Wajeeha KHALIL, Umar ZIA, Iftikhar AHMAD, Salabat KHAN, Muhammad Naeem KHAN

Towards human activity recognition for ubiquitous health care using data from a waist-mounted smartphone

Understanding human activities is a newly emerging paradigm that is greatly involved in developingubiquitous health care (u-Health) systems. The aim of these systems is to seamlessly gather knowledge about thepatient’s health and, after collecting knowledge, make suggestions to the patient according to his/her health profile.For this purpose, one of the most important ubiquitous communication trends is the smartphone, which has drawn theattention of both professionals and caregivers for monitoring the aging population, childcare, fall detection, and cognitiveimpairment. Recognizing human actions in a ubiquitous environment is very challenging and researchers have extensivelyinvestigated different methods to recognize human activities in the past decade. However, this field of research still needsfurther exploration in order to improve the accuracy and reduce the computational cost of these health care systems.Therefore, for expediting the existing system, this research work investigated a novel approach based on feature selectionand classification. In the proposed work, sparse Bayesian multinomial logistic regression (SBMLR) is used for featuresubset selection and a multiclass support vector machine (SVM) is adapted for the classification of six human dailyactivities (laying down, walking up stairs, walking down stairs, sitting, standing, and walking). For identifying the bestfeatures among the features returned by the SBMLR, a tuned threshold value is used for the selection of the features.Further, other classification algorithms including K-nearest neighbor, decision tree, and naive Bayes and different featureselection methods such as principal component analysis and random subset feature selection are also used for evaluationand comparison. The dataset used for testing is obtained from the UCI Machine Learning Repository. It is collectedby using a smartphone embedded with an accelerometer and gyroscope. The experimental results show that the highestaccuracy of 99.40% can be achieved by using the proposed method. Moreover, the paired sample two-tailed t-test overthe significance level of 0.05 reveals that the performance difference between the proposed technique and a competingtechnique is statistically significant.

PDF

___

[1] Ponce H, Martínez-Villaseñor M, Miralles-Pechuán L. A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks. Sensors 2016; 16 (7): 1033. doi: 10.3390/s16071033
[2] Jalal A, Kamal S, Kim D. A depth video-based human detection and activity recognition using multi-features and embedded hidden markov models for health care monitoring systems. International Journal of Interactive Multimedia & Artificial Intelligence 2017; 4 (4): 54-62. doi: 10.9781/ijimai.2017.447
[3] Braeken A, Porambage P, Gurtov A, Ylianttila M. Secure and efficient reactive video surveillance for patient monitoring. Sensors 2016; 16 (1): 32. doi: 10.3390/s16010032
[4] Hsu SC, Chuang CH, Huang CL, Teng R, Lin MJ. A video-based abnormal human behavior detection for psychiatric patient monitoring. In: International Workshop on Advanced Image Technology (IWAIT); Chiang Mai, Thailand; 2018. pp. 1-4. doi: 10.1109/IWAIT.2018.8369749
[5] Maurer U, Smailagic A, Siewiorek DP, Deisher M. Activity recognition and monitoring using multiple sensors on different body positions. In: International Workshop on Wearable and Implantable Body Sensor Networks (BSN’06); Cambridge, MA, USA; 2006. pp. 113-116. doi: 10.1109/BSN.2006.6
[6] Wang L, Gu T, Tao X, Lu J. Toward a wearable RFID system for real-time activity recognition using radio patterns. IEEE Transactions on Mobile Computing 2017; 16 (1): 228-242. doi: 10.1109/TMC.2016.2538230
[7] Ward JA, Lukowicz P, Troster G, Starner TE. Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006; 28 (10): 1553-1567. doi: 10.1109/TPAMI.2006.197
[8] Luo X, Guan Q, Tan H, Gao L, Wang Z et al. Simultaneous indoor tracking and activity recognition using pyroelectric infrared sensors. Sensors 2017; 17 (8): 1738. doi: 10.3390/s17081738
[9] Tran DN, Phan DD. Human activities recognition in android smartphone using support vector machine. In: 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS); Bangkok, Thailand; 2016. pp. 64-68. doi:10.1109/ISMS.2016.51
[10] Nam Y, Park JW. Physical activity recognition using a single triaxial accelerometer and a barometric sensor for baby and child care in a home environment. Journal of Ambient Intelligence and Smart Environments 2013; 5 (4): 381-402. doi: 10.3233/AIS-130217
[11] Chen J, Kwong K, Chang D, Luk J, Bajcsy R. Wearable sensors for reliable fall detection. In: 27th Annual International Conference of the IEEE; Shanghai, China; 2006. pp. 3551-3554. doi: 10.1109/IEMBS.2005.1617246
[12] Zheng Y, Wong WK, Guan X, Trost S. Physical activity recognition from accelerometer data using a multi-scale ensemble method. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence; Bellevue, Washington; 2013. pp. 1575-1581.
[13] Garcia-Ceja E, Brena RF, Carrasco-Jimenez JC, Garrido L. Long-term activity recognition from wristwatch accelerometer data. Sensors 2014; 14 (12): 22500-22524. doi:10.3390/s141222500
[14] Bao L, Intille SS. Activity recognition from user-annotated acceleration data. In: International Conference on Pervasive Computing; Berlin, Germany; 2004. pp. 1-17. doi:10.1007/978-3-540-24646-6_1
[15] Reiss A, Stricker D. Introducing a modular activity monitoring system. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Boston, MA, USA; 2011. pp. 5621-5624. doi:10.1109/IEMBS.2011.6091360
[16] Bonomi AG, Goris A, Yin B, Westerterp KR. Detection of type, duration, and intensity of physical activity using an accelerometer. Medical Science Sports Exercise 2009; 41 (9): 1770-1777. doi: 10.1249/MSS.0b013e3181a24536
[17] Muhammad SA, Klein BN, Van Laerhoven K, David K. A feature set evaluation for activity recognition with body-worn inertial sensors. In: International Joint Conference on Ambient Intelligence; Berlin, Germany; 2011. pp. 101-109. doi: 10.1007/978-3-642-31479-7_17
[18] Ravi N, Dandekar N, Mysore P, Littman ML. Activity recognition from accelerometer data. In: Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence; Pittsburgh, PA, USA; 2005. pp. 1541-1546.
[19] Sánchez D, Tentori M, Favela J. Activity recognition for the smart hospital. IEEE Intelligent Systems. 2008; 23 (2): 50-57. doi:10.1109/MIS.2008.18
[20] Noury N, Poujaud J, Fleury A, Nocua R, Haddidi T et al. Smart sweet home...A pervasive environment for sensing our daily activity. In: Noury N, Poujaud J, Fleury A, Nocua R, Haddidi T, Rumeau P (editors). Activity Recognition in Pervasive Intelligent Environments. Paris, France: Atlantis Press, 2011, pp. 187-208. doi:10.2991/978-94-91216- 05-3_9
[21] Assaf MH, Mootoo R, Das SR, Petriu EM, Groza V et al. Sensor based home automation and security system. In: IEEE Instrumentation and Measurement Technology Conference (I2MTC); Graz, Austria; 2012. pp. 722-727. doi: 10.1109/I2MTC.2012.6229153
[22] Hong YJ, Kim IJ, Ahn SC, Kim HG. Mobile health monitoring system based on activity recognition using accelerometer. Simulation Modelling Practice and Theory 2010; 18 (4): 446-455. doi: 10.1016/j.simpat.2009.09.002
[23] Lustrek M, Kaluza B. Fall detection and activity recognition with machine learning. Informatica 2009; 33 (2): 197-204.
[24] Ahmed MU, Loutfi A. Physical activity identification using supervised machine learning and based on pulse rate. International Journal of Advanced Computer Sciences and Applications 2013; 4 (7): 210-217.
[25] Hao T, Xing G, Zhou G. iSleep: unobtrusive sleep quality monitoring using smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems; Rome, Italy; 2013. pp. 4. doi:10.1145/2517351.2517359
[26] Fida B, Bernabucci I, Bibbo D, Conforto S, Schmid M. Pre-processing effect on the accuracy of event-based activity segmentation and classification through inertial sensors. Sensors 2015; 15 (9): 23095-23109. doi: 10.3390/s150923095
[27] AlNuaimi N, Masud MM, Mohammed F. Examining the effect of feature selection on improving patient deterioration prediction. International Journal of Data Mining & Knowledge Management Process (IJDKP). 2015; 5: 1-33. doi: 10.5121/ijdkp.2015.5602
[28] Villar JR, González S, Sedano J, Chira C, Trejo JM. Human activity recognition and feature selection for stroke early diagnosis. In: International Conference on Hybrid Artificial Intelligence Systems; Berlin, Germany; 2013. pp. 659-668. doi: 10.1007/978-3-642-40846-5_66
[29] Gupta P, Dallas T. Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Transactions on Biomedical Engineering 2014; 61 (6): 1780-1786. doi: 10.1109/TBME.2014.2307069
[30] Atallah L, Lo B, King R, Yang GZ. Sensor positioning for activity recognition using wearable accelerometers. IEEE Transactions on Biomedical Circuits and Systems 2011; 5 (4): 320-329. doi: 10.1109/TBCAS.2011.2160540
[31] Koza JR. Genetic Programming: On the Programming Of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press, 1997.
[32] Pudil P, Novovičová J, Kittler J. Floating search methods in feature selection. Pattern Recognition Letters 1994; 15 (11): 1119-1125. doi: 10.1016/0167-8655(94)90127-9
[33] Gilad-Bachrach R, Navot A, Tishby N. Margin based feature selection-theory and algorithms. In: Proceedings of the Twenty-First International Conference on Machine learning; Banff, Alberta, Canada; 2004. pp. 43. doi: 10.1145/1015330.1015352
[34] Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005; 27 (8): 1226-1238. doi: 10.1109/TPAMI.2005.159
[35] Kira K, Rendell LA. A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine learning; Scotland, United Kingdom; 1992. pp. 249-256. doi:10.1016/B978-1-55860-247-2.50037-1
[36] Mannini A, Sabatini AM. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 2010; 10 (2): 1154-1175. doi: 10.3390/s100201154
[37] Hall MA, Smith LA. Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In: Proceedings of the Twelfth International Florida Artificial Intelligence; Orlando, FL, USA; 1999. pp. 235-239.
[38] Rashidi P, Cook DJ. Keeping the resident in the loop: adapting the smart home to the user.IEEE Transactions on Systems, Man, and Cybernetics 2009; 39 (5): 949-959. doi: 10.1109/TSMCA.2009.2025137
[39] Cook DJ, Youngblood M, Heierman EO, Gopalratnam K, Rao S et al. MavHome: an agent-based smart home. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications; Fort Worth, TX, USA; 2003. pp. 521-524. doi: 10.1109/PERCOM.2003.1192783
[40] Alemdar H, Ertan H, Incel OD, Ersoy C. ARAS human activity datasets in multiple homes with multiple residents. In: 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops; Venice, Italy; 2013. pp. 232-235. doi: 10.4108/icst.pervasivehealth.2013.252120
[41] Kröse B, Van Kasteren T, Gibson C, Van den Dool T. Care: context awareness in residences for elderly. In: International Conference of the International Society for Gerontechnology; Tuscany, Italy; 2008. pp. 101-105. doi: 10.4017/gt.2008.07.02.083.00
[42] Colon LNV, DeLaHoz Y, Labrador M. Human fall detection with smartphones. In: IEEE Latin-America Conference on Communications (LATINCOM); Cartagena de Indias, Colombia; 2014. pp. 1-7. doi: 10.1109/LATINCOM.2014.7041879
[43] Sahyoun A, Chehab K, Al-Madani O, Aloul F, Sagahyroon A. ParkNosis: diagnosing Parkinson’s disease using mobile phones. In: IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom); Munich, Germany; 2016. pp. 1-6. doi: 10.1109/HealthCom.2016.7749491
[44] Zmily A, Abu-Saymeh D. Alzheimer’s disease rehabilitation using smartphones to improve patient’s quality of life. In: 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops; Venice, Italy; 2013. pp. 393-396. doi: 10.4108/icst.pervasivehealth.2013.252248
[45] Dernbach S, Das B, Krishnan NC, Thomas BL, Cook DJ. Simple and complex activity recognition through smart phones. In: Eighth International Conference on Intelligent Environments; Guanajuato, Mexico; 2012. pp. 214-221. doi: 10.1109/IE.2012.39
[46] Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL. A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN); Bruges, Belgium; 2013. pp. 437–442.
[47] Hanai Y, Nishimura J, Kuroda T. Haar-like filtering for human activity recognition using 3d accelerometer. In: IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop; Marco Island, FL, USA; 2009. pp. 675-678. doi: 10.1109/DSP.2009.4786008.
[48] Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine 2006; 10 (1): 156-167. doi: 10.1109/TITB.2005.856864
[49] Rasekh A, Chen CA, Lu Y. Human activity recognition using smartphone. arXiv preprint arXiv:14018212. 2014.
[50] Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence 1997; 97 (1-2): 273-324. doi: 10.1016/S0004-3702(97)00043-X
[51] Das S. Filters, wrappers and a boosting-based hybrid for feature selection. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001); Williamstown, MA, USA; 2001. pp. 74-81.
[52] Suto J, Oniga S, Sitar PP. Comparison of wrapper and filter feature selection algorithms on human activity recognition. In: 6th International Conference on Computers Communications and Control (ICCCC); Oradea, Romania; 2016. pp. 124-129. doi: 10.1109/ICCCC.2016.7496749
[53] Atla A, Tada R, Sheng V, Singireddy N. Sensitivity of different machine learning algorithms to noise. Journal of Computing Sciences in Colleges 2011; 26 (5): 96-103.
[54] Cawley GC, Talbot NL, Girolami M. Sparse multinomial logistic regression via bayesian l1 regularisation. In: Advances in Neural Information Processing Systems; Vancouver, BC, Canada; 2007. pp. 209-216.
[55] Hussain M, Jibreen A, Aboalsmah H, Madkour H, Bebis G et al. Feature selection for hand-shape based identification. In: Computational Intelligence in Security for Information Systems; Burgos, Spain; 2015. pp. 237-246. doi:10.1007/978-3-319-19713-5_21
[56] Khan S, Khan A, Maqsood M, Aadil F, Ghazanfar MA. Optimized gabor feature extraction for mass classification using cuckoo search for big data e-healthcare. Journal of Grid Computing 2019; 17 (2): 239-254. doi: 10.1007/s10723- 018-9459-x
[57] Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2011; 2 (3): 27.
[58] Murad A, Pyun JY. Deep recurrent neural networks for human activity recognition. Sensors 2017; 17 (11): 2556. doi: 10.3390/s17112556
[59] Ronao CA, Cho SB. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications 2016; 59: 235-244. doi: 10.1016/j.eswa.2016.04.032
[60] Ronao CA, Cho SB. Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. In: 10th International Conference on Natural Computation (ICNC); Xiamen, China; 2014. pp. 681-686. doi: 10.1109/ICNC.2014.6975918
[61] Hernández F, Suárez LF, Villamizar J, Altuve M. Human activity recognition on smartphones using a bidirectional LSTM network. In: XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA); Bucaramanga, Colombia; 2019. pp. 1-5. doi: 10.1109/STSIVA.2019.8730249
[62] Yu S, Qin L. Human activity recognition with smartphone inertial sensors using bidir-LSTM networks. In: 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE); Huhhot, China; 2018. pp. 219-224. doi: 10.1109/ICMCCE.2018.00052
[63] Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and Intelligent. Laboratory Systems 1987; 2 (1-3): 37-52. doi: 10.1016/0169-7439(87)80084-9
[64] Räsänen O, Pohjalainen J. Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech. In: Proceedings of the Annual Conference of the International Speech Communication Association Interspeech; Lyon, France; 2013. pp. 210-214.
[65] Dietterich TG. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 1998; 10 (7): 1895-1923. doi: 10.1162/089976698300017197