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Comparison of Classification Algorithms for Detecting Patient Posture in Expandable Tumor ProsthesesKOCAOGLU, S. , AKDOGAN, E.
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biomedical measurement, machine learning, prosthetics, supervised learning, support vector machines
recognition(19), posture(13), activity(12), wearable(11), detection(9), sensors(8), comput(8), biomed(8), system(7), human(7)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 131 - 138
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02015
Web of Science Accession Number: 000537943500015
SCOPUS ID: 85087436149
Autonomous tumor prostheses are extended without the need of a clinic and of a medical supervision. It is necessary to make sure that the patient is not standing before extending these prostheses. This study aims to determine the posture of the patient for expandable tumor prostheses by employing oft-used three machine learning-based classification methods through comparing them all with each other. Patient posture is determined by using accelerometer and gyroscope data from inertial control unit placed in autonomous expandable tumor prosthesis. By using the created dataset, 48 features are extracted. Then, for optimization, with feature selection, the number of features is reduced to 10. The selected features are processed using the decision tree, the k-nearest neighborhood and support vector machine algorithms. These algorithms were compared with each other using machine learning performance parameters. Accuracy, recall, precision and F-score values are calculated and compared. Consequently, support vector machine is determined as the most successful technique. Then, the model is tested on the experimental setup developed within the scope of the study, and the posture is determined. It is found that with this system, in the presence of a load on the prosthesis, it can be accurately detected at a rate of 97.1% (the recall parameter).
|References|||||Cited By «-- Click to see who has cited this paper|
| G. J. S. Verkerke, "Design of a lengthening element for a modular femur endoprosthetic system," J Eng Med,vol. 203 pp. 97-102, 1989. |
 G. J. S. Verkerke, H. S. Koops, R. P. H. Veth, H. H. van den Kroonenberg, H.J. Grootenboer, H. K. L. Nielsen, J. Oldhoff and A. Postma, "An extendable modular endoprosthetic system for bone tumour management in the leg," J Biomed Eng vol. 6 pp.12:19, 1990.
[CrossRef] [Web of Science Times Cited 10]
 M. D. Neel, R. M. Wilkins, B. N. Rao, C. M. Kelly, "Early Multicenter Experience With a Noninvasive Expandable Prosthesis," Clin Orthop Relat Res vol. 415 pp. 72-81. 2003.
[CrossRef] [Web of Science Times Cited 67]
 A. Gupta, J. Meswania, R. Pollock, S.R. Cannon, T. W. R. Briggs, S. Taylor and G. Blunn, "Non-invasive distal femoral expandable endoprosthesis for limb-salvage surgery in paediatric tumours," J Bone Joint Surg Br, vol. 88 pp. 649-654. 2006.
[CrossRef] [Web of Science Times Cited 67]
 J. M. Meswania, S. J. G. Taylor, and G. W. Blunn, "Design and characterization of a novel permanent magnet synchronous motor used in a growing prosthesis for young patients with bone cancer.," Proc. Inst. Mech. Eng. Part H-Journal Eng. Med., vol. 222, no. 3, pp. 393-402, 2008.
[CrossRef] [Web of Science Times Cited 7]
 P. Borkowski, K. Skalski, "Expandable endoprosthesis for growing patients-Reliability and research," Biocybern Biomed Eng vol. 34, pp. 199-205. 2014.
[CrossRef] [Web of Science Times Cited 3]
 K. K. Sarma, "Neural network based feature extraction for assessment character and numeral recognition, International Journal of Artificial Intelligence. 2009, 2 (S09): 37-56.
 C. Pozna, R.-E. Precup, J.K. Tar, I. Skrjanc, S. Preitl, "New results in modelling derived from Bayesian filtering Knowledge-Based Systems", 23 (2) (2010), pp. 182-194.
[CrossRef] [Web of Science Times Cited 48]
 Alvarez Gil R. P., Johanyak Z. C., Kovacs T. Surrogate model based optimization of traffic lights cycles and green period ratios using microscopic simulation and fuzzy rule interpolation International Journal of Artificial Intelligence. 2018, 16 (1), pp. 20-40.
 Albu A, Precup R.E, Teban T.A. Results and challenges of artificial neural networks used for decision-making and control in medical applications. FU Mech Eng. 2019; 17(3): 285-308.
[CrossRef] [Web of Science Times Cited 19]
 D. Yang, J. Huang, X. Tu, G. Ding, T. Shen, X. Xiao, "A Wearable Activity Recognition Device Using Air-Pressure and IMU Sensors," IEEE Access vol. 7 pp. 6611-6621, 2019.
[CrossRef] [Web of Science Times Cited 5]
 D. RodrÃguez-MartÃn, C. Perez-Lopez, A. Sama, J. Cabestany, A. CatalÃ , "A wearable inertial measurement unit for long-term monitoring in the dependency care area." Sensors (Switzerland) vol. 13 pp. 14079-14104, 2013.
[CrossRef] [Web of Science Times Cited 42]
 T. Nguyen Gia, V. K. Sarker, I. Tcarenko, A. M. Rahmani, T. Westerlund, P. Liljeberg, H. Tenhunen, "Energy efficient wearable sensor node for IoT-based fall detection systems," Microprocess Microsyst vol. 56, pp. 34-46, 2018
 Y. Ma, N. Amini, H. Ghasemzadeh, "Wearable sensors for gait pattern examination in glaucoma patients," Microprocess Microsyst vol. 46 pp. 67-74, 2016.
[CrossRef] [Web of Science Times Cited 5]
 S. Afifi, H. G. Hosseini, R.A. Sinha, "A system on chip for melanoma detection using FPGA-based SVM classifier," Microprocess Microsyst, vol. 65 pp. 57-68, 2019.
[CrossRef] [Web of Science Times Cited 9]
 A. Alvarez-Alvarez, G. Trivino, O. CordÃ³n, "Body posture recognition by means of a genetic fuzzy finite state machine," in IEEE 5th Int Work Genet Evol Fuzzy Syst, 2011, pp. 60-65.
 G. Diraco, A. Leone, P. Siciliano, "An active vision system for fall detection and posture recognition in elderly healthcare," Proc. Conf. Des. Autom. Test Eur., EDAA; 2010, pp. 1536-1541.
 M. Yu, A. Rhuma, S. M. Naqvi, L. Wang, J. Chambers, "A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment," IEEE Trans Inf Technol Biomed, 2012, pp. 1274- 1286.
[CrossRef] [Web of Science Times Cited 147]
 B. Boulay, F. Bremond, M. Thonnat, "Applying 3D human model in a posture recognition system," Pattern Recognit Lett, vol. 27, pp. 1788-1796, 2006.
[CrossRef] [Web of Science Times Cited 29]
 T. L. Le, M. Q. Nguyen, T. T. M. Nguyen, "Human posture recognition using human skeleton provided by Kinect," Int Conf Comput Manag Telecommun:ComManTel2013, 2013, pp. 340-345.
 C. Chi-Wei, I. Cohen, "Posture and Gesture Recognition using 3D Body Shapes Decomposition," IEEE Comput Soc Conf Comput Vis Pattern Recognit - Work , 2006, 69-76.
 N. Foubert, A. M. McKee, R. A. Goubran, F. Knoefel, "Lying and sitting posture recognition and transition detection using a pressure sensor array, IEEE Symp Med Meas Appl Proc 2012, pp. 65-70.
 J. Huang, X. Yu, Y. Wang, X. Xiao, "An integrated wireless wearable sensor system for posture recognition and indoor localization," Sensors (Switzerland), vol. 16, pp. 1-24, 2016.
[CrossRef] [Web of Science Times Cited 18]
 H. Gjoreski, M. Lustrek, M. Gams, "Accelerometer placement for posture recognition and fall detection," Proc -7th Int Conf Intell Environ IE 2011, pp. 47-54.
 F. R. Allen, E. Ambikairajah, N. H. Lovell, B. G. Celler, "Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models," Physiol Meas, vol. 27, pp. 935-951, 2006.
[CrossRef] [Web of Science Times Cited 103]
 E. S. Sazonov, G. Fulk, J. Hill, Y. Schutz, R. Browning, "Monitoring of posture allocations and activities by a shoe-based wearable sensor," IEEE Trans Biomed Eng, 2011, vol. 58 pp. 983-990.
[CrossRef] [Web of Science Times Cited 121]
 J. Chen, J. Qiu, C. Ahn, "Construction worker's awkward posture recognition through supervised motion tensor decomposition." Autom Constr, vol. 77, pp. 67-81, 2017.
[CrossRef] [Web of Science Times Cited 44]
 S. Kocaoglu, E. Akdogan, "Design and development of an intelligent biomechatronic tumor prosthesis," Biocybern Biomed Eng, vol. 39, pp. 561-570, 2019.
[CrossRef] [Web of Science Times Cited 1]
 K.K. Tan, S. Zhao, J.-X. Xu Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach IET Control Theory Appl., 1 (2007), pp. 90-96.
[CrossRef] [Web of Science Times Cited 53]
 S. Preitl, R. E. Precup, Z. Preitl, S. Vaivoda, S. Kilyeni, and J. K.Tar, "Iterative feedback and learning control. Servo systems applications," IFAC Workshop ICPS07, vol. 40, no. 8, pp. 16-27, Jul. 9-11, 2007.
 Ruiz-Rangel, J.; Hernandez, C.J.A.; Gonzalez, L.M.; Molinares, D.J. Ernead: Training of Artificial Neural Networks based on a Genetic Algorithm and Finite Automata Theory. Int. J. Artif. Intell. 2018, 16, 214-253.
 A. Bulling, U. Blanke, B. Schiele, "A tutorial on human activity recognition using body-worn inertial sensors," ACM Comput Surv vol. 46 pp. 1-33, 2014.
[CrossRef] [Web of Science Times Cited 533]
 N. Ravi, N. Dandekar, P. Mysore, M. L. Littman, "Activity Recognition from Accelerometer Data," Procedia Technol vol. 7 pp. 248-56, 2013.
[CrossRef] [Web of Science Times Cited 17]
 E. M. Tapia, S.S. Intille, W. Haskell, K. Larson, J. Wright, A. King and R. Friedman, "Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor," 11th IEEE Int. Symp. Wearable Comput., IEEE; 2007, pp. 1-4.
 Y. Liu, L. Nie, L. Liu, D. S. Rosenblum, "From action to activity: Sensor-based activity recognition," Neurocomputing vol. 181, pp. 108-115, 2016.
[CrossRef] [Web of Science Times Cited 346]
 S. Halkiotis, T. Botsis, M. Rangoussi. "Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks", Signal Processing, vol. 87, no. 7, pp. 1559-1568, 2007.
[CrossRef] [Web of Science Times Cited 71]
 M. Hekim, A. A. Yurdusev, C. Oral. "The detection and classification of microcalcifications in the Visibility-Enhanced mammograms obtained by using the Pixel Assignment-Based spatial filter." Advances in Electrical and Computer Engineering, vol. 19, no. 4, pp. 73-82, 2019.
[CrossRef] [Full Text] [Web of Science Times Cited 1]
 M. Ermes, J. Parkka, J. Mantyjarvi and I. Korhonen, "Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions," IEEE Trans. Inf. Tech. Biomed, 2006, vol.12 pp 20-26.
[CrossRef] [Web of Science Times Cited 398]
 J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola and I. Korhonen "Activity classiï¬cation using realistic data from wearable sensors," IEEE Trans. Inf. Technol. Biomed., 2006, vol. 10, pp. 119-128.
[CrossRef] [Web of Science Times Cited 414]
 N. Ravi, N. Dandekar, P. Mysore and M. Littman, "Activity recognition from accelerometer data," Proceedings of the 7th Innovative Applications of Artiï¬cial Intelligence Conference, 2005, pp. 11-18.
 S. J. Preece, J. Y. Goulermas, L. P. J Kenney, D. Howard, K. Meijer and R. Crompton. "Activity identiï¬cation using body-mounted sensors-a review of classiï¬cation techniques", Physiological Measurement, vol. 30, R1, 2009.
[CrossRef] [Web of Science Times Cited 335]
 S. J. Preece, J. Y. Goulermas, L. P. J. Kenney and D. Howard, "A comparison of feature extraction methods for the classiï¬cation of dynamic activities from accelerometer data," IEEE Trans. Biomed. Eng. vol. 36, pp. 871-879, 2008.
[CrossRef] [Web of Science Times Cited 283]
 J. Fahrenberg, F. Foerster, M. Smeja and W. Muller, "Assessment of posture and motion by multichannel piezoresistive accelerometer recordings," Psychophysiology vol.34, pp. 607-12, 1997.
[CrossRef] [Web of Science Times Cited 94]
 F. Foerster and J. Fahrenberg, "Motion pattern and posture: correctly assessed by calibrated accelerometers,"Behav. Res. Methods Instrum. Comput, vol.32 pp. 450-457, 2000.
[CrossRef] [Web of Science Times Cited 104]
 J. B. Bussmann, W. L. Martens, J. H. Tulen, F. C. Schasfoort, H. J. van den Berg-Emons and H. J. Stam, "Measuring daily behavior using ambulatory accelerometry: the activity monitor," Behav. Res. Methods Instrum. Comput. vol. 33 pp. 349-356, 2001.
[CrossRef] [Web of Science Times Cited 221]
 A. Krause, M. Ihmig, E. Rankin, D. Leong, S. Gupta, D. Siewiorek, A. Smailagic, M. Deisher and U. Sengupta, "Trading off prediction accuracy and power consumption for context-aware wearable computing," Proc. of the 9th IEEE International Symposium on Wearable Computers, 2005, pp 20-26.
[CrossRef] [Web of Science Times Cited 63]
 Y. Kim and H. Ling, "Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine," in IEEE Transactions on Geoscience and Remote Sensing, 2009, vol. 47, no. 5, pp. 1328-1337.
[CrossRef] [Web of Science Times Cited 414]
 D. Anguita, A. Ghio, L. Onet, X. Parra, J. L. Reyes-Ortiz, "Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine," In: Bravo J., HervÃ¡s R., RodrÃguez M. (eds) Ambient Assisted Living and Home Care. IWAAL, 2012.
 N. Gopalakrishna, V. Krishnan, V. Gopalakrishnan, "Ensemble Feature Selection to Improve Classification Accuracy in Human Activity Recognition," In: Ranganathan G., Chen J., Rocha A. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 89. Springer, Singapore, 2020.
 I. Guyon, A. Elisseeff, "An Introduction to Variable and Feature Selection," An Introd to Var Featur Sel, vol. 3, pp. 1157-1182, 2003.
[CrossRef] [Web of Science Times Cited 99]
 Y. W. Chen, C. J. Lin, "Combining SVMs with various feature selection strategies," Stud Fuzziness Soft Comput vol. 207 pp. 315-324, 2006.
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