AUTOMATIC TRACKING ALGORITHMS BASED ON WEARABLE TECHNOLOGY

Li Wang∗,∗∗

References

  1. [1] Hong S J, Thong J Y L, Tam K Y. Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 2017, 1819-1834.
  2. [2] Wang H Y, Wang S H. User acceptance of mobile Internet based on the Unified Theory of Acceptance and Use of Technology: investigating the determinants and gender differences. Social Behavior & Personality An International Journal, 38(38), 2014, 415-426.
  3. [3] Xu X, Thong J Y L, Tam K Y. Winning Back Technology Disadopters: Testing a Technology Readoption Model in the Context of Mobile Internet Services. Social Science Electronic Publishing, 34(1), 2017, 102-140.
  4. [4] S Wan, L Qi, X Xu, C Tong, Z Gu. Deep Learning Models for Real-time Human Activity Recognition with Smartphones, Mobile Networks and Applications, 1–13, 2019.
  5. [5] Sultan N. Reflective thoughts on the potential and challenges of wearable technology for health care provision and medical education. International Journal of Information Management, 35(5), 2015, 521–526.
  6. [6] Lv, Zhihan, AlaaHalawani, ShengzhongFeng, Shafiq Ur Rhman, and Haibo Li. “Touch-less interactive augmented reality game on vision-based wearable device.” Personal and Ubiquitous Computing 19, no. 3–4 (2015): 551–567.
  7. [7] Signorini M G, Fanelli A, Magenes G. Monitoring Fetal Heart Rate during Pregnancy: Contributions from Advanced Signal Processing and Wearable Technology. Comput Math Methods Med, 2014(1), 2014, 707581.
  8. [8] Zambotti M D, Claudatos S, Inkelis S, et al. Evaluation of a Consumer Fitness-Tracking Device to Assess Sleep in Adults: Evaluation of Wearable Technology to Assess Sleep. Chronobiology International, 32(7), 2015, 1024.
  9. [9] Wu J, Li H, Cheng S, et al. The Promising Future of Healthcare Services: When Big Data Analytics Meets Wearable Technology. Information & Management, 53(8), 2016, S0378720616300775.
  10. [10] S Wan, Z Gu, Q Ni. Cognitive computing and wireless communications on the edge for healthcare service robots, Computer Communications. https://doi.org/10.1016/ j.comcom.2019.10.012
  11. [11] Lyons E J, Swartz M C, Lewis Z H, et al. Feasibility and Acceptability of a Wearable Technology Physical Activity Intervention With Telephone Counseling for Mid-Aged and Older Adults: A Randomized Controlled Pilot Trial.JmirMhealthUhealth, 5(3), 2017, e28.
  12. [12] Van U J M T, Tom I, Alan L, et al. A Viewpoint on Wearable Technology-Enabled Measurement of Wellbeing and HealthRelated Quality of Life in Parkinsons Disease. Journal of Parkinsons Disease, 6(2), 2016, 279–287.
  13. [13] Slade Shantz J A, Veillette C J. The application of wearable technology in surgery: ensuring the positive impact of the wearable revolution on surgical patients. Front Surg, 1, 2014, 39.
  14. [14] Yingling L R, Brooks A T, Wallen G R, et al. Community Engagement to Optimize the Use of Web-Based and Wearable Technology in a Cardiovascular Health and Needs Assessment Study: A Mixed Methods Approach. JmirMhealth&Uhealth, 4(2), 2016, e38.
  15. [15] Xu, W., Qu, S., Zhao, L., & Zhang, H. (2020). An Improved Adaptive Sliding Mode Observer for a Middle and High-Speed Rotors Tracking. IEEE transactions on power electronics, 1. https://doi.org/10.1109/TPEL.2020.3000785
  16. [16] Belsi A, Papi E, Mcgregor A H. Impact of wearable technology on psychosocial factors of osteoarthritis management: a qualitative study. Bmj Open, 6(2), 2015, e010064.
  17. [17] Stephenson A, Mcdonough S M, Murphy M H, et al. Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis. International Journal of Behavioral Nutrition & Physical Activity, 14(1), 2017, 105.
  18. [18] Aungst T D, Lewis T L. Potential uses of wearable technology in medicine: lessons learnt from Google Glass. International Journal of Clinical Practice, 69(10), 2015, 1179–1183. 5
  19. [19] Arigo D. Promoting physical activity among women using wearable technology and online social connectivity: a feasibility study. Health Psychology & Behavioral Medicine An Open Access Journal, 3(1), 2015, 391–409.
  20. [20] Riemann R, Wang D Z W, Busch F. Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium. Transportation Research Part C, 58, 2015, 1–12.
  21. [21] Chen H, Lou W. On protecting end-to-end location privacy against local eavesdropper in Wireless Sensor Networks. Pervasive & Mobile Computing, 16, 2015, 36–50.
  22. [22] Kala S M, Reddy M P K, Musham R, et al. Interference mitigation in wireless mesh networks through radio co-location aware conflict graphs. Wireless Networks, 22(2), 2015, 1–24.
  23. [23] Ruben, Lopez, Javier. Exploiting Context-Awareness to Enhance Source-Location Privacy in Wireless Sensor Networks. Computer Journal, 54(10), 2018, 1603–1615.
  24. [24] Jon M, Paul L. Indoor Wireless Localization Using Kalman Filtering in Fingerprinting-based Location Estimation System. International Journal of Software Engineering & Its Applications, 8(1), 2014, 235–246.
  25. [25] Long C, Yan W, Hao W, et al. Non-parametric location estimation in rough wireless environments for wireless sensor network. Sensors & Actuators A Physical, 224, 2015, 57–64.
  26. [26] Liu L, Liu Z, Barrowes B E. Through-Wall Bio-Radiolocation with UWB Impulse Radar: Observation, Simulation and Signal Extraction. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 4(4), 2011, 791–798.
  27. [27] Parada, R., Melia-Segui, J., and Pous, R. 2018. “Anomaly Detection Using Rfid-Based Information Management in an Iot Context,” Journal of Organizational and End User Computing (30:3), pp. 1–23.
  28. [28] Cui L, Wang L, Deng J. RFID technology investment evaluation model for the stochastic joint replenishment and delivery problem. Expert Systems with Applications, 41(4), 2014, 1792– 1805.
  29. [29] Liu, Wenbin; Wang, Qing; Guo, Qi. Automatic Radar Waveform Recognition Based On Neural Network. Mechatronic Systems And Control. 2018. 46(2). pp. 92–96.
  30. [30] Consequently. A Real-Time Location-Based Services System Using WiFi Fingerprinting Algorithm for Safety Risk Assessment of Workers in Tunnels. Mathematical Problems in Engineering, 2014(4), 2014, 1–10.
  31. [31] Nguyen, VanHan, Pyun, et al. Sensors, Vol. 15, Pages 6740– 6762: Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System. Sensors, 15(3), 2015, 6740–6762.
  32. [32] Wang, Q., Li, Y., & Liu, X. (2018) “Analysis Of Feature Fatigue EEG Signals Based On Wavelet Entropy”, International Journal of Pattern Recognition and Artificial Intelligence, 32(08), 1854023.
  33. [33] Tang, De-zhi; Wang, Jian-hong. Optimal Closed-Loop Input Signal For Internal Model Control. Mechatronic Systems And Control. 2019. 47(3). pp. 122–128.
  34. [34] Wang L , Zhang L , Wang J , et al. Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural Networks. IEEE Transactions on Cognitive and Developmental Systems, 2020, 12(1):98–108.
  35. [35] Zhao Chunhui, Liu Haiyan. Aircraft target tracking algorithm in remote sensing satellite video. Journal of Shenyang University: Natural Science Edition, 2019, 031(004): 284–290.
  36. [36] Wang Zhengning, Zhou Yang, LV Xia, et al. An improved MDP tracking algorithm based on 2D and 3D joint information. Computer science, 2019, 46(03): 103–108.
  37. [37] Zhang Hongying, Wang Sainan, Hu Wenbo. MS tracking algorithm based on block color histogram. Journal of Civil Aviation University of China, 2019, 37(01): 41–47.
  38. [38] Yu Dexin, Wang Wenqiang, Cao Xiaojie. Automatic detection and tracking algorithm based on inter frame difference and spatiotemporal context. Software guide, 2019, 18(01): 91–94.
  39. [39] Wang, Qing; Teng, Liping; Zhao, Shuang. A Contextual AwarenessLearning Approach To Multi-Objective Mobility Management In 5G Ultra-Dense Network. Mechatronic Systems And Control. 2018. 46(2). 82–91.
  40. [40] Wang, Qing; Gao, Lirong; Yang, Yaotong; Zhao, Jianjun; Dou, Tongdong; Fang, Haoyu. A Load-Balanced Algorithm For Multi-Controller Placement In Software-Defined Network. Mechatronic Systems And Control. 2018. 46(2). 72–81.
  41. [41] Zhou, Zhaihe; Fu, Jiajie; Lv, Jianxin; Zhang, Qianyun. An Improved Complementary Filter Algorithm In The Application Of The Mobile Robot Attitude Estimation. Mechatronic Systems And Control. 2018. 46(4). 163–169.

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