IMPROVE ENERGY-EFFICIENT REAL-TIME HUMAN MOBILITY STATE CLASSIFICATION USING SMARTPHONE

Journal for Research| Volume 01| Issue 08 | October 2015 ISSN: Applied

Improve Energy-Efficient Real-Time Human Mobility State Classification using Smartphone Chaitali Kakad Department of Computer Engineering K.V.N. Naik Institute of Engineering Education and Research, Nasik

Pooja Andhale Department of Computer Engineering K.V.N. Naik Institute of Engineering Education and Research, Nasik

Madhuri Lokhande Department of Computer Engineering K.V.N. Naik Institute of Engineering Education and Research, Nasik

Priyanka Kathe Department of Computer Engineering K.V.N. Naik Institute of Engineering Education and Research, Nasik

Prof. N. V. Kapade Department of Computer Engineering K.V.N. Naik Institute of Engineering Education and Research, Nasik

Abstract Accelerometer is a one kind of sensor key using the Smartphone for human mobility state, with or without location determined based upon GPS, Wi-Fi or GSM i.e. energy efficient, provides the real-time human information and has high availability. Using accelerometer measurements for human mobility analysis presents its own challenges as we all carry our Smartphone’s differently and the measurements are dependent on body placement. Also it often relies on an on demand remote data exchange for analysis and processing; which is low energy-efficient, has higher network costs and is not real-time. This method presents a novel accelerometer framework using a probabilistic algorithm that neutralizes the efficient of different Smartphone on-body placements as well as orientations to allow human movements to be more accurately and energy efficiently identified. Using only the embedded Smartphone accelerometer without need for historical data and accelerometer noise altering, our method can identify the human mobility state in real-time with a time constraint of 2 seconds. The method achieves 92 percent accuracy overall average classification of when evaluated on a data set gathered from fifteen individuals that classified nine different urban human Mobility State.

Keywords: Urban human mobility state, Smartphone, Accelerometer _______________________________________________________________________________________________________

I. INTRODUCTION The focus is on the type of the mobility instead of location context, but these two may be combined in complementary manner.

Mobility can be characterized at a low level as the rate of change of location in x; y; z directions and velocity with respect to time. Mobility represents an associated human mobility type of activity such as being stationary versus walking at a higher level of abstraction. Mobility allows u to move freely and easily. The mobility context can be used to define an associated activity context for e.g., being stationary for some time at a location context such as a cafor restaurant at time can indicate someone taking a lunch break. Mobility patterns of acceleration can be used to determine the travel as well as transportation mode of the user i.e. the user is in a moving vehicle versus walking. The existing model is relatively insensitive to noisy data. In this it is found that even though the noise we can reduced when we apply Kalman filtering , to computational features were stymied in the output making it use redundant in classification between different user mobility states. The main advantage of using accelerometer in Smartphone’s is that it will gives you exact state of what is going on? or what is your current state?.

Accelerometer in Smartphone able to detection of the changes in orientation and tell the rotate to screen .In this, the system should decide from which point it is start to collecting the sample point and also decide where to stop collecting sample points.

There is an increasing demand and need to implement for having exact state of human and overcome its problems. Mainly we focused on android based phones, that EHMS can be used with any type of Smartphone as the micro-electro-mechanical systems (MEMS) specifications similar across a range of embedded systems.

II. COMPARATIVE STUDY The accelerometer is the most precious non-transceiver sensor used to present the information about movement forces and the data for activity monitoring. Our main focus is on using only the Smartphone accelerometer for user state classification. The

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Improve Energy-Efficient Real-Time Human Mobility State Classification using Smartphone (J4R/ Volume 01 / Issue 08 / 002)

accelerometer has some advantages in excess of transceiver based location signal sensors. First, less energy consumption of 96 mW as and 330 mW by GPS and 1426 mW for Wi-Fi scans. Second, when accelerometer starts there is no delay; where in GPS the receiving location updates are depends in start mode. In a hot start mode the Termed-Time-to Subsequent-Fix (TTSF) within 10 seconds and in a cold start mode the Time-To-First-Fix (TTFF) it is about 15 minutes. Third, in accelerometer sensor readings are continuously available whereas with the transceiver based sensors that are In this paper author Stefan Poslad, and Zelun Zhang have extracted light weight computation features those are Peak(P) , Through(T),TPT (sum of total no of peak and through), mm(difference between Maxima P T and Minimum P T), Pmm, Tmm. These values are computed for human mobility state classification one dimension of context the transportation mode of an individual outside to used method are Dynamic programming used based on the current power state of device features and the optimal strategy for minimizing the power consumption for this time limit and how to schedule features to satisfy the limit considering both possible GPS and Wi-Fi as well as radio delays. to used method are Detected changes-of-state use to Monitoring a persons mobility in these environments is thus important. Awearable mobility monitoring system is a promising tool for helping rehabilitation to specialists determine mobility. To detect mobility change of state using a mobile phone based approach. to used method are Decision tree based background sound. If speech is not detected, the background environment will simply be considered to loud or noisy data, and no further classification algorithm to be conducted to distinguish music, noise and other types of sound. Improve the recognition mobile phone accuracy, Reconstruction method issue used for the representation of the trajectory of the phones corner that touching writing/drawing surface from the measurements obtained from phones gyroscope and accelerometer.In this angular trajectory is used for reconstruction. Gyro pen is connected to handwriting recognition system to demonstrate that accuracy is enough for text entry . It have introduced AAMPL. The Accelerometer Augmented Mobile Phone Localization framework that utilizes phones physical localization service. It uses client-server architecture in which client collects X,Y and Z axes .accelerometer is used to collect sample point at one second intervals and sever is responsible for classifying the accelerometer data and using the results to the physical location have introduced to EnLoc i.e. Energy Efficient Localization framework is designed. This framework characterizes the optimal localization accuracy for given energy budget. Dynamic Programming solution is developed which takes GPS, Wi-Fi, GSM readings as input and outputs a sensor reading schedule with minimum ALE (avg. localization error).

Majorly focuses on use of sensor placed differently on body in order to get acceleration data and make its use in analyzing and differentiating various daily life task. Decision tree is generated in order to identify the task on 20 subjects. shown how user activity is recognized using accelerometer data. Four features are extracted from each of three axes of accelerometer, that are mean, standard deviation ,energy , correlation. Some activities are harder to recognize using single accelerometers in case Meta classifier is used.

III. CONCLUSION Location cannot determined along different state between some human body modality . e.g., GPS and Wi-Fi cant classification between cycling and traveling car ,bus, train within a traffic. The types of human body movement state other than location to be sensed to classify the i.e. e .g combination with location determined. Energy Efficient Human Mobility Sensor state classified human movement using a State Classification algorithm and feature extraction of accelerometer data on the any Smartphone device. The accelerometer collect the sample points within 2 seconds (8accelerometer samples i.e. one second can collect 4 sample and so on ) classify activities with a high accuracy.

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Improve Energy-Efficient Real-Time Human Mobility State Classification using Smartphone (J4R/ Volume 01 / Issue 08 / 002)

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