INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

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1 INTERNATIONAL JOURNAL OF ELECTRONICS AND International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN (Print), ISSN (Online) Volume 3, Issue 2, July-September (2012), COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) IAEME ISSN (Print) ISSN (Online) Volume 3, Issue 2, July- September (2012), pp IAEME: Journal Impact Factor (2011): (Calculated by GISI) IJECET I A E M E MONITORING AND FALL DETECTION OF PATIENTS USING MOBILE 3-AXIS ACCELEROMETERS SENSORS ABSTRACT Vani. Surapaneni #1, V. Shanthi Sri #2 ECE Department, JNTU Kakinada VRS & YRN College of Engineering & Technology, Cherala. Andhra Pradesh (State), India. 1 [email protected] 2 [email protected] This paper describes system for monitoring and fall detection of patients using triaxial accelerometer together with ZigBee transceiver to detect fall of patients. The system is composed of data acquisition, fall detection and database for analysis. Triaxial accelerometer is used for human position traking and fall detection. The system is capable of monitoring patients in real time and on the basis of results another important parameters of patient can be deducted: the quality of therapy, the time spent on different activities, the joint movement, etc. The system, including calibration of accelerometers and measurement is explained in detail. The Accidental Fall Detection System will be able to assist carers as well as the elderly, as the carers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the carers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver. Keywords-component; accelerometers, fall detection, ZigBee standard I. INTRODUCTION The leading health problems in the elderly community. They can occur in home as well as in hospitals or in the long-term care institutions [1]. Falls increase risk for serious injuries, chronic pain, long-term disability, and loss of independence, psychological and social limitations due to institutionalization. Nearly 50% of older adults hospitalized for fallrelated injuries are discharged to nursing homes or long-term care facilities [2]. A fall can cause psychological damage even if the person did not suffer a physical injury. Those 41

2 who fall often experience decrease activities of daily living and self-care due to fear of falling again. This behavior decreases their mobility, balance and fitness and leads to reduced social interactions and increased depression. The mortality rate for falls increases progressively with age. Falls caused 57% of deaths due to injuries among females and 36% f deaths among males, age 65 and older [3]. Majority of falls result from an interaction between multiple long-term and short-term factors in person s environment [4]. Common risk factors include problems with balance and stability, arthritis, muscle weakness, multiple medications therapy, depressive symptoms, cardiac disorders, stroke, impairment in cognition and vision [5,6].Detection of a fall possibly leading to injury in timely manner is crucial for providing adequate medical response and care. Present fall detection systems can be categorized [7,8,9] under one of the following groups: user activated alarm systems (wireless tags), floor vibration-based fall detection, wearable sensors (contact sensors and switches, sensors for heart rate and temperature [10], accelerometers, gyroscopes), acoustic fall detection, visual fall detection. The most common method for fall detection is using a triaxial accelerometers or bi-axial gyroscopes. Accelerometer is a device for measuring acceleration, but is also used to detect free fall and shock, movement, speed and vibration. Using the threshold algorithms while measuring changes in acceleration in each direction, it is possible do detect falls with very high accuracy [11]. Using two or more tri-axial accelerometers and combining them with gyroscopes at different body locations it is possible to recognize several kinds of postures (sitting, standing, etc.) and movements, thereby detecting falls with much better accuracy [12]. An easy and simple method to detect fall detection of patients is using accelerometer together with ZigBee transceiver to communicate with Monitoring System through wireless network, and in this paper a system for monitoring and fall detection of patients using mobile MEMS accelerometers will be presented. II. SYSTEM FOR MONITORING AND FALL DETECTION The whole system consists of a set of sensors (two or more sensors on the patient, usually MEMS sensors) which the patient wears on himself, local units to collect data that are placed in patient vicinity and systems for collecting. The tiny sensors in the strap are capable of measuring user orientation and motion in three-dimensions and it is constantly monitoring and analyzing the signals in real-time looking for movement indicating a fall. 42

3 Figure 1: myhalo System Flow II.I COMPARISON OF EXISTING SYSTEMS Systems Installation Monthly Subscription Total Cost per Annum (USD) Approximate in SGD (USD 1 = SGD 1.4) Alert1 Self, plug-n-play and pendant USD Self, myhalo USD Chest strap Table Error! No text of specified style in document..1 Comparison of Elderly Fall Detection System From the comparison Table Error! No text of specified style in document..1, it shows that the system maybe a hindrance to the consumer in terms of price over the years. The aim of this project is to be able to provide equal standard of care at an affordable cost. The system is shown in Figure 1 The space is divided into sections which are defined by interior and exterior of the institution in which a system is operated. Each room is stocked with local receivers. Local receivers collect data from sensors that the patients are wearing on the clothes. The sensors are small and lightweight. One sensor is located in the upper garment and the other at the bottom. This is not limited to two sensors, if necessary, there may be more, but for the detection of falls to the back the system must have at least 2 sensors [13]. Local receivers pass information to the server. The server information is processed local health care service. Personal computers are used to browse the database collected at the server. The database contains information about the mobility of patients, treatment efficacy, joints. All these data can be analyzed offline and used to adjust patient therapy. This has served a double function of the system: 43

4 real-time patient monitoring and early detection of the fall in order to deliver medical assistance as soon as possible. Figure 2. System for monitoring and fall detection In this application FreescaleTM ZSTAR wireless sensing triple axis board is used (Fig. 2). It is very practical because of low power consumption, portability, and the ability to be mounted in small pockets inside the clothes of patients. Board is divided into sensory and receiver part. The sensor is placed at the patient and is equipped with an accelerometer, microprocessor, and transceiver with the antenna which sends the measurement data to the receiver. The receiver also has a microprocessor that adjusts the signals received through the antenna to send with the USB protocol. These data are sent to the server. The server collects, process and stores the data. Each sensor that is connected to the patient is personalized, and its data are stored in a file under person's name to get an overview of all activities and physical stress of the patient Figure 3. Wireless ZSTAR accelerometer sensing board III. FALL DETECTION USING TWO ACCELEROMETERS In this chapter the operation of the system through one of its functions and to the detection of fall will be described. The figures have been simplified for better 44

5 understanding of the system. The algorithm used is improved algorithm given in [13], with better detection of backwards falls. Setup for accelerometer fall detection (Fig. 3.), consists of the measuring sensors with transmitter, receiver and server for data processing and fall detection. Figure 4 Accelerometer fall detection system The fall is detected by the algorithm described in Figure 4. It can be seen that fall detection algorithm uses data from both sensors that are monitored at the same time. This algorithm is able to distinguish between falls( forward,back word fall into a sitting position) and the normal daily activity, such as walking, mastering stairs, sitting in a chair, lying walking is also detecting by the sensors. Figure 5 Algorithm for fall detection with two accelerometers 45

6 However, these impacts are not isolated, and after them there is no significant change in orientation between the two sensors. Vectors are in the area that will call common zone.if an isolated stoke which causes a change in orientation of the body is detected, or the orientation of certain body parts in relation to the situation before the stroke, then with some certainty it can be said that the fall hade occurred. IV. HARDWARE DESCRIPTION This reference design is intended to be a hardware and software platform that enables evaluation of our ZigBee transceiver MC13192, 3-axis accelerometer MMA7260Q, and the MC56F8013 Digital Signal Controller. This section describes in more detail the electrical design of the module, its features and the advantages of using a hardware architecture like the one proposed in this reference design. Figure 6 presents the block diagram for the hardware module of the reference design. As can be seen, the design is centered around the processing unit (the MC56F8013 DSC). Some peripherals were added to enable user interaction, such as the buzzer, the push buttons, and the LEDs. A JTAG interface was added for programming and debugging. For additional debugging and to allow for serial communication, a serial interface was included. The board is powered either from a 9 V battery or from an external 9 V power supply. The voltage regulator provides 3.3 V. The RF Transceiver is controlled by the DSC, and accomplishes transmission and reception of data packets using the PCB dipole antennas. The antennas connect to the transceiver using matching networks. The design is simple, yet it has the necessary elements to evaluate many applications, thus reducing development time and costs. V. CONCLUSION Figure 6 Human fall detection building block Triaxial accelerometers can be used for detecting fall of patients. They offer low cost solution, and together with wireless connectivity solutions such as ZigBee provide efficient solution for both patients and medical personne l. This paper describes the system for monitoring and fall detection 46

7 of patient using triaxial accelerometers sensors. The system parts are shown and described. The use of two accelerometers on patient s body as explained in this paper can be used to detect falls. And all data that is collected about a patient are stored in. REFERENCES [1] Benceković Ž.; Analiza indikatora kvalitete zdravstvene njege na Internoj klinici (2. dio) ; Hrvatski časopis za javno zdravstvo; Vol 4,Broj 14, 7. travanj [2] Shumway-Cook A.; Ciol M.A.; Hoffman J.; Dudgeon B.J.; Yorkston K.;Chan L.; Falls in the Medicare Population: Incidence, Associated Factors, and Impact on Health Care ; Physical Therapy, Volume 89, Number 4, April 2009.; pp [3] British Columbia Ministry of Health Planning Office of the Provincial Health Officer, Prevention of Falls and Injuries Among the Elderly: A Special Report From the Office of the Provincial Health Officer, January 2004, [4] Tinetti M.E.; Preventing Falls in Elderly Persons ; The New England Journal of Medicine, Volume 348:42-49 [5] George F. Fuller; Falls in the Elderly, American Family, Physician; April 2000; 61; pp , [6] Erica Weir; Luana Culmer; Fall prevention in the elderly population, Public Health, Canadian Medical Association, September 28, 2004; 171(7): 724, [7] Luštrek M.; Kaluža B.; Fall Detection and Activity Recognition with Machine Learning ; Informatica 33 (2009) [8] U. Lindemann; A. Hock; M. Stuber; W. Keck; C. Becker; Evaluation of a fall detector based on accelerometers: a pilot study ; Medical & Biological Engineering & Computing, 2005, Vol. 43, pp [9] Zhang T; Wang J.; Liu P.; Hou J.; Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm ; IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.10, October 2006, pp [10] Yazaki S.; Matsunaga T.; A Proposal of Abnormal Condition Detection System for Elderly People Using Wireless Wearable Biosensor ; SICE Annual Conference 2008 August 20-22, The University Electro- Communications, Japan; pp [11] Barth, A.T.; Qiang Li; Gang Zhou; Hanson, M.A.; Lach, J.; Stankovic, J.A.; Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information ; Wearable and ImplantableBody Sensor Networks, BSN Sixth International Workshop on; 3-5 June 2009; pp: [12] Pavel Lajšner and Radomír Kozub, Wireless Sensing Triple Axis Reference Design, Designer Reference Manual, ZSTARRM, Rev. 3,01/2007 [13] Mostarac, Petar; Hegeduš, Hrvoje; Malari, Roman; Jur evi, Marko; Lay-Ekuakille, Aime. Fall Detection of Patients Using 3-Axis Accelerometer System // Wearable and Autonomous Biomedical Devices and Systems for Smart Environment / Lay-Ekuakille, Aimé ; Mukhopadhyay, Subhas Chandra (ur.). Berlin : Springer-Verlag, 2010.pp: the database on the server and can be used to improve health care for the patient. Vani. Surapanannnei has completed my B.Tech (EIE) from ST. Anna s College of Engineering & Technology, Cherala. Presently pursuing M.Tech (VLSI & ES) from VRS & YRN College of Engineering, Cherala, Andhra Pradesh. 47