MEMS Accelerometer Based Hand Gesture Recognition

MEMS Accelerometer Based Hand Gesture quotationMEMS ACCELEROMETER BASED HAND GESTURE RECOGNITIONMeenaakumari.M1, M.Muthulakshmi21Dept.of ECE, Sri Lakshmi Aammal Engineering College, Chennai,2Asst.Prof, Dept.of ECE, Sri Lakshmi Aammal Engineering College, Chennai,Abstract This paper presents an MEMS accelerometer mostly ground on gesture realisation algorithmic ruleic ruleic rule and its applications. The hardw atomic figure of speech 18 staff consists of a triaxial mems accelerometer, microcontroller, and zigbee piano(prenominal)tuner transmission module for sensing and collecting quickenings of achievewriting and slip by gesture trajectories. Users provide use this hardware module to deliver down patterns, alphabets in digital kind by making four hand gestures. The quickenings of hand feats mensur equal to(p) by the accelerometer are transmitted wirelessly to a personal computer for trajectory credit. The trajectory algorithm serene of information assortment co llection, signal preprocessing for speculateing the trajectories to attenuate the cumulative errors cause by drift of sensors. So, by changing the couch of MEMS (micro electro mechanical systems) we croup able to show the alphabetical instances and numerical within the PC.Keywords MEMS accelerometer, gesture, handwritten acknowledgement, trajectory algorithm. aditNOW A DAYS, the expansion of human machine interaction technologies in electronic spells has been greatly trim the proportion and weight of consumer electronics products such as smart phones and handheld computers, and therefore will change magnitudes our daylight to day convenience. Recently, an attractive alternative, a conveyable embedded device with inertial sensors, has been projected to sense the activities of human and to catch their interrogation trajectory information from accelerations for handwriting and recognizing gestures. The foremost necessary advantage of inertial sensors for general effort s ensing is that they can be operated without any external reference and demarcation in operating conditions. However, exploit trajectory cognizance is comparatively tough for different users since they withstand different speeds and styles to generate various motion trajectories. Thus, several researchers have tried to avoid the problem domain for increasing theaccuracy of handwriting recognition systems. During this work a miniature MEMS accelerometer base recognition systems which acknowledge four hand gestures in three-D is constructed by using this four gestures, numerical and alphabets will be recognized in the digital format.MEMS are termed as micro electro mechanical system where mechanical parts like cantilevers or membranes have been manufacture at microelectronics locomotes. It uses the technology known as micro-fabrication technology. It has holes, cavity, channels, cantilevers, membranes and redundantly imitates mechanical parts. The emphasis on MEMS is based on s ilicon. The explanation that prompt that prompt the utilization of MEMS technology are for example miniaturization of actual devices, victimisation of new devices based on principal that do non work at large carapace and to interact with micro world. Miniaturization reduces cost by decreasing material consumption. It also increases applicability by reducing mass and size allowing placing the, MEMS in places where a traditional system. Instead of having a series of external factors connected by wire or soldered to printed circuit board the MEMS on silicon can be integrated directly with the electronics. These are called smart integrated MEMS already include data acquisition, separateing, data storage, confabulation interfacing and ne dickensrking. MEMS technology not only makes the things smaller but often makes them better. A typical example is brought by the accelerometer development.An accelerometer is a device that measures the physical acceleration. The physical parameter s are temperature, pressure, force, light etc. it measures the weight per unit mass. By contrast, accelerometers in free fall or at rest in outer space will measure zero. Another term for the type of acceleration that accelerometers can measure is g-force. It works on the regulation of displacement of a small proof mass etched into the silicon surface of the integrated circuit and suspended by small beams.RELATED WORKThere are mainly two existing types of gesture recognition orders, i.e., vision-based and accelerometer and/or gyroscope based. Due to some limitations like ambient optical noise, sluggish dynamic response, and relatively large data collections/processing of vision-based method 1, our recognition system is implemented based on an inertial measurement unit based on MEMS acceleration sensors. If gyroscopes are used for inertial measurement 2 it causes heavy computational burden, thus our system is based on MEMS accelerometers only and gyroscopes are not implemented. Ma ny researchers have focused on developing effective algorithms for error compensation of inertial sensors to cleanse the recognition accuracy. For few examples, Yang et al. 3 proposed a pen-type input device to track trajectories in three-D space by using accelerometers and gyroscopes. An efficient acceleration error compensation algorithm based on zero fastness compensation was true to decrease the acceleration errors for acquiring faultless reconstructed trajectory. An extended Kalman l from each one with magnetometers (micro inertial measurement unit (IMU) with magnetometers), proposed by Luo et al. 10, was employed to compensate the orientation of the proposed digital writing cats-paw. If the orientation of the instrument was estimated precisely, the motion trajectories of the instrument were reconstructed accurately. Dong et al. 4 proposed an optical tracking calibration method based on optical tracking system (OTS) to calibrate 3-D accelerations, angular velocities, and space attitude of handwriting motions. The OTS was developed for the following two goals 1) to obtain accelerations of the proposed ubiquitous digital writing instrument (UDWI) by calibrating 2-D trajectories and 2) to obtain the accurate attitude angles by using the multiple camera calibration. However, in order to recognize or reconstruct motion trajectories accurately, the aforementioned approaches introduce other sensors such as gyroscopes or magnetometers to obtain precise orientation. This increases additional cost for motion trajectory recognition systems as well as computational burden of their algorithms.In this paper, a portable device has been developed with a trajectory recognition algorithm. The portable device consists of a triaxial accelerometer, a microprocessor, and an zigbee wireless transmission module. The acceleration signals measurable from the triaxial accelerometer are transmitted to a computer via the zigbee wireless module. Users can utilize this portal de vice to write digits and make hand gestures at normal speed. The measured acceleration signals of these motions can be recognized by the trajectory recognition algorithm. The recognition procedure is still of acceleration acquisition, signal preprocessing, lark about generation, cavort selection, and feature origin. The acceleration signals of hand motions are measured by the portable device. The signal preprocessing procedure consists of calibration, a moving average filter, a high-pass filter, and normalization. First, the accelerations are calibrated to rack up drift errors and offsets from the raw signals. These two filters are applied to accept high frequency noise and gravitative acceleration from the raw data, respectively. The features of the preprocessed acceleration signals of each axis include mean, correlation among axes, interquartile range (IQR), mean rank(a) deviation (MAD), root mean square (rms), VAR, standard deviation (STD), and might. Before sort outifyi ng the hand motion trajectories, we commit the procedures of feature selection and extraction methods. In general, feature selection aims at selecting a subset of size m from an master key set of d features (d m). Therefore, the criterion of nubble-based class separability (KBCS) with best individual N (BIN) is to select world-shattering features from the original features (i.e., to pick up some important features from d) and that of linear discriminate analysis (LDA) is to reduce the dimension of the feature space with a better recognition performance (i.e., to reduce the size of m). The objective of the feature selection and featureextraction methods is not only to eradicate the burden of computational load but also to increase the accuracy of classification. The decreased features are used as the inputs of classifiers. The contributions of this paper include the following 1) the development of a portable device with a trajectory recognition algorithm, i.e., with the hardwar e module , can give coveted commands by hand motions to control electronics devices anywhere without space limitations, and 2) an effective trajectory recognition algorithm, i.e., the proposed algorithm can efficiently select significant features from the time and frequency domains of acceleration signals and project the feature space into a smaller feature dimension for motion recognition with high recognition accuracy.III.HARDWARE DESIGN OFPORTABLE thingumabobThe portable device consists of a triaxial accelerometer (MMA2240), a microcontroller (C8051F206 with a 12-b A/D converter), and a wireless transceiver (nRF2401, Nordic). The triaxial accelerometer measures the acceleration signals generated by a users hand motions. The microcontroller collects the analog acceleration signals and converts the signals to digital ones via the A/D converter. The wireless transceiver transmits the acceleration signals wirelessly to a personal computer (PC).The MMA2240 is a low-cost capacitive m icro machined accelerometer with a temperature compensation extend and a g-select function for a full-scale selection of +_2 g to +_6 gand is able to measure accelerations over the bandwidth of 0.5 kHz for all axes. The accelerometers sensitivity is set from 2 g to +2 g. The C8051F206 integrate a high-performance 12-b A/D converter and an optimized signal cycle 25-MHz 8-b microcontroller unit (MCU) (8051 instruction set compatible) on a signal chip. The output signals of the accelerometer are sampled at 100 Hz by the 12-b A/D converter. Then, all the data sensed by the accelerometer are transmitted wirelessly to a PC by an zigbee transceiver at 2.4-GHz transmission band with 1-Mb/s transmission rate. The overall power consumption of the digital pen circuit is 30 mA at 3.7 V. The block diagram of the portable device is shown in Fig. 1.MEMSPICACCELEROMTERMICROCONZIGBEE TX angler fishPCRS 232ZIGBEE RXFig.1. Block diagram of the portable device.IV. TRAJECTORY RECOGNITIONALGORITHMThe pr oposed trajectory recognition algorithm consisting of acceleration acquisition, signal preprocessing, feature generation, feature selection, and feature extraction. In this paper, the motions for recognition include Arabic numerals alphabets. The acceleration signals of the hand motions are measured by a triaxial accelerometer and then preprocessed by filtering and normalization.Consequently, the features are extracted from the preprocessed data to reconcile the characteristics of different motion signals, and the feature selection process based on KBCS picks p features out of the original extracted features. To reduce the computational load and increase the recognition accuracy of the classifier, LDA is utilized to decrease the dimension of the selected features. The reduced feature vectors are then fed into a PNN classifier to recognize the motion to which the feature vector it belongs.A. subscribe PreprocessingThe microcontroller collects the acceleration signals of hand motion s which are generated by the accelerometer. Due to slight tremble movement of hand authorized amount of noise is generated. The signal preprocessing consists of calibration, a moving average filter, a high-pass filter, and normalization. First, the accelerations are calibrated to remove drift errors and offsets from the raw signals. The second step of the signal preprocessing is to use a moving average filter to reduce the high-frequency noise of the calibrated accelerations, and the filter is expressed aswhere xt is the input signal, yt is the output signal, and N is the enactment of points in the average filter. In this paper, we set N = 8. The decision of using an eight-point moving average filter is based on our empirical tests. Then, a high-pass filter is used to remove the gravitational acceleration from the filtered acceleration to obtain accelerations caused by hand movement. In general, the size of samples of each movement between fast and behindhand writers is different . Therefore, after filtering the data, we first segment each movement signal properly to extract the exact motion interval. Then, we normalize each segmented motion interval into equal sizes via interpolation.B. let GenerationThe characteristics of different hand movement signals can be obtained by extracting features from the preprocessed x-,Fig 2 Block diagram of the trajectory recognition algorithm.5) Correlation among axes The correlation among axes is computed as the ratio of the covariance to the product of the STD for each pair of axes. For example, the correlation (corrxy) between two variables x on x-axis and y on y-axis is outlined aswhere E represents the expected nourish, x and x are STDs, and mx and my are the expected values of x and y, respectively.6)MAD7)rmsY-, and z-axis signals, and we extract eight featureswhere xi is the acceleration instance and m isfrom the triaxial acceleration signals, including mean,the mean value of xi in (6) to (7).STD, VAR, IQR 6, corre lation between axes 7,MAD, rms, and energy 8 . They are explicated asfollows.8) Energy Energy is calculated as the sum of1) Mean The mean value of the accelerationthe magnitudes of squared discrete fastsignals of each hand motion is the dcFourier transform (FFT) components of thecomponent of the signalsignal in a window. The equation is definedaswhere W is the length of each hand motion.2) STD STD is the square root of VARwhere Fi is the ith FFT component of thewindow and Fi is the magnitude of Fi.C. Feature SelectionFeature selection comprises a selection criterion. TheKBCS can be computed as follows let (x, y) (Rd -3)VARY)represents a sample, where Rd denotes a ddimensional feature space, Y symbolizes the set ofclass labels, and the size of Y is the number of class c.This method projects the samples onto a spirit space,where xi is the acceleration instance and m isand m i is defined as the mean vector for the I thclass in the kernel space, ni denotes the number ofthe mean value o f xi in (3) and (4).samples in the ith class, m denotes the mean vector4)IQR When differentclasses havesimilarfor all classes in the kernel space, S B denotes thebetween-class scatter matrix in the kernel space, andmean values,theinterquartilerangeS/ Wdenotes the within-class scatter matrix in therepresentsthedispersionofthe dataandkernel space. Let () be a possible nonlineareliminatestheinfluenceofoutliersinthemapping from the feature space Rd to a kernel spacedata. and tr(A) represents the trace of a square matrixA.1889www.ijarcet.orgISSN 2278 1323International Journal of Advanced Research in Computer Engineering Technology (IJARCET) Volume 2, No 5, May 2013The following two equations are used in the class separability measure1234The class separability in the kernel space can be measured asTo maintain the numerical stability in the maximisation of J , the denominator tr(S W ) has to be prevented from approaching zero.IV. EXPERIMENTAL RESULTSIn this section, the effectiveness of trajectory recognition algorithm is validated.A.Handwritten Digit RecognitionThe acceleration signals after the signal preprocessing procedure of the proposed trajectory recognition algorithm for the digit 0. The calibrated acceleration signals acquired from the accelerometer module are shown. With the preprocessed accelerations, alphabets and numerical features are generated by the feature generation procedure. Subsequently, the KBCS is take to choose characteristic features from the generated features. We choose digits 0 and 6 to illustrate the effectiveness of the KBCS, since their accelerations and handwritten trajectories are exquisite similar and difficult to classify. The IQR features of these two digits are closely overlapped. Thus, the features are not effective for12345678910Fig. 4. Trajectories of four hand gestures.corrxy, meanz, energyx, energyy, and energyz selected by the KBCS. Finally, the dimension of the selected features was further reduced by the LDA not only to ease the burden of computational load but also to increase the accuracy of classification.Fig. 5.a Trajectories of alphabetsFig. 5.b. Trajectories of alphabets.Fig. 6. IQR features of (red star) digit 0 and (blue diamond) digit 6.Fig. 3. Generation of numerical1890www.ijarcet.orgISSN 2278 1323International Journal of Advanced Research in Computer Engineering Technology (IJARCET) Volume 2, No 5, May 2013Fig. 6.a. Mean feature of (red star) digit 0 and digit (blue diamond) 6.Therefore, the total testing samples were 100 (10 - 10 - 1) for the testing procedure, and the total training samples were 900 (10 - 10 - 9) for the raining procedure. Because there are ten digits needed to be classified, the maximum of the dimension of the feature extraction by the LDA was nine. To see the performance variation caused by feature dimensions, we varied the dimensions of the LDA from one to nine. In Fig. 10, the best average recognition rate ofFig. 7. Average recognition rates versus the featur e dimensions of the PNN classifier by using the LDA.Fig. 8. Average recognition rates versus the feature dimensions of the PNN classifier by using the KBCS.V. consequenceThe development of a portable device, is used to generate desired commands by hand motions tocontrol electronic devices without space limitations. The time and frequency domains of acceleration signals of motion recognition, which has high recognition accuracy. The acceleration made by the hand gesture is measured by accelerometer are wirelessly transmitted to computer. In the experiments, we used 2-D handwriting digits, alphabets by using four hand gestures to validate the effectiveness of the proposed device and algorithm. The overall handwritten digit recognition rate was 98%, and the gesture recognition rate was also 98.75%. This result encourages us to further check up on the possibility of using our digital pen as an effective tool for HCI applications. In this project, an additional push can be used to all ow users to indicate the starting point and ending point of motion. That is, the limitation of the proposed trajectory recognition algorithm is that it can only recognize a letter or a number finished with a single stroke.VI. FUTURE ENHANCEMENTThe algorithms can be developed for letter or words with multistrokes which involve more challenging problems.REFERENCESS. Zhou, Q. Shan, F. Fei, W. J. Li, C. P. Kwong, and C. K. Wu et al.,Gesture recognition for interactive controllers using MEMS motion sensors, in Proc. IEEE Int. Conf. Nano/Micro Engineered and MolecularSystems, Jan. 2009,pp. 935-940.S. Zhang, C. Yuan, and V. Zhang, Handwritten character recognition using orientation quantization based on 3-D accelerometer, presented at the 5th Annu. Int. Conf. Ubiquitous Systems, Jul. 25th, 2008.J. Yang, W. Chang, W. C. heraldic bearing, E. S. Choi, K. H.Kang, S. J. Cho, and D. Y. 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