fates and movement injuries with correlated impacts to their lives and the environs. If preventive measures are not taken in future, the numbers of injuries caused by falls is estimated to be 100% higher in the upcoming years. In health, sensor nodes can also be deployed to monitor patients and assist disabled patients. ZigBee is an Assistive Technology that can be used all over in the world [2]. New Assistive Technologies in Health Care based on largely established standards, like ZigBee, will play substantial role in reducing the severe growing of well-being expenses. Medical devices established on the ZigBee standard assistance the reduction of expenses related with the improvement and industrialised of novel medical devices. This Standards compact huge low-cost of balances profits for modules, even though suggesting an exceptional capability to make equipment easier for the control and monitoring of patients and purposes in clinics, aging care services and even homes[2]. ZigBee Health Care was proposed for use by assistive devices working in non-invasive health care [2]. Kinsella and Phillips [3] identify that the population of 65 and above elderly people in the developed nations will approach 20% of whole population in the future 20 years and will recognisably turn into a serious healthcare problem in the nearby future. In some nations, the complete number of people arriving adult ages will run into countrywide administrations, mostly health structures. This numeric surge in senior person is dramatically verified in the world s two populous nations: China and India. Chinas elder population persons above age 65 @IJMTER-2016, All rights Reserved 232

will projected great to 330 million by 2050 from 110 million these days. India s present elder population of 60 million is projected to go above 227 million in 2050, rise of around 280 percent from these days. By the inside of this century, here could be 100 million Chinese above the phase of 80[4].In WHO Global report, it highlights the growing proportion of elder people in parallel with a decreasing proportion of younger people. The triangular like structure population pyramid of 2005 will be moved with a more cylinder-like structure in 2025 [5]. Falls exponentially rise with growthrelated natural variation, hence a distinct number of people over the age of 80 years will cause extensive rise of falls and fall injury at an alarming rate [5]. Thus, as falls and fall-related injuries remains a prominent task in the public robustness field. The world-wide number of deaths affected by fall events was about 391,000 and precisely 40% of the fall happenings were as of people above 70 years of age in 2003 [6]. In NYC (New York City) hospitalization interpretations identify 56% of senior adults that be there admitted for a fall happenings and clear-fell at home in 2011 [7]. Thus we need consistent and instant recognition of falls is important so that appropriate medical facility can be delivered. Still, such monitoring designs are frequently too costly due to the high social resources needed. On behalf of this purpose, a strong study interest is in recent times focussed on the use of novel computer revelation tools for distinguishing falls of ageing people. Thus we making the research of information society towards enlightening elderly living a demanding need. The development of wireless sensor network and low-power sensor nodes, lots of novel methods have been suggested to resolve the problem to effectively detect fall incident. There are mainly three categories of fall detection methods for elderly people monitoring, which are, vision based methods, ambient based methods and wearable device based methods. Yu et.al. [8] Presented a vision based fall detection method for background subtraction is applying to extract the foreground human body and post processing to progress the result. To identify a fall event, data was send into a directed acyclic graph support vector machine and that can be used for posture recognition. Such type of system described a high fall detection rate and low false detection rate [8]. Popescu et al. [10] established an acoustic-based fall Detection system which used an array of acoustic sensors. The fall detection sensors are linear arrays of electret condensers placed on a pre-amplifier board. In order to capture the information of the sound height, the sensor array was placed in the z-axis. The limitation of this method was that that only one person was allowed in the vicinity. Bagalà et al. [12] presented an evaluation of accelerometer-based fall detection algorithms on real-world falls. Abbate et al [13] proposed a smartphone based fall detection system with consideration of the acceleration signal produced by fall-like activities of daily lives. Jin Wang et.al. [14] Provided a fall detection system for elderly person monitoring using 3-axis accelerometer, chardiotachometer and GPS in home networks. Various fall-detection resolutions have been previously recommended to create a reliable surveillance system for elderly people with high requirements on accuracy. Video based methods are usually more accurate than wearable based and ambient based methods. However, these systems often suffer from high risk of privacy and the prohibitive cost implementing the cameras. Thus, wearable sensor based methods are considered in this research. In this system, fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body. With treble thresholds, accidental falls can be detected in the home environment. By using information collected from an accelerometer, cardio tachometer sensors, GPS the effects of falls can be proposed. The proposed system can be deployed in a prototype system as deliberated here. This proposed system aims to construct an implementation for obtaining different parameters of body positions and acceleration and sending this information to levels depending on the state of emergency using GSM modem. The section II of the paper describes methodology to detect fall. Algorithm and flow chart is discussed in section III. The Experimentation and result analysis of the device is explained in section IV. The paper is concluded in the section V. II. METHODOLOGY The proposed system has been deployed in a prototype system as detailed fall detection system for elderly person monitoring through a user home environment that is based on smart @IJMTER-2016, All rights Reserved 233

sensors which are worn on the body. The key component of this system is ARM 7 Microcontroller. The design includes two sections where section one transmitter section consists of sensors interfaced with LPC2148 controller (ARM7) and another is wireless communication modules for transmitting the information to the monitoring section (PC). Figure 1: The Proposed Fall Detection System Figure 2: Monitoring Section The project will use ARM7TDMI-S based NXPs (national semiconductors and Philips) LPC 2148 microcontroller in LQFP (Liquid Quad Flat package) with 64 pins. The input contains of acceleration values, cardio tachometer values and GPS data. To detect the accidental falls, a small low power tri-axial accelerometer sensor is used. This specified 3-axis accelerometer (adxl335) sensor is used to measured acceleration of the body in all the three axes. Acceleration information of three axes is produced as three analog signals which differ with body posture. Acceleration value produced in each axis is read through distinct pins, selecting one analog input at a time. Analog signals produced by the sensor are digitized by analog to digital converter of the microcontroller. The transmitter pin of GPS is joined to the receiver of microcontroller. The system will also send the location of the person using GPS. Location information is read consecutively byte by byte which provides the data of longitude and latitude. The other sensor used in this system are utilized to detect the heart rate. The low-powered, ZigBee devices is used to transmit data over longer distances. ZigBee transceiver pair (Tarang F4) is used for communication between the controller and the computer. The status will be displayed on the LCD for the user convenient. On recognition of fall, the device sends a text message through GSM modem, and communicates it to PC through ZigBee transceivers. A message is sent to a mobile number when a fall is detected. The GSM modem used is SIM 300 V_7.05. ZigBee transceiver and GSM modem are connected to RS232 port of microcontroller board via a switch. ZigBee transceiver and the GSM modem are powered up by a single DC source of 12V, 2A. The sensor board and GPS device use 5V produced by built in voltage regulator of microcontroller board. By using information collected from an accelerometer, GPS and cardio tachometer the impacts of falls can be projected. The family members access this data and evaluate the safety of the elderly person. The system will contact the emergency center immediately through @IJMTER-2016, All rights Reserved 234

text message using GSM modem. The system is complemented with a customer interface designed to monitor information in real-time. The Keil Software-Software wants to develop our code in C language. To compile the C code KEIL software is required for making HEX file. Once that has been completed the Hex File can be downloaded to the target hardware and debugged. Alternatively Keil can be used to generate source files; automatically compile, link and covert using options set with an easy to use user interface and lastly simulate. Flash Magic is a tool which used to program hex code in EEPROM of microcontroller or flash magic is a software tool used for burning the.hex files to NXP Controllers and it is a free-ware tool. III. ALGORITHM AND FLOW CHART OF THE SYSTEM Above flowchart gives the stepwise working of receiver unit. Initially user provides inputs to the system i.e. power supply. Then controller starts to read the data from the different sensors and GPS data. I.e. accelerometer (adxl335) sensor, cardio tachometer sensor (Heart Rate sensor). Then controller check the sensed value form environment compare it with the threshold value. All sensor values are compare with their respective threshold values. By using information collected from an accelerometer, GPS and cardio tachometer the impacts of falls can be detected. The family members can, access this data and will contact the emergency center instantly to levels depending on the state of emergency through text message on different mobile number. Development of sensed data can be done on the device itself using embedded intelligence, while for further elaboration collected data are generally sent to a base station by means of wireless communication. Figure 3: Flow Chart of System @IJMTER-2016, All rights Reserved 235

IV. EXPERIMENTATION AND RESULTS ANALYSIS Figure 4. Experimental set up The experiment is performed on subjects. The sensor was attached to the centre of the chest. This is the optimum location to attach sensor for fall detection. In this experiment, subject performs different kinds of fall tests: forward fall, backward fall, right side fall, and left side fall. The fall event and heart rate is detected on PC using flash magic software. The fall event and heart rate is as shown in figure 5. Table 1. Shows the fall detection parameters. Figure 5. Output of fall event at flash magic window Table 1. : Fall Detection Parameters Sr. Acceleration (mv) Fall No. X- Axis Y- Axis Z- Axis Body Positions 1 492 517 104 Stable 2 528 425 132 Front 3 504 513 104 Back 4 607 532 125 Left 5 424 529 120 Right 6 572 496 107 Cross1 7 412 541 133 Cross2 V. CONCLUSION To conclude that the falls in the third age have been stated common reason of death, particularly for the elderly. Still, such monitoring designs are usually too expensive due to the high human resources required. Thus, as falls and fall-related injuries persist a key task in the public health field, consistent and instant detection of falls is important so that suitable medical support can @IJMTER-2016, All rights Reserved 236

be provided. In this study, using acceleration sensor and heart rate sensor we had implemented fall detection system for elderly person monitoring. In this experimentation, subject performs different kinds of fall trials: forward fall, backward fall, right side fall, and left side fall and so on. We had detected heart rate of person after fall event. In this paper, we had completed our main goal making a working prototype able to distinguish falls. Observing at the primary detection process, our fall detection system improves on previous systems and it is low cost system. REFERENCES [1] Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless sensor networks: a survey, Journal of Computer Networks, vol. 38, no. 4, pp. 393-422, March 2002. [2] J. Yick, B. Mukherjee, and D. Ghosal, Wireless sensor network survey, Journal of Computer Networks, vol. 52, no. 12, pp. 2292-2330, Aug. 2008. [3] K. Kinsella and D. R. Phillips, Global aging: the challenge of success, Population Bulletin, vol. 60, 2005 [4] Global Health And Aging, National Institute on Aging, National Institutes of Health, World Health Organization, 2010.. [5] WHO Global Report on Falls Prevention in Older Age, Ageing and Life Course, Family and Community Health, World Health Organization 2007. [6] WHO, The injury chart-book: a graphical overview of the global burden of injury, Geneva: WHO, pp. 43-50, 2012. [7] NYC Vital Signs, New York City Department of Health and Mental Hygiene January 2014 Volume 13, No. 1. [8] M. Yu, A. Rhuma, S. Naqvi, L. Wang, and J. Chambers, A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment, IEEE Trans. Infor. Tech. Biom., vol. 16, no. 6, pp. 1274-1286, Aug. 2012. [9] C. Rougier, J. Meunier, A.S. Arnaud, and J. Rousseau, Robust video surveillance for fall detection based on human shape deformation, IEEE Trans. Circ. Syst. for Video Tech., vol. 21, no. 5, pp. 611-622, May 2011. [10] M. Popescu, Y. Li, M. Skubic, M. Rantz, An Acoustic Fall Detector System that Uses Sound Height Information to Reduce the False Alarm Rate, in Proc. 30th Int. IEEE Eng. in Medicine and Bio. Soc. Conference, pp. 4628-4631, Aug. 2008. [11] H.R. Yan, H.W. Huo, Y.Z. Xu, and M. Gidlund, Wireless sensor network based E-health system: implementation and experimental results, IEEE Trans. Consumer Electron., vol. 56, no. 4, pp. 2288-2295, Nov. 2010. [12] F. Bagalà, C. Becker, A. Cappello, L. Chiari, and K. Aminian, Evaluation of accelerometer-based fall detection algorithm in realworld falls, PLoS ONE, vol. 7, no. 5, pp. 1-8, May 2012. [13] S. Abbate, M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini, and A.Vecchio, A smartphone-based fall detection system, Pervasive and Mobile Computing, vol. 8, no. 6, pp. 883-899, Dec. 2012. [14] Jin Wang A Novel Fall Activity Recognition Method for Wireless Sensor Networks, International Journal of u- and e- Service, Science and Technology Vol. 5, No. 4, December, 2012. [15] Jin Wang, Member, Zhongqi Zhang, Bin Li, Sungyoung Lee, and R. Simon Sherratt, An Enhanced Fall Detection System for Elderly Person Monitoring using Consumer Home Networks, IEEE Transactions on Consumer Electronics, Vol. 60, No. 1, February 2014. @IJMTER-2016, All rights Reserved 237