Abstract
Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%.
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References
Abdelnasser H, Youssef M, Harras KA (2014) WiGest: a ubiquitous WiFi-based gesture recognition system. In: Proc. IEEE INFOCOM, Piscataway, New Jersey, US
Abdelnasser H, Youssef M, Harras KA (2015) WiGest: a ubiquitous WiFi-based gesture recognition system. In: Proc. IEEE INFOCOM
Alemdar H, Ersoy C (2017) Smart environment architecture for robust people detection by infrared and visible video fusion. J Ambient Intell Human Comput 8:513. https://doi.org/10.1007/s12652-016-0440-x
Azimi I, Rahmani AM, Liljeberg P et al (2017) Internet of things for remote elderly monitoring: a study from user-centered perspective. J Ambient Intell Human Comput 8:273. https://doi.org/10.1007/s12652-016-0387-y
Ben-Arie J, Wang Z, Pandit P, Rajaram S (2002) Human activity recognition using multidimensional indexing. IEEE Trans Pattern Anal Mach Intell 24(8):1091–1104
Ben-Arie J, Wang Z, Pandit P, Rajaram S (2012) Human activity recognition using multidimensional indexing. IEEE Trans Pattern Anal Mach Intell 24(8):1091–1104
Breiman L (2001) Random forests. J Mach Learn 45:5–32
Castillo JC, Fernández-Caballero A, Serrano-Cuerda J et al (2017) Smart environment architecture for robust people detection by infrared and visible video fusion. J Ambient Intell Human Comput 8:223. https://doi.org/10.1007/s12652-016-0429-5
Chen T, Chiu MCJ (2018) Smart technologies for assisting the life quality of persons in a mobile environment: a review. Ambient Intell Human Comput 9:319. https://doi.org/10.1007/s12652-016-0396-x
Dilber D, Kaur J (2016) EEG-based detection of epilepsy by a mixed design approach. In: IEEE international conference on recent trends in electronics, information and communication technology (RTEICT), Bangalore, pp 1425–1428
Fan D et al (2018) Breathing rhythm analysis in body centric networks. IEEE Access 6:32507–32513. https://doi.org/10.1109/ACCESS.2018.2846605
Giorgetti G, Cidronali A, Gupta SKS, Manes G (2007) Exploiting low-cost directional antennas in 2.4 GHz IEEE 802.15.4 wireless sensor networks. In: European conference on wireless technologies, Munich, pp 217–220
Haider D, Ren A, Fan D et al (2018) Utilizing a 5G spectrum for health care to detect the tremors and breathing activity for multiple sclerosis. Trans Emerg Tel Tech 29:e3454. https://doi.org/10.1002/ett.3454
Liu L, Shah SA, Zhao G, Yang X (2018) Respiration symptoms monitoring in body area networks. Appl Sci 8:568
Lotfi A, Langensiepen C, Mahmoud SM et al (2012) Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behavior. J Ambient Intell Human Comput 3:205. https://doi.org/10.1007/s12652-010-0043-x
Pedram MZ, Shamloo A, Alasty A, Ghafar-Zadeh E (2015) MRI-guided epilepsy detection. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Milan, pp 4001–4004
Quek YT, Woo WL, Logenthiran T (2016) DC equipment identification using K-means clustering and kNN classification techniques. In: IEEE region 10 conference (TENCON), Singapore, pp 777–780. https://doi.org/10.1109/TENCON.2016.7848109
Shah SA et al (2016) Posture recognition to prevent bedsores for multiple patients using leaking coaxial cable. IEEE Access 4:8065–8072
Shevchik SA, Saeidi F, Meylan B, Wasmer K (2017) Prediction of failure in lubricated surfaces using acoustic time–frequency features and random forest algorithm. IEEE Trans Ind Inf 13(4):1541–1553. https://doi.org/10.1109/TII.2016.263508
Sigg S, Scholz M, Shi S, Ji Y, Beigl M (2014) RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE Trans Mobile Comput 13(4):907–920
Song B, Kamal AT, Soto C, Ding C, Farrell JA, Roy-Chowdhury AK (2010) Tracking and activity recognition through consensus in distributed camera networks. IEEE Trans Image Process 19(10):2564–2579
Tambe SB, Gajre et al (2018) Cluster-based real-time analysis of mobile healthcare application for prediction of physiological data. J Ambient Intell Human Comput 9:429. https://doi.org/10.1007/s12652-017-0562-9
Tao D, Jin L, Wang Y, Li A (2014) Rank-preserving discriminant analysis for human behavior recognition on wireless sensor networks. IEEE Trans Ind Inf 10(1):813–823
Yang X et al (2018a) Freezing of gait detection considering leaky wave cable. IEEE Trans Antennas Propag. https://doi.org/10.1109/TAP.2018.2878081
Yang X et al (2018b) Detection of essential tremor at the S-band. IEEE J Transl Eng Health Med 6:1–7. https://doi.org/10.1109/JTEHM.2017.2789298(Art no. 2000107)
Yang X et al (2018c) Monitoring of patients suffering from REM sleep behavior disorder. IEEE J Electromagn RF Microwaves Med Biol 2(2):138–143. https://doi.org/10.1109/JERM.2018.2827705
Zhu M et al (2018) Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access 6:4641–4652. https://doi.org/10.1109/ACCESS.2018.2789428
Funding
The work was supported in part by the Fundamental Research Funds for the Central Universities (No. JB180205), International Scientific and Technological Cooperation and Exchange Projects in Shaanxi Province (No. 2017KW-005), and China Postdoctoral Science Foundation Funded Project (No. 2018T111023).
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Shah, S.A., Fan, D., Ren, A. et al. Seizure episodes detection via smart medical sensing system. J Ambient Intell Human Comput 11, 4363–4375 (2020). https://doi.org/10.1007/s12652-018-1142-3
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DOI: https://doi.org/10.1007/s12652-018-1142-3