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i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 11 Issue 2

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Volume 7 Issue 3 July - September 2019

Research Paper

Wearable Health Monitoring Smart Gloves

K. Richa* , Samiksha Mirjha, Sewatisahu , Kunal Gaurav , Mukesh Chandrakar

* - *****Department of Electrical and Electronics Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Richa, K., Mirjha, S., Sewatisahu, Gaurav, K., and Chandrakar, M. (2019). Wearable Health Monitoring Smart Gloves. i-manager’s Journal on Digital Signal Processing. 7(3), 1-8. https://doi.org/10.26634/jdp.7.3.17155

Abstract

Nowadays, heart diseases are the major cause of death toll. Sometimes, a heart attack results in immediate death and in some case, patients need to be monitored after their first attack to reduce the risk of an attack. This paper is aimed to improve the patient monitoring with minimal number of staffs. In this paper, smart hand gloves are introduced to monitor the patient heart rate and the body temperature. Physiological health parameters are sensed which after collecting and processing, is displayed on the LCD screen. The system is also connected via IoT platform named Blynk, which assists the caretaker in remote monitoring. The data collected can be stored for future diagnostic purposes. Moreover, the system helps the patients to do basic activity on their own. It is equipped with buttons to enable the patient to communicate with the family members and doctors as well. The fitted buzzer will alert family members about emergency situations where the heart rate or body temperature is higher than normal range. Additionally, further advances can be made by real time camera setup to guard the patient's activity.

References

[1]. Ahmed, Z. U., Mortuza, M. G., Uddin, M. J., Kabir, M. H., Mahiuddin, M., & Hoque, M. J. (2018, December). Internet of things based patient health monitoring system using wearable biomedical device. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-5). IEEE. https://doi.org/10.1109/ CIET.2018.8660846

[2]. Anliker, U., Ward, J. A., Lukowicz, P., Troster, G., Dolveck, F., Baer, M., Keita, F., Schenker, E. B., Catarsi, F., Coluccini, L., Belardinelli, A., Shklarski, D., Alon, M., Hirt, E., Schmid, R., & Vuskovic, M. (2004). AMON: A wearable multiparameter medical monitoring and alert system. IEEE Transactions on Information Technology in Biomedicine, 8(4), 415-427. https://doi.org/10.1109/TITB.2004.837888

[3]. Arora, J., Singh, A., Singh, N. P., Rawat, S. S., & Singh, G. (2014, February). Heartbeat rate monitoring system by pulse technique using HB sensor. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE. https://doi.org/10.1109/ICICES.2014.7033986

[4]. Baker, S. B., Xiang, W., & Atkinson, I. (2017). Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access, 5, 26521-26544. https://doi.org /10.1109/ACCESS.2017.2775180

[5]. Bansal, P., Malik, M., & Kundu, R. (2018, March). Smart heart rate monitoring system. In 2018 IEEMA Engineer Infinite Conference (eTechNxT) (pp. 1-4). IEEE. https:// doi.org/10.1109/ETECHNXT.2018.8385347

[6]. Deshmukh, S. D., & Shilaskar, S. N. (2015, January). Wearable sensors and patient monitoring system: A review. In 2015 International Conference on Pervasive Computing (ICPC) (pp. 1-3). IEEE. https://doi.org/10.1109/ PERVASIVE.2015.7086982

[7]. Kabilan, P., Sakthivel, P., Karthi, S., & Kumar, G. M. M. (2019). Smart wearable IoT based Health Monitoring System (HMS) using BSN. International Research Journal of Engineering and Technology (IRJET), 6(2), 1193- 1196.

[8]. Khan, T., & Chattopadhyay, M. K. (2017, August). Smart health monitoring system. In 2017 International Conference on Information, Communication, Instru mentation and Control (ICICIC) (pp. 1-6). IEEE. https:// doi.org/10.1109/ICOMICON.2017.8279142

[9]. Majumder, S., Rahman, M. A., Islam, M. S., & Ghosh, D. (2018, September). Design and implementation of a wireless health monitoring system for remotely located patients. In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) (pp. 86-91). IEEE. https://doi.org/ 10.1109/CEEICT.2018.8628077

[10]. Megalingam, R. K., Unnikrishnan, D. K. M., Radhakrishnan, V., & Jacob, D. C. (2012, December). HOPE: An electronic gadget for home-bound patients and elders. In 2012 Annual IEEE India Conference (INDICON) (pp. 1272-1277). IEEE. https://doi.org/10.1109/ INDCON.2012.6420814

[11]. Miah, M. A. R., Basak, S., Huda, M. R., & Roy, A. (2013, May). Low cost computer based heart rate monitoring system using fingertip and microphone port. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 1-4). IEEE. https://doi.org/10.1109/ ICIEV.2013.6572703

[12]. Mohana, S. R., & Aradhya, H. R. (2015, April). Remote monitoring of heart rate and music to tune the heart rate. In 2015 Global Conference on Communication Technologies (GCCT) (pp. 678-681). IEEE. https://doi.org/ 10.1109/GCCT.2015.7342748

[13]. Omoogun, M., Ramsurrun, V., Guness, S., Seeam, P., Bellekens, X., & Seeam, A. (2017, July). Critical patient eHealth monitoring system using wearable sensors. In 2017 1st International Conference on Next Generation Computing Applications (NextComp) (pp. 169-174). IEEE. https://doi.org/10.1109/NEXTCOMP.2017.8016194

[14]. Paliwal, S., Lakshmi, C. V., & Patvardhan, C. (2016, December). Real time heart rate detection and heart rate variability calculation. In 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-4). IEEE. https://doi.org/10.1109/R10-HTC.2016.7906818

[15]. Patil, P., Waichal, R., Kumbhar, U., & Gadkari, V. (2017). Patient health monitoring system using IOT. International Research Journal of Engineering and Technology (IRJET), 4(1), 638-641.

[16]. Pawar, P. A. (2014, March). Heart rate monitoring system using IR base sensor & Arduino Uno. In 2014 Conference on IT in Business, Industry and Government (CSIBIG) (pp. 1-3). IEEE. https://doi.org/10.1109/CSIBIG. 2014.7057005

[17]. Sathya, M., Madhan, S., & Jayanthi, K. (2018). Internet of Things (IoT) based health monitoring system and challenges. International Journal of Engineering & Technology, 7(1.7), 175-178.

[18]. Vippalapalli, V., & Ananthula, S. (2016, October). Internet of Things (IoT) based smart health care system. In 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1229-1233). IEEE. https://doi.org/10.1109/ SCOPES.2016.7955637

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Research Paper

Health Monitoring and Eccentricity Fault Diagnosis of Induction Motor by Signal Processing Techniques

Khadim Moin Siddiqui* , Bhavesh Kumar Chauhan, Sonu Bala Garg*

, Babu Banarsi Das National Institute of Technology and Management (BBDNITM), Lucknow, Uttar Pradesh India.

IKG Punjab TechnicalUniversity, Jalandhar Campus, Punjab, India.

Moinsiddiqui, K., Chauhan, B. K., and Garg, S. B. (2019). Health Monitoring and Eccentricity Fault Diagnosis of Induction Motor by Signal Processing Techniques. i-manager’s Journal on Digital Signal Processing. 7(3), 9-15. https://doi.org/10.26634/jdp.7.3.17063

Abstract

In the present time, there is a strong need to develop an efficient health monitoring system to diagnose air-gap eccentricity fault of the induction motor at early stages. If the fault is diagnosed in the early stages then one can save the industry for millions of dollars. The main aim of the researchers is to develop a non-intrusive health monitoring system for induction motor health detection in relatively low cost and also ought to be powerful for detection of developing online faults in the early stages. In the induction motor, due to unbalanced magnetic pull, the airgap eccentricity faults occur and if this fault is not diagnosed in the early stages then it will lead to large revenue losses for the industry. This issue has been addressed in this research paper and an effort is made to give an competent health monitoring technique for this kind of fault detection purpose. To achieve better results, hybrid technique has been used to extract relevant information of the fault from the raw signal in the developing stage. The EMD and wavelet algorithm has been used jointly for efficient health monitoring purpose for inverter fed induction motor machine. Two techniques have been used for fault diagnosis purpose, one is FFT technique and the other is hybrid technique. It has been observed that the hybrid technique has given encouraging results over FFT technique.

References

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[6]. Bessous, N., Zouzou, S. E., Sbaa, S., Bentrah, W., Becer, Z., & Ajgou, R. (2017b, October). Static eccentricity fault detection of induction motors using MVSA, MCSA and discrete wavelet transform (DWT). In 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B) (pp. 1-10). IEEE. https://doi.org/10.1109/ICEE-B.2017. 8192035

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[9]. Dehina, W., Boumehraz, M., Kratz, F., & Fantini, J. (2019, September). Diagnosis and Comparison between stator current analysis and vibration analysis of static eccentricity faults in the induction motor. In 2019 4th International Conference on Power Electronics and their Applications (ICPEA) (pp. 1-4). IEEE. https://doi.org/10.1109 /ICPEA1. 2019.8911193

[10]. Dorrell, D. G., & Kayani, O. (2014). Measurement and calculation of unbalanced magnetic pull in wound rotor induction machine. IEEE Transactions on Magnetics, 50 (11), 1- 4. https://doi.org/10.1109 /TMAG.2014. 2326414

[11]. Dorrell, D. G., Thomson, W. T., & Roach, S. (1997). Analysis of airgap flux, current, and vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3- phase induction motors. IEEE Transactions on Industry applications, 33(1), 24-34. https://doi.org/ 10.1109/28.567073

[12]. Drif, M., & Cardoso, A. J. M. (2008). Airgap-eccentricity fault diagnosis in three-Phase induction motors by the complex apparent power signature analysis. IEEE Transactions on Industrial Electronics, 55(3), 1404-1410. https://doi.org/ 10.1109/TIE.2007.909076

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[18]. Siddiqui, K. M., Sahay, K., & Giri, V. K. (2014). Health monitoring and fault diagnosis in induction motor-a review. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(1), 6549-6565.

[19]. Siddiqui, K. M., Sahay, K., & Giri, V. K. (2015, December). Detection of bearing fault in inverter fed induction motor by transformative techniques. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE. https://doi.org/10.1109/ INDICON.2015.7443129

[20]. Siddiqui, K. M., Sahay, K., & Giri, V. K. (2016a, March). Airgap eccentricity fault detection in the transient condition for inverter driven induction motor drives. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCPCT. 2016.7530236

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Research Paper

Effect of Sampling Frequency on SNR in the Removal of Ocular Artifacts in EEG Signals using Wavelets

B. Krishna Kumar*

Department of Electronics and Communication Engineering, Methodist College of Engineering and Technology, Hyderabad, Telangana, India.

Kumar, B. K. (2019). Effect of Sampling Frequency on SNR in the Removal of Ocular Artifacts in EEG Signals using Wavelets. i-manager’s Journal on Digital Signal Processing. 7(3), 16-20. https://doi.org/10.26634/jdp.7.3.17343

Abstract

This study investigates the relationship between sampling frequency and SNR of Electroencephalogram (EEG) signal. The EEG is a standard technique for investigating the electrical activity of brains in different psychological and pathological states. At the time of EEG recording, various artifacts such as muscle activity, eye blinks, eye movements and electrical noise corrupt the EEG signal. Normally, EEG signals fall in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts have the similar statistical properties of EEG signals, and often interfere with EEG signal, making the analysis of EEG signals more complex. In this research paper, two different datasets were taken from Physionet data base. The sampling frequency of one dataset is 100Hz and the sampling frequency of another dataset is 250Hz. The research paper attempts to establish the relationship between sampling frequency and SNR of EEG signal. In this paper, the collected EEG signals are normalized and then mixed linearly with the normalized Electrooculography (EOG) signals, resulting in noisy EEG signals. Later soft and hard thresholding techniques were applied for detail coefficients and to estimate the SNR of the denoised EEG signals. This research paper concludes that signals with lower sampling rates provide better SNR than the signals with higher sampling rates. In addition to this, Haar wavelet provided better SNR compared to dB10 and Sym8 wavelets.

References

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[2]. Arab, M. R., Suratgar, A. A., Martínez-Hernández, V. M., & Rezaei Ashtiani, A. (2010). Electroencephalogram signals processing for the diagnosis of petit mal and grand mal epilepsies using an artificial neural network. Journal of Applied Research and Technology, 8(1), 120-128.

[3]. Gratton, G., Coles, M. G., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4), 468-484. https:/ doi.org/10.1016/0013- 4694(83)90135-9

[4]. Holmes, G. L., & Lombroso, C. T. (1993). Prognostic value of background patterns in the neonatal EEG. Journal of Clinical Neurophysiology, 10, 323-323.

[5]. Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., Mckeown, M. J., Iragui, V., & Sejnowski, T. J. (2000). Removing electroen cephalographic artifacts by blind source separation. Psychophysiology, 37(2), 163-178. https://doi.org/10.1111/1469-8986.3720163

[6]. Kumar, B. K. (2019). Denoising of EEG signals using wavelets and various thresholding techniques. International Journal of Electronics Engineering, 11(2), 261-269.

[7]. Kumar, B. K., Prasad, K. V. S. V. R., & Alekhya, D. (2016, August). Performance comparision of various thresholding techniques on the removal of ocular artifacts in the EEG signals. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 3, pp. 1-5). IEEE. https://doi.org/10.1109/INVENTIVE.2016.7830179

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Research Paper

Segmentation to Detect the Presence of Human during Natural Disasters

Chethana H. T* , Pooja C. A, P. Vamshika*

*-*** Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

Chethana, H. T., Pooja, C. A., and Vamshika, P. (2019). Segmentation to Detect the Presence of Human during Natural Disasters. i-manager’s Journal on Digital Signal Processing. 7(3), 21-26. https://doi.org/10.26634/jdp.7.3.16934

Abstract

Nowadays, object detection is widely used for various purposes. There is a need for detecting living objects so that various problems like humans getting stuck in an undesirable place, and to find the suspected objects out of a group of other objects. Image segmentation is a process of dividing a digital picture into many segments, which helps in getting meaningful and well analyzed data that can be used in obtaining a well-defined output. Detection of living beings during the natural disasters is the most challenging task. Our proposed system makes use of You Only Look Once (YOLOv3), the quickest and most accurate among object detection system. During natural disasters, objects/living beings are detected using YOLOv3 model and the same information will be sent to the rescue team so that lives of human beings will be saved quickly and effectively.

References

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Review Paper

A Review on Distributed Arithmetic and Offset Binary Coding

P. L. Lahari* , M. Bharathi, Yasha Jyothi M. Shirur*

* Department of Electronics and Communications Engineering, SreeVidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

Assistant Professor, Electronics and Communications Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

* Department of Electronics and Communications Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India.

Lahari, P. L., Bharathi, M., and Shirur, Y. J. M. (2019). A Review on Distributed Arithmetic and Offset Binary Coding. i-manager’s Journal on Digital Signal Processing. 7(3), 27-33. https://doi.org/10.26634/jdp.7.3.17035

Abstract

Distributed Arithmetic or Dispersed Arithmetic (DA) is named so considering the way that the number shuffling activities that give up in indication planning (e.g., addition, multiplication) are not "lumped" as a strong helpful component, but instead are passed on in a consistently unrecognizable way. Distributed Arithmetic is the procedure or technique, which is used most for the computation of the inner product between fixed and variable data vector. In Digital Signal Processing, the most often form met is sum of product, dot product, or inner product generation. This is the computation that is seen in DA. It is well suited for the implementation in Field Programmable Gate Array because of its usage in lookup tables. The main motivation of using Distributed Arithmetic is to increase the efficiency in computation whereas drawback raised here is for each added input line size of the ROM used increases exponentially. To overcome this, various techniques are preferred, among them Offset Binary Coding is suggested. By designing carefully, it is possible to reduce the total gate count in a signal processing arithmeticunit.

References

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