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International Journal of Multimedia Computing, 2020, 1(2); doi: 10.38007/IJMC.2020.010202.

Dance Movement Interference Suppression Algorithm Based on Wearable Sensors in a Smart Environment


Junpei Zhong

Corresponding Author:
Junpei Zhong

Nottingham Trent University, Britain


Wearable device (Wearable device) is a hardware device that can be installed on clothes, clothing accessories or directly worn on the body for easy carrying. It can collect a large amount of user data and behavior habits in real time, and complete some by using software information technology. This paper aims to study the dance movement interference suppression algorithm based on wearable sensors in a smart environment. This paper proposes a method of human action recognition based on wearable sensors, using the original motion capture data collected by the wearable sensors to perform three-dimensional reconstruction, which shows that the basic link of constructing action words is the extraction of key gestures. In addition, this article also proposed an interference suppression algorithm, introduced wavelet noise reduction processing, calculated wavelet coefficients, noisy signals and other basic information, which proved the effectiveness of the interference suppression algorithm. The experimental results of this article show that the wearable sensor dance movement recognition in a smart environment has been greatly improved compared with the traditional dance movement recognition. Among them, the recognition rate of dance movements has increased by 25%, and the error of dance movements has become smaller and smaller. The results show that the interference suppression algorithm has obvious effects on the dance movements of the wearable sensor.


Internet of Things, Smart Environment, Wearable Sensors, Dance Moves, Interference Suppression Algorithms

Cite This Paper

Junpei Zhong. Dance Movement Interference Suppression Algorithm Based on Wearable Sensors in a Smart Environment. International Journal of Multimedia Computing (2020), Vol. 1, Issue 2: 13-28. https://doi.org/10.38007/IJMC.2020.010202.


[1] Li G, Liu T, Yi J, et al. The Lower Limbs Kinematics Analysis by Wearable Sensor Shoes. IEEE Sensors Journal, 2016, 16(8):2627-2638. https://doi.org/10.1109/JSEN.2016.2515101

[2] Perera C, Liu C H, Jayawardena S. The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey. IEEE Transactions on Emerging Topics in Computing, 2017, 3(4):585-598. https://doi.org/10.1109/TETC.2015.2390034

[3] Honig M L, Goldstein J S. Adaptive reduced-rank interference suppression based on the multistage Wiener filter. Communications IEEE Transactions on, 2016, 50(6):986-994. https://doi.org/10.1109/TCOMM.2002.1010618

[4] Caccami M C, Mulla M Y S, Di Natale C, et al. Graphene Oxide-based Radiofrequency Identification Wearable Sensor for Breath Monitoring. IET Microwaves Antennas & Propagation, 2018, 12(4):467-471. https://doi.org/10.1049/iet-map.2017.0628

[5] Michele Caldara, Claudio Colleoni, Emanuela Guido. Optical monitoring of sweat pH by a textile fabric wearable sensor based on covalently bonded litmus-3-glycidoxypropyltrimethoxysilane coating. Sensors and Actuators B Chemical, 2016, 222(JAN.):213-220. https://doi.org/10.1016/j.snb.2015.08.073

[6] Siegmund G P, Guskiewicz K M, Marshall S W, et al. Laboratory Validation of Two Wearable Sensor Systems for Measuring Head Impact Severity in Football Players. Annals of Biomedical Engineering, 2016, 44(4):1257-1274. https://doi.org/10.1007/s10439-015-1420-6

[7] Abosreea S M, Zekry A, Youssef K Y, et al. An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System. International Journal of Electronics and Telecommunications, 2020, 66(2):267-272.

[8] Bini S A, Shah R F, Bendich I, et al. Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial. The Journal of Arthroplasty, 2019, 34(10):2242-2247. https://doi.org/10.1016/j.arth.2019.07.024

[9] Rymarczyk T. Wearable sensor for biopotential measurements of patients' health monitoring. Przeglad Elektrotechniczny, 2020, 1(9):101-104. https://doi.org/10.15199/48.2020.09.21

[10] Mostafa H, Kerstin T, Regina S. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthcare Informatics Research, 2017, 23(1):4-15. https://doi.org/10.4258/hir.2017.23.1.4

[11] Mishra D, Gunasekaran A, Childe S J, et al. Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature. Industrial Management & Data Systems, 2017, 116(7):1331-1355. https://doi.org/10.1108/IMDS-11-2015-0478

[12] Lin J, Yu W, Zhang N, et al. A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 2017, 4(5):1125-1142. https://doi.org/10.1109/JIOT.2017.2683200

[13] Singh J, Pasquier T, Bacon J, et al. Twenty Security Considerations for Cloud-Supported Internet of Things. IEEE Internet of Things Journal, 2017, 3(3):269-284. https://doi.org/10.1109/JIOT.2015.2460333

[14] Akpakwu G, Silva B, Hancke G P, et al. A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges. IEEE Access, 2018, 5(12):3619-3647. https://doi.org/10.1109/ACCESS.2017.2779844

[15] Yang Y, Wu L, Yin G, et al. A Survey on Security and Privacy Issues in Internet-of-Things. Internet of Things Journal, IEEE, 2017, 4(5):1250-1258. https://doi.org/10.1109/JIOT.2017.2694844

[16] Mosenia A, Jha N K. A Comprehensive Study of Security of Internet-of-Things. IEEE Transactions on Emerging Topics in Computing, 2017, 5(4):586-602. https://doi.org/10.1109/TETC.2016.2606384

[17] Razzaque M A, Milojevic-Jevric M, Palade A, et al. Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal, 2017, 3(1):70-95. https://doi.org/10.1109/JIOT.2015.2498900

[18] Rahmani A M, Gia T N, Negash B, et al. Exploiting Smart E-Health Gateways at the Edge of Healthcare Internet-of-Things: A Fog Computing Approach. Future Generation Computer Systems, 2017, 78(PT.2):641-658. https://doi.org/10.1016/j.future.2017.02.014

[19] Zhang Y, Wen J. The IoT electric business model: Using blockchain technology for the internet of things. Peer-to-Peer Networking and Applications, 2017, 10(4):983-994. https://doi.org/10.1007/s12083-016-0456-1

[20] Longchao Z, Jianjun X, Limei Y. Research on congestion elimination method of circuit overload and transmission congestion in the internet of things. Multimedia Tools and Applications, 2017, 76(17):18047-18066. https://doi.org/10.1007/s11042-016-3686-6

[21] Alrawais A, Alhothaily A, Hu C, et al. Fog Computing for the Internet of Things: Security and Privacy Issues. IEEE Internet Computing, 2017, 21(2):34-42. https://doi.org/10.1109/MIC.2017.37

[22] Tian Z, Wen B, Jin L, et al. Radio Frequency Interference Suppression Algorithm in Spatial Domain for Compact High-Frequency Radar. IEEE Geoence and Remote Sensing Letters, 2017, 15(99):102-106. https://doi.org/10.1109/LGRS.2017.2775609

[23] Qian J, He Z. Mainlobe interference suppression with eigenprojection algorithm and similarity constraints. Electronics Letters, 2016, 52(3):228-230. https://doi.org/10.1049/el.2015.2951

[24] Chen F, Nie J, Ni S, et al. Combined algorithm for interference suppression and signal acquisition in GNSS receivers. Electronics Letters, 2017, 53(4):274-275. https://doi.org/10.1049/el.2016.4297

[25] Xiong H. An Efficient Narrowband Interference Suppression Approach in Ultra-Wideband Receiver. IEEE Sensors Journal, 2017, 17(9):2741-2748. https://doi.org/10.1109/JSEN.2017.2676012

[26] WU, Tao, JING, et al. Exploration of Multiple Access Interference Suppression Based on Multi-user Detection. Chinese Journal of Electronics, 2019, v.28(04):173-178. https://doi.org/10.1049/cje.2019.05.011

[27] Li C, Jiang Y, Zhang N, et al. Improved generalized sidelobe cancelation algorithm combined with signal preprocessing about interference suppression in ELF communication. Xian Dianzi Keji Daxue Xuebao/journal of Xidian University, 2019, 46(1):98-105. https://doi.org/10.1080/09205071.2019.1614484

[28] Wu J, Yang S, Lu W, et al. Iterative modified threshold method based on EMD for interference suppression in FMCW radars. IET Radar, Sonar & Navigation, 2020, 14(8):1219-1228. https://doi.org/10.1049/iet-rsn.2020.0092

[29] Feng L, Zhe Z, Di Y. Dynamic mainlobe interference suppression method based on monopulse with gray model Kalman filter. The Journal of Engineering, 2019, 2019(20):6703-6707. https://doi.org/10.1049/joe.2019.0137

[30] Tian Z, Wen B, Jin L, et al. Radio Frequency Interference Suppression Algorithm in Spatial Domain for Compact High-Frequency Radar. IEEE Geoscience and Remote Sensing Letters, 2018, 15(1):102-106. https://doi.org/10.1109/LGRS.2017.2775609