Proposal FALL DETECTION. ECE4007 Senior Design Project. Section L05, Fall Detection Team. Nicholas Chan, Group Leader.

Transcription

1 Proposal FALL DETECTION ECE4007 Senior Design Project Section L05, Fall Detection Team Nicholas Chan, Group Leader Akshay Patel Abhishek Chandrasekhar Hahnming Lee Submitted February 4, 2009 Fall Detection (ECE4007L05) 1

2 Table of Contents EXECUTIVE SUMMARY... 3 INTRODUCTION... 4 Objective... 4 Motivation... 4 Background... 4 PROJECT DESCRIPTION AND GOALS..5 TECHNICAL SPECIFICATION... 6 DESIGN APPROACH AND DETAILS... 7 Design Approach... 7 Codes and Standards... 9 Constraints, Alternatives, and Tradeoffs... 9 SCHEDULE, TASKS, AND MILESTONES PROJECT DEMONSTRATION MARKETING AND COST ANALYSIS Marketing Analysis Cost Analysis SUMMARY REFERENCES APPENDIX A GANTT CHART FOR FALL DETECTION APPENDIX B CODE ALGORITHIM AND CODE RESPONSIBILITIES Fall Detection (ECE4007L05) 2

3 EXECUTIVE SUMMARY Hospitals and nursing homes are experiencing a simultaneous increase in injuries due to falls and a decrease in qualified staff hires. In countries like England, falls account for 32.3% of reported patient safety incidents in hospitals [1]. Solving the problem involves implementing preventative measures that will minimize incidents leading to injury without necessitating a larger staff. It is difficult to stop falls from ever occurring, but decreasing the number of injuries would lessen the dilemma. The proposed fall detection system deals with both of these problems and can be implemented on a large-scale level. During hours when there is a smaller on-call staff, such as at night, the system activates itself and begins to detect motion in a room. It uses a strategically positioned camera to record any movement. A feed of the images is continuously transmitted to a computer, where the data is analyzed and processed to determine if a fall has occurred and whether it necessitates immediate medical assistance. The computer can differentiate between sudden movements and motion that is actually a fall. If help is necessary, an alarm or alert is sent to a station in order to direct staff to action. Because of the unpredictable nature of falls, a database is compiled on the computer and network. The database takes incidents of falls and categorizes it for future reference. If another possible fall occurs, the fall can be analyzed in realtime and compared to previous incidents. The final project will cost less than $100 dollars per room to implement. The system will not only aid in the prevention of falls and the subsequent injuries, but will reduce hospital s administrative and personnel costs. The cost advantages the proposed project has will make it appealing to any hospital needing to cut spending. Fall Detection (ECE4007L05) 3

4 FALL DETECTION The proposal details the procedure for a fall detection to help prevent unnecessary injuries in hospitals. Any alert would immediately be sent to a local attendee, whom can then respond. INTRODUCTION Objective The team will build a system that will be able to determine falls in older patients during vulnerable periods of times, like when they are alone or during the night. Falls are one of the most common injuries in hospitals, being 40% more likely to occur in a hospital than in other industries and locations [2]. Older people are approximately 50% more likely to fall than the general population [3]. Lawsuits have led to administrators and doctors being held more liable and therefore prone to overspend in areas like personnel [4]. The product will appeal to bigger hospitals and homes that have a very high patient-to-doctor ratio. Motivation The project attempts to solve a common problem that has had multiple attempts at solutions, but none have been optimal. Some, like a physical alarm, would be intrusive on the surrounding patients and may even increase the likelihood of falling [5]. Incorporating elements from various products in the market today yields the best system for hospitals and nursing homes. It also aims to be one of the lower-cost solutions on the market. Background In the past, many products would require direct input from the patient [6]. One required a patient to carry a belt-size alarm with a button on it that only sounds when pushed. While Fall Detection (ECE4007L05) 4

5 effective in preventing some injuries, it failed to account for those who fall unconscious or have no control of their arms or legs. Another used two magnetic strips, triggering an alarm when the two become disconnected. The method relies heavily on generalization of what constitutes a fall and is likely to produce more false-positives than the proposed project. Alternatives are constantly being sought out, as there has been no empirical evidence of a decrease in injuries resulting from falls. PROJECT DESCRIPTION AND GOALS Correctly detect a fall, send an alarm to someone nearby, and update a database In the project, an environment will be designed and optimized to help a specific patient and his or her needs. A camera will be strategically placed to capture as much of the area of the room as possible. The feed from the cameras is transferred through a USB connection to be analyzed by a computer. A program will be written that will help differentiate between situations requiring assistance and minor falls requiring none. The computer will have a database inside its hard drive that categorizes past incidents, and will perform its own independent analysis, match it to known falls within the database, and then decide the necessary course of action. The advantage lies in long-term use, as the incidence of false-positives will decrease with more examples. Cost less than 100 dollars per room to implement Cameras bought at retail cost $69.99, but can either be downgraded or bought in bulk to reduce cost. The specific model used in the project is a Microsoft VX A computer with MATLAB installed can be bought or built for under $500 and would be able to support a network of rooms. If the infrastructure is already in place (a network for internet or something similar), the cost to wire rooms would be minimal and would not require significant manpower or time. The cost would be a one-time flat rate, excluding any maintenance, which may or may Fall Detection (ECE4007L05) 5

6 not be necessary. If the alarm is decided to be a page sent to a nurse s pager, a cellular modem has to be installed. The method could also be performed over the internet or with a cellular modem, costing approximately $50 depending on type. Appeal to larger hospitals and nursing homes The product will be marketed towards middle-tiered hospitals and nursing homes that are under-staffed and overpopulated. It will help them reduce costs while simultaneously preventing an injury occurring from a fall. It is not marketed towards younger people and customers staying at home without assistance. The algorithm is optimized towards the elderly, as their actions are more predictable and they are more likely to be unable to seek help if injured [7]. TECHNICAL SPECIFICATION Table 1. Microsoft VX-6000 Web Camera Specifications Video Resolution Frame Rate Audio Support Computer Interface 320x240 (scaled from 1280x1024) 30 fps Yes USB Table 1 displays the relevant facts in this experiment. The camera will be set in a fixed position, meaning viewing angle is irrelevant. The frame rate will be able to capture any fall quickly enough. The size could be much higher, but testing is necessary to see if the image size is sufficiently large to analyze the nuances of each situation. The computer is connected through a standard USB connection. Table 2. Alienware Area-51 m15x Laptop with Windows Vista and MATLAB 2008b Processor 2.00GHz Core 2 Duo Graphics Card 256 MB NVIDIA GeForce 8600M GT USB ports 3 Memory 2048 MB of DDR2 Ram Line Voltage 100V to 240V AC Frequency 50 to 60 Hz Fall Detection (ECE4007L05) 6

7 Operating Temperature F Table 2 displays the test server being used. The computer will only be connected to the camera and a power outlet. Since the webcam will be running and streaming Windows proprietary media format WMV, a machine running Windows Vista Home with 2048 MB of RAM will handle the feed. If a different web camera were to be used in a different system, any operating system capable of running a version of MATLAB could be used for the system. DESIGN APPROACH AND DETAILS Design Approach While the ultimate goal is to prevent falls from ever occurring, the problem can at least be mitigated with the prevention of many injuries from falls. The project creates a set-up with one camera and one computer that will be able to provide immediate assistance through an alert being sent to a nurse. A USB camera, specifically the VX-6000, will be connected to a computer using a standard USB 2.0 cable. The optimal position of the camera has yet to be tested. It will be the location that can cover the largest area where any potential fall could occur. The camera, as stated in the technical specifications, captures video at 30 frames per second (FPS). The feed is sent to the computer and analyzed in MATLAB with the aid of the image acquisition toolbox. An established connection has been tested and MATLAB is a capable program of rendering the real time feed and analyzing it. Though it can run at a relatively high speed, it is unlikely the camera will need to run at the specified frame rate in order to evaluate a fall. Furthermore, lower FPS would decrease the workload of the processor as less data would need to be processed, increasing the efficiency of the computer and its ability to deal with multiple rooms. Fall Detection (ECE4007L05) 7

8 Once processed into MATLAB, an independent program will analyze the image to detect whether or not it is a fall. The theoretical outline of the program is diagramed in Appendix A. The program will first take in the image and grayscale it. Color is irrelevant in the final analysis and would hinder the data analysis speed. This acquisition would also determine the person in the picture. In the theoretical assumption, there would be only one person, as two people would make the system unnecessary; the system would only be activated when a patient is not directly supervised. Once the shape of the person is mapped out, an ellipse is drawn around the body. The ellipse has multiple characteristics that are helpful towards determining a fall. The directions of the major and minor axis determine the ellipse s orientation, while the ratio between the two axes determines the eccentricity. Sudden changes in either are strong indicators of a fall. These will be monitored throughout the recorded time. These characteristics are analyzed in conjunction with the speed of the motion, referred to here as the motion coefficient. Using black and white pixels, white to represent the person s general shape and black to represent the background, images are analyzed frame-by-frame, with the previous frame being the basis for the value of the motion coefficient. This will be monitored throughout the recorded time. All three of these factors have empirical data performed by another study, meaning numbers have yet to be calculated of what would constitute a likely fall. Other situations such as if two are sufficiently large but one is significantly less have yet to be researched and addressed. Once a fall is detected, an alarm is set off. The type of alarm has yet to be decided, but noise alarms will not be used. Their disruptive nature during the middle of the night would alert a large number of people, meaning more are prone to make sudden movements, increasing Fall Detection (ECE4007L05) 8

9 potential falls and injuries. With nurses carrying beepers throughout the hospital, a page could be immediately sent or an attendee could receive a message at a local computer. Testing has yet to be done on any advantage to a specific method. The method of alarm would also dictate the network setup. If the network were to page a nurse, it may require a cellular modem or a connection towards some phone system. If it were only to require being connected to the internet or a local network, the system could be integrated into a hospital s pre-built network, minimizing costs. Codes and Standards The codes and standards requiring adherence in the project are below. IEEE g The computer could be connected to a wireless network built into the hospital. Universal Serial Bus (USB) The computer and the camera will communicate through a USB connection. National Health Services (NHS) The network would have to be deemed safe to be built into a hospital. Constraints, Alternatives, and Tradeoffs The two constraints in this project are directly linked to the amount of money willing to be spent, which is often at the discretion of the customer. The algorithm and program do not gain efficiency when a higher resolution or faster camera is used, meaning the cost of cameras can be kept on the lower end. Incorporating different price levels of cameras may allow the group to explore more accurate possibilities, but the improvement is negligible, especially when coupled with the financial considerations. The main constraint is the power of the processing computer. A workstation with dedicated graphics cards and a processor may be able to analyze and send a Fall Detection (ECE4007L05) 9

10 signal quicker than one without high-end components. Analysis of necessary response time has not been performed, therefore it is difficult to make an assessment of whether or not this is a tradeoff or if it is an unnecessary cost. The cost would be most applicable if a customer were to use one computer to analyze many rooms, possibly slowing down response time and decreasing the overall quality of the product. If deemed necessary (e.g., if the current failure rate is too high), a second camera will be installed on the bed-side to further analyze the shape and the motion. It will function as a double check and will provide confirmation for detected falls. SCHEDULE, TASKS, AND MILESTONES A Gantt chart is included in Appendix B. Since it is particularly large, a complete chart is accessible on the group s web site. Because the hardware of the proposed project is both simple to set-up and acquire, most of the work is done on the software level. The modular nature of the project allows us to split the software development into parts which can be concurrently developed by different team members. This not only makes the project easier to write and compile, but also helps hold others accountable, as a missing function would prevent the whole project from functioning. The difficulty is mainly placed in the writing and optimization of the program. The specific tasks are assigned to each group member (as dictated in Appendix A). PROJECT DEMONSTRATION In early April in Van Leer, during the recitation session normally held by Dr. Art Koblasz, the project will be shown to a classroom of other students building various other projects. The project can be demonstrated in real-time accompanied with some statistical Fall Detection (ECE4007L05) 10

11 analysis. A typical test environment would be set up, and people of various builds and weights would then perform motions. All four group members will participate, as the motions will differ from person to person. Not only would falls be acted out, but non-falls that could be interpreted as falls would demonstrate the program s sophistication. Common non-falls include falling into chairs and running from one point to another point in the room. If the program can reasonably detect falls at a an acceptably high rate, i.e., near 100%, and send a signal in a very short period of time the project would be deemed a success. Since the project will involve large amounts of optimization in order to increase the efficiency of the code and the actions, there will be various builds throughout the cycle. Any additional functions or parts can be included in the final build. MARKETING AND COST ANALYSIS Marketing Analysis Current market analysis is aimed towards home consumers and others. Items requiring 24 hour call centers, like the Brick House Alert, are unsuitable for hospitals [8]. It poses an unnecessary monthly cost on them and would eliminate any benefit as help is already present at the hospital. A physical and loud alarm, like the Patient Alarm & Fall Down Safety Alert offered at the Survival Store online, is unsuitable for hospitals because it will disturb other patients and may cause an increase in falls as more incidents occur [6]. Other products have also used a sensor attached to a part of the body to analyze if a sudden motion has occurred, but it is undesirable as it creates too many false alarms and would require different analysis for different body types. These, while fulfilling certain segments, fail to prevent injuries. The system being built will have lower false-positives, if not in the present, in the future when even more data is Fall Detection (ECE4007L05) 11

12 added towards the database. It will be able to notify help without causing unnecessary panic. It will also be significantly cheaper, because the product will have a one-time cost as opposed to a monthly cost (like in a 24-hour call center). Cost Analysis The cost of the product is relatively low, presenting yet another advantage to most customers. One Microsoft VX-6000 was rented, retailing for $69.99 MSRP. The price can go lower as buying in bulk will be cheaper for both the seller and the buyer. The other large cost would be a server large enough to support an operation that would analyze most rooms, but this is dependent on the hospital. For some smaller nursing home, a regular pc, one likely around in place, is sufficient; for larger hospitals, a specially built workstation can perform the analysis. The cost is also decided upon by the hospitals, as speed and performance is theoretically directly related to the amount paid for the computers. A MATLAB license will also need to be bought for this specific program, but can be bought as a volume to make it cheaper. Regular costs can range from $ for the student version to thousands for the complete version. Miscellaneous wiring and mounting tools will likely be available in any of the customers locations, deducting some amount from the final cost. A system to support 10 rooms with two cameras each connected to a mainframe running MATLAB could cost as little as $ SUMMARY The theoretical program has been planned out and tasks for specific components have been assigned to various group members. The modular nature of the program allows relative independence when developing functions. The camera specification sheets have been analyzed and a program has been written that will establish a connection between the camera and Fall Detection (ECE4007L05) 12

13 MATLAB on the computer. Videos of falls have been taken and will be categorized and grouped in a database to help establish the basis of the adaptation the computer will use. REFERENCES [1] F Healey, S Scobie, D Oliver, A Pryce, R Thomson, and B Glampson, Falls in English and Welsh hospitals: a national observational study based on retrospective analysis of 12 months of patient safety incident reports, Quality and Safety in Health Care, vol. 17, pp , [2] Simple strategies can reduce falls and liability: women and elderly fall more frequently, Rehab Continuum Report, Nov [3] Oldies, depressed people more likely to take a tumble, Thaindian News, 18 June [4] L. Kowalczyk, Spending on Health Care rises 7 Percent in Hospitals, Drug costs contribute to faster acceleration 12, The Boston Globe, 8 Jan [5] D.L. Gray-Miceli, A Nursing Guide to the Prevention and Management of Falls in Geriatric Patients in Long-term Care Settings, Medscape Today, 19 May [6] Patient Alarm & Fall Down Safety Alert, [Survival Store], [cited 2009 Jan 21], Available HTTP: Fall Detection (ECE4007L05) 13

14 [7] S. Lord, C. Sharrington, and H. Menz, Epidemiology of Falls and fall-related injuries, in FALLS in older people: Risk Factors and strategies for prevention, 1 st ed. Cambridge, England: Cambridge Univ. Press, 2001, ch. 1, pp [8] Fall Detection, [Brick House Alert], [cited 2009 Jan 21], Available Fall Detection (ECE4007L05) 14

15 APPENDIX A GANTT CHART FOR FALL DETECTION Fall Detection (ECE4007L05) 15

16 Fall Detection (ECE4007L05) 16

17 APPENDIX B CODE ALGORITHIM AND CODE RESPONSIBILITIES Fall Detection (ECE4007L05) 17

18 Fall Detection (ECE4007L05) 18