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RETRACTED ARTICLE: A novel hidden Markov model-based adaptive dynamic time warping (HMDTW) gait analysis for identifying physically challenged persons

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This article was retracted on 04 September 2024

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Abstract

Internet of things plays vital role in real-time applications, and the research thrust towards implementing IoT in gait analysis increases day by day in order to obtain efficient gait recognition mechanism. IoT in gait analysis is used to monitor and communicate the observing gait, and also to transfer data to others is the current trend which is available. This research work provides an efficient gait recognition system with IoT using dynamic time wrapping and naïve bays classifier as combination to obtain hybrid model. The objective of this research is identifying the patients or persons with walking disabilities in a crowded area and providing suitable alerts to them by monitoring the walking styles. So that the possibility of getting injured is avoided and the information related to the persons also alerted through IoT module. Also, IoT module is used to collect information from the sensors used in persons accessories and other places. Twenty-five males and 10 females are subjected to examine the proposed model in different locations and achieved the overall accuracy percentage of 92.15%.

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References

  • AbdelMaseeh M, Chen TW, Stashuk D (2016) Extraction and classification of multichannel electromyographic activation trajectories for hand movement recognition. IEEE Trans Neural Syst Rehab Eng 26(6):662–673

    Article  Google Scholar 

  • Balazia M, Plataniotis KN (2017) Human gait recognition from motion capture data in signature poses. IET Biomet 6(2):129–137

    Article  Google Scholar 

  • Connor P, Ross A (2018) Biometric recognition by gait: a survey of modalities and features. Comput Vis Image Underst 167:1–27

    Article  Google Scholar 

  • Deng M, Wang C, Chen Q (2016) Human gait recognition based on deterministic learning through multiple views fusion. Pattern Recogn Lett 78:56–63

    Article  Google Scholar 

  • Deng M, Wang C, Cheng F, Zeng W (2017) Fusion of spatial–temporal and kinematic features for gait recognition with deterministic learning. Pattern Recogn 67:186–200

    Article  Google Scholar 

  • Eggleston JD, Harry JR, Hickman RA, Dufek JS (2017) Analysis of gait symmetry during over-ground walking in children with autism spectrum disorder. Gait Posture 55:162–166

    Article  Google Scholar 

  • Elsworth-Edelsten C, Bonnefoy-Mazure A, Laidet M, Armand S, Allali G (2017) Upper limb movement analysis during gait in multiple sclerosis patients. Hum Mov Sci 54:248–252

    Article  Google Scholar 

  • Fisher JM, Hammerla NY, Ploetz T, Andras P, Rochester L, Walker RW (2016) Unsupervised home monitoring of Parkinson’s disease motor symptoms using body-worn accelerometers. Parkinsonism Relat Disord 33:44–50

    Article  Google Scholar 

  • Godfrey A (2017) Wearables for independent living in older adults: gait and falls. Maturitas 100:16–26

    Article  Google Scholar 

  • Kalron A, Frid L, Menascu S (2017) Gait characteristics in adolescents with multiple sclerosis. Pediatr Neurol 68:73–76

    Article  Google Scholar 

  • Kluge F, Gaßner H, Hannink J, Pasluosta C, Klucken J, Eskofier B (2017) Towards mobile gait analysis: concurrent validity and test-retest reliability of an inertial measurement system for the assessment of spatio-temporal gait parameters. Sensors 17(7):1522

    Article  Google Scholar 

  • Liu L, Huai Y (2019) Dynamic hand gesture recognition using LMC for flower and plant interaction. Int J Pattern Recognit Artif Intell 33(01):1950003

    Article  Google Scholar 

  • Ma Y, Amini N, Ghasemzadeh H (2016) Wearable sensors for gait pattern examination in glaucoma patients. Microprocess Microsyst 46:67–74

    Article  Google Scholar 

  • Manogaran G, Thota C, Lopez D, Vijayakumar V, Abbas KM Sundarsekar R (2017) Big data knowledge system in healthcare. In: Bhatt C, Dey N, Ashour A (eds) Internet of things and big data technologies for next generation healthcare. Springer, Cham, pp 133–157

  • Martindale C, Hoenig F, Strohrmann C, Eskofier B (2017) Smart annotation of cyclic data using hierarchical hidden Markov models. Sensors 17(10):2328

    Article  Google Scholar 

  • Pinar AJ, Rice J, Hu L, Anderson DT, Havens TC (2017) Efficient multiple kernel classification using feature and decision level fusion. IEEE Trans Fuzzy Syst 25(6):1403–1416

    Article  Google Scholar 

  • Sun J, Liu Y, Yan S, Cao G, Zhang K (2017) Clinical gait evaluation of patients with knee osteoarthritis. Gait Posture 58:319–324

    Article  Google Scholar 

  • Tadano S, Takeda R, Sasaki K, Fujisawa T, Tohyama H (2016) Gait characterization for osteoarthritis patients using wearable gait sensors (H-gait systems). J Biomech 49(5):684–690

    Article  Google Scholar 

  • Tapus A, Bandera A, Vazquez-Martin R, Calderita LV (2019) Perceiving the person and their interactions with the others for social robotics—a review. Pattern Recogn Lett 118:3–13

    Article  Google Scholar 

  • Thomas KS, Russell DM, van Lunen BL, Colberg SR, Morrison S (2017) The impact of speed and time on gait dynamics. Hum Mov Sci 54:320–330

    Article  Google Scholar 

  • Yang K, Dou Y, Lv S, Zhang F, Lv Q (2016) Relative distance features for gait recognition with kinect. J Vis Commun Image Represent 39:209–217

    Article  Google Scholar 

  • Yang G, Tan W, Jin H, Zhao T, Tu L (2018) Review wearable sensing system for gait recognition. Clust Comput. https://doi.org/10.1007/s10586-018-1830-y

    Article  Google Scholar 

  • Yeoh TW, Daolio F, Aguirre HE, Tanaka K (2017) On the effectiveness of feature selection methods for gait classification under different covariate factors. Appl Soft Comput 61:42–57

    Article  Google Scholar 

  • Yu S, Chen H, Wang Q, Shen L, Huang Y (2017) Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239(1):81–93

    Article  Google Scholar 

  • Zou Q, Ni L, Wang Q, Li Q, Wang S (2018) Robust gait recognition by integrating inertial and RGBD sensors. IEEE Trans Cybern 48(4):1136–1150

    Article  Google Scholar 

Download references

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Correspondence to Sampath Dakshina Murthy Achanta.

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Communicated by Sahul Smys.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-024-10138-x"

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Achanta, S.D.M., Karthikeyan, T. & Vinothkanna, R. RETRACTED ARTICLE: A novel hidden Markov model-based adaptive dynamic time warping (HMDTW) gait analysis for identifying physically challenged persons. Soft Comput 23, 8359–8366 (2019). https://doi.org/10.1007/s00500-019-04108-x

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