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Available online at www.ijiere.com
International Journal of Innovative and Emerging
Research in Engineering
e-ISSN: 2394 - 3343 p-ISSN: 2394 - 5494
Activity Recognition from Accelerometer and proximity sensor
using GO Sense system on Smartphone
A.Arun
1, M.Mayuranathan
21 PG Scholar, 2Assistan Professor
1Department of Computer Science and Engineering,Valliammai Engineering College, Chennai, India. 2Department of Computer Science and Engineering, Valliammai Engineering College, Chennai, India.
ABSTRACT:
Mobile devices are becoming more and more sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors, direction sensors, and acceleration sensors. GO SENSE application is developed truly based on our innovative ideas. This mobile application is developed to encourage the use of smartphone in more smarter ways. It invokes proximity, accelerometer, GPS etc. To achieve a smarter way to obtain innovative and useful features. GO SENSE avails you features like. The goal of this project is to use smartphone more smarter. Nowadays, smartphone is just used for calling, music, games and net surfing. But, it can be used more efficiently by using all sensors available in smartphone. So, we developed an application for smartphone called GO SENSE. This enables you to access sensors for more crazy and useful features.
Keywords: GO SENSE, proximity, GPS, sensors.
I.INTRODUCTION
The GO SENSE application is we can make calls, send sms even though your mobile display is damaged or cracked and loss of touch capability. So, we taken project that make mobile users to re-use their damaged mobiles with some Features. Touch in mode, where options and app can be used by touching. (a) This mode offers you SOUND ANALYZER, LOCATION TRACKER, BODY FITNESS, and SHAKE POINTS. (b) Touch free mode, where app or mobile can be controlled without touching the screen just by using volume buttons. It can be used to make CALL or send a SMS to required contact. As well as we are trying to make users to reuse their old damaged mobiles too. It indirectly helps in reducing e-waste.
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II.RELATE WORK
Mobile devices are becoming progressively sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors, audio sensors, light sensors, temperature sensors, direction sensors, and modification sensors the availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. [1] Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper immediate the implementation of a real-time classification system for some basic human movements using a customary mobile phone equipped with an accelerometer. The aim of this study was to check the present capacity of received mobile phones to execute in real-time all the necessary pattern recognition algorithms to classify the corresponding human movements. [2]
Driving mode can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive.
Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially competitive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. [3] The propose a new method to recognize a user’s activities of daily living with accelerometers and RFID sensor. Two wireless accelerometers are used for classification of five human body states using designation tree, and detection of RFID-tagged objects with hand movements provides additional instrumental activity database. Besides, we apply our activity recognition module to the health monitoring system. [4] In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 Participants: a wrist sensor (accelerometer and skin conductance), cell phone usage (call, short message service, Location and screen on/off) and surveys (stress, mood, sleep, tiredness, and general health, and alcohol or refresher beverage intake and electronics usage). [5]
Sensor data processing and smoothing techniques are discussed first to reduce the special noise present in phone-collected accelerometer data. [6] Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, interpret capturing this contextual information on mobile devices consumes huge amount of energy. [7] The use of context in mobile devices is receiving increasing attention in mobile and omnipresent computing research. In this article we consider how to augment mobile devices with awareness of their environment and scene as context. Most work to date has been based on integration of generic sensor, in particular for positioning and for vision. [8] The consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of devoted sensors on vehicles or on the roadside, or the tracking of mobile phones by service providers. [9] This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. [10]
III.SYSTEM ARCHITRCTURE
34 Fig: .3 Architecture of Touch in Mode
IV.SYSTEM PROCESS
A. Sound Analyzer
The sound analyzer is an option where it records sounds through mobile, if sound is heard to mobile with certain amount of amplitude that we specify. Then it sends an automatic message to that number prescribed. Normally human hearable amplitude is 10,000 to 25,000. When an audio is recorded with amplitude in between 10k to 25k. It sends a template message to prescribed user.
Fig: 4 Sound Analyzer
The alert the owner about the lively changes happening in their shops by sending a quick alert sms. And reduce the waiting time of person who waits till a process completes. It will help you to count cooker whistles, when you” re not nearby it. Benefits: It can work at any situation since we are specifying the minimum amplitude, so the nature sounds are not consider and analyzed.
B. Location Tracker
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Fig: 5 Location Trackers
Working: The latitudes and longitudes are recorded for particular time interval set by the app during commutation of the person .These recorded points are marked as hotspots in map and send to the specified person's phone as emergency alert through SMS. To alert the location of the person in emergency to another specified person’s phone as a text message And track the places of the persons whenever journey is made and send it to loved ones or friends.
Fig: 6 Send regular updates of your location
Fig: 7 Send request to specified location
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C. Body Fitness
Body fitness option can help you to count the pushups with help of proximity sensor of your Smartphone, it counts when you close the gap between you and the mobile. It saves your daily record and can be viewed at any time.
Fig: 8 Body Fitness
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D. Shake Points
The shake point is a Fun and luck based game. It is included in GO SENSE because it is also a sensor based one as well as we’re not allowing anyone to get bored by this app only features important things. We are decided not to discourage use of GO SENSE users. So, to make them (users) always entertained, we included this accelerometer sensor based mini-game.
Fig: 10 Processes of Shake Points
The discuss about Figure: 10 when there is change in values of X, Y, Z denotes that there is a motion occurring in your Smartphone. This app will get your changes in axis and then checks for difference is yes it creates a random integer between 1-10 which is displayed as a score. This game is purely based on your luck and accelerometer sensor quality of your mobile.
E. Touch Free Mode
Smartphone, which are being most popular inventions of all time, has many advantages and features. We all live in the age of Smartphone and we know the benefits of these devices. But all these will apply only when an user can interact with it, interaction here means touch.
Screen is the most basic and most needed component in a Smartphone. So, when that screen becomes damaged, accidentally by a fall, when one tries to pick up the phone. When a screen cracks, most basic feature gets destroyed, this is a display. As a result, user cannot access the phone and has to either change the display or worse, have to throw the phone. But, in this situation is no longer a problem. Here comes our Touch free mode, by installing GO SENSE app, User can switch to a touch free mode, which enables them to use Smartphone to call without even touching the screen. By using volume buttons, which is present in every phone, user can simply use the volume-up button to quickly enter into touch free mode. In that, they can choose call option and select a contact from their contact list, and then can call that contact, all by simply pressing volume up button. This enables them to call anyone, anywhere without depending upon the phone’s screen to do the work. This feature is a must and handy for the users who lost their display in their Smartphone and Proves to be a very useful one. The main motto of our TOUCH FREE MODE is “RE-USE AN USED”.
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V.RESULT
Fig: 11 Output of Touch in mode
The discuss about Figure: 11 output of Touch in mode. Display in sound analyze and location tracker.
Fig: 12 Touch free mode in mobile application
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VI.CONCLUSION AND FUTUREWORK
Due to good planning & hard work, we have managed to develop an application successfully. As for now, we developed an application with basic functions of sensors. We are also planning for expansion of this app with wide functionalities. We think that this application would get positive and good reviews for our innovative ideas. If it so, we will plan for app with more functionalities. We think that we're capable of doing anything because of our hard work and dedication. In this GO SENSE application we are discussed about accuracy of sound analyzer to record sound perfectly and we are underway to develop more features in location tracker i.e. group of peoples or friends sharing their location for a meeting. Let’s hope we would do our best in futures!!
We hope that our project application is very useful to overcome the problems in creative and innovative way. Let’s our self-take an initiative to avoid e-waste!!
REFERENCES
[1] Jennifer R. Kwapisz, Gary M. Weiss, Samuel A. Moore” Activity Recognition using Cell Phone Accelerometers” SIGKDD, Volume 12, Issue 2, P.74-82.
[2] T. Brezmes, J.-L. Gorricho, and J. Cotrina” Activity Recognition from Accelerometer Data on a Mobile Phone” IWANN 2009, Part II, LNCS 5518, pp. 796–799, 2009.
[3] Derick A. Johnson and Mohan M. Trivedi” Driving Style Recognition Using a Smartphone as a Sensor Platform” IEEE Conference on Washington, PP.1606-1615, 2011.
[4] Yu-Jin Hong, Ig-Jae Kim *, Sang Chul Ahn, Hyoung-Gon Kim” Mobile health monitoring system Based on activity recognition using accelerometer” Elsevier, 2009.
[5] Akane Sano Rosalind W. Picard” Stress Recognition using Wearable Sensors and Mobile Phones” IEEE, p.671-676, 2013.
[6] Jun Yang” Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones” IMCE, 2009.
[7] Yi Wang, Jialiu Lin, Murali Annavaram, Quinn A. Jacobson, Jason Hong, Bhaskar Krishnamachari, Norman Sadeh” A Framework of Energy Efficient Mobile Sensing for Automatic User State Recognition”2009.
[8] Davrondzhon Gafurov, Kirsi Helkala, and Torkjel Søndrol” Biometric Gait Authentication Using Accelerometer Sensor” journal of computers, vol. 1, no. 7, p.51-59, 2006.
[9] Shuangquan Wang,Canfeng Chen, Jian Ma” Accelerometer based transportation mode recognition on mobile Phones”APWCS.2010-18, 2010.
