Unveiling Patterns and Abnormalities of Human Gait: A Comprehensive Study
DOI:
https://doi.org/10.51983/ijiss-2024.14.1.3754Keywords:
Gait Analysis, Approaches, Parameter, Types, Applications, Limitation, Meta-AnalysisAbstract
Varieties of serious mental and physical disorders are the cause of variations in gait. Gait analysis is extensively used in a variety of clinical applications to diagnose and monitor specific disorders. Sports, physical rehabilitation, clinical evaluation, surveillance, identification, modeling, and other industries all benefit from gait analysis. The study provides extensive information on characteristics, types, methodologies, limitations, applications, datasets, and tools used in gait analysis employing different sensor-based and vision-based approaches. A thorough study on gait analysis indicates a significant research gap in various elements of vision-based gait analysis. The field is either undiscovered or has received minimal attention in various scenarios, thus requiring emphasis on comprehensive analysis and exploration. This study will help analyze human walking patterns concerning clinical applications, rehabilitation, injury assessment, and fall risk assessment. It can provide important insights into various aspects of a person’s gait.
References
Agosti, V., Vitale, C., Avella, D., Rucco, R., Santangelo, G., Sorrentino, P., Varriale, P., & Sorrentino, G. (2016). Effects of Global Postural Reeducation on gait kinematics in Parkinsonian patients: A pilot randomized three-dimensional motion analysis study. Neurological Sciences, 37(4), 515-522.
Ahmad, M., Khan, A. M., Mazzara, M., Distefano, S., Ali, A., & Tufail, A. (2019, April). Extended sammon-projection and wavelet kernel extreme learning machine for gait-based legitimate user identification. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 1216-1219). ACM.
Arai, K., & Asmara, R. A. (2013). 3D Skeleton model derived from Kinect Depth Sensor Camera and its application to walking style quality evaluations. International Journal of Advanced Research in Artificial Intelligence, 2(7), 24-28.
Armand, S., Watelain, E., Roux, E., Mercier, M., & Lepoutre, F. X. (2007). Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait & Posture, 25(3), 475-484.
Arya, V., Mishra, A. K., & González-Briones, A. (2023). Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 507-518. https://doi.org/10.14201/adcaij.27349.
Attias, M., Bonnefoy-Mazure, A., Lempereur, M., Lascombes, P., De Coulon, G., & Armand, S. (2015). Trunk movements during gait in cerebral palsy. Clinical Biomechanics, 30(1), 28-32.
Avvenuti, M., Carbonaro, N., Cimino, M., Cola, G., Tognetti, A., & Vaglini, G. (2018). Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases. Sensors, 18(11), 3811.
Bensoussan, L., Mesure, S., Viton, J. M., & Delarque, A. (2006). Kinematic and kinetic asymmetries in hemiplegic patients' gait initiation patterns. Journal of Rehabilitation Medicine, 38(5), 287–294.
Blaya, J. A., & Herr, H. (2004). Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(1), 24-31.
Boulgouris, N. V., & Chi, Z. X. (2007). Gait recognition using Radon transform and linear discriminant analysis. IEEE Transactions on Image Processing, 16(3), 731-740.
Bovi, G., Rabuffetti, M., Mazzoleni, P., & Ferrarin, M. (2011). A multiple-task gait analysis approach: kinematic, kinetic and EMG reference data for healthy young and adult subjects. Gait & Posture, 33(1), 6-13.
Cai, X., Han, G., Song, X., & Wang, J. (2019). Gait symmetry measurement method based on a single camera. International Journal of Machine Learning and Cybernetics, 10(6), 1399-1406.
Cha, Y. J., Kim, J. D., Choi, Y. R., Kim, N. H., & Son, S. M. (2018). Effects of gait training with auditory feedback on walking and balancing ability in adults after hemiplegic stroke: a preliminary, randomized, controlled study. International Journal of Rehabilitation Research, 41(3), 239–243.
Chan, H., Zheng, H., Wang, H., Sterritt, R., & Newell, D. (2013, July). Smart mobile phone-based gait assessment of patients with low back pain. In 2013 Ninth International Conference on Natural Computation (ICNC) (pp. 1062-1066). IEEE.
Chanyal, H., Yadav, R. K., & Saini, D. K. J. (2022). Classification of Medicinal Plants Leaves Using Deep Learning Technique: A Review. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 78–87.
Chini, G., Ranavolo, A., Draicchio, F., Casali, C., Conte, C., Martino, G., ... & Serrao, M. (2017). Local stability of the trunk in patients with degenerative cerebellar ataxia during walking. The Cerebellum, 16(1), 26-33.
Condliffe, E. G., Jeffery, D. T., Emery, D. J., & Gorassini, M. A. (2016). Spinal inhibition and motor function in adults with spastic cerebral palsy. The Journal of Physiology, 594(10), 2691-2705.
Conte, C., Pierelli, F., Casali, C., Ranavolo, A., Draicchio, F., Martino, G., ... & Serrao, M. (2014). Upper body kinematics in patients with cerebellar ataxia. The Cerebellum, 13(6), 689-697.
Corchado, J. M., Bajo, J., De Paz, Y., & Tapia, D. I. (2008). Intelligent environment for monitoring Alzheimer patients, agent technology for healthcare. Decision Support Systems, 44(2), 382–396.
Corchado, J. M., Pavón, J., Corchado, E. S., & Castillo, L. F. (2004). Development of CBR-BDI agents: a tourist guide application. In Advances in case-based reasoning (pp. 547–559). Springer.
Coste, C. A., Sijobert, B., Froger, J., & Fattal, C. (2015). Preliminary developments towards closed-loop FES-assistance of posture and gait. IFAC-PapersOnLine, 48(20), 333-337.
Cutti, A. G., Ferrari, A., Garofalo, P., Raggi, M., Cappello, A., & Ferrari, A. (2010). 'Outwalk': A protocol for clinical gait analysis based on inertial and magnetic sensors. Medical & Biological Engineering & Computing, 48(1), 17.
Dang, Q. K., Seo, H. G., Pham, D. D., & Chee, Y. (2019). Wearable Sensor Based Stooped Posture Estimation in Simulated Parkinson's Disease Gaits. Sensors, 19(2), 223.
Dehzangi, O., Taherisadr, M., & Changal Vala, R. (2017). IMU-based gait recognition using convolutional neural networks and multi-sensor fusion. Sensors, 17(12), 2735.
Despard, J., Ternes, A. M., Dimech-Betancourt, B., Poudel, G., Churchyard, A., & Georgiou-Karistianis, N. (2015). Characterising upper limb movements in Huntington's disease and the impact of restricted visual cues. PloS One, 10(8), e0133709.
DiLiberto, F. E., Nawoczenski, D. A., & Houck, J. (2018). Ankle and midfoot power during walking and stair ascent in healthy adults. Journal of Applied Biomechanics, 34(4), 262-269.
DiLiberto, F. E., Tome, J., Baumhauer, J. F., Quinn, J. R., Houck, J., & Nawoczenski, D. A. (2015). Multi-joint foot kinetics during walking in people with Diabetes Mellitus and peripheral neuropathy. Journal of Biomechanics, 48(13), 3679-3684.
Dingwell, J. B., Bohnsack-McLagan, N. K., & Cusumano, J. P. (2018). Humans control stride-to-stride stepping movements differently for walking and running, independent of speed. Journal of Biomechanics, 76, 144-151.
Dingwell, J. B., Salinas, M. M., & Cusumano, J. P. (2017). Increased gait variability may not imply impaired stride-to-stride control of walking in healthy older adults: Winner: 2013 Gait and Clinical Movement Analysis Society Best-Paper Award. Gait & Posture, 55, 131-137.
Eek, M. N., Tranberg, R., & Beckung, E. (2011). Muscle strength and kinetic gait pattern in children with bilateral spastic CP. Gait & Posture, 33(3), 333-337.
Farina, N., Sherlock, G., Thomas, S., Lowry, R. G., & Banerjee, S. (2019). Acceptability and feasibility of wearing activity monitors in community-dwelling older adults with dementia. International Journal of Geriatric Psychiatry, 34(4), 617-624.
Frenkel-Toledo, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., & Hausdorff, J. M. (2005). Effect of gait speed on gait rhythmicity in Parkinson's disease: Variability of stride time and swing time respond differently. Journal of Neuroengineering and Rehabilitation, 2(1), 1.
Fu, C., & Chen, K. (2008). Gait synthesis and sensory control of stair climbing for a humanoid robot. IEEE Transactions on Industrial Electronics, 55(5), 2111-2120.
Fuhrer, M. (2014). Disability inclusive disaster risk reduction. PlanetRisk, 2(3), 1–35.
Gaba, I., & Ahuja, S. P. (2014). Gait analysis for identification by using BPNN with LDA and MDA techniques. In 2014 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE) (pp. 122-127). IEEE.
Gibson, R. M., Amira, A., Ramzan, N., Casaseca-de-la-Higuera, P., & Pervez, Z. (2017). Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomedical Signal Processing and Control, 33, 96-108.
Gil, C. R., Calvo, H., & Sossa, H. (2019). Learning an efficient gait cycle of a biped robot based on reinforcement learning and artificial neural networks. Applied Sciences, 9(3), 502.
Gomes, A. A., Onodera, A. N., Otuzi, M. E., Pripas, D., Mezzarane, R. A., & Sacco, I. C. (2011). Electromyography and kinematic changes of gait cycle at different cadences in diabetic neuropathic individuals. Muscle & Nerve, 44(2), 258-268.
Grabiner, M. D., Marone, J. R., Wyatt, M., Sessoms, P., & Kaufman, K. R. (2018). Performance of an attention-demanding task during treadmill walking shifts the noise qualities of step-to-step variation in step width. Gait & Posture, 63, 154-158.
Guimarães, I. J. A., Lopes, R. M., Junior, J. F. L. S., Sousa, B. S., Marães, V. R. F. S., & Brasil, L. M. (2019). Predicting Knee Angles from Video: An Initial Experiment with Machine Learning. In XXVI Brazilian Congress on Biomedical Engineering (pp. 375-379). Springer, Singapore.
Gupta, K., Khajuria, A., Chatterjee, N., Joshi, P., & Joshi, D. (2019). Rule-based classification of neurodegenerative diseases using data-driven gait features. Health and Technology, 9(4), 547-560.
Hansen, M., Haugland, M. K., & Sinkjær, T. (2004). Evaluating robustness of gait event detection based on machine learning and natural sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(1), 81-88.
Harris-Love, M., Benson, K., Leasure, E., Adams, B., & McIntosh, V. (2018). The Influence of Upper and Lower Extremity Strength on Performance-Based Sarcopenia Assessment Tests. Journal of Functional Morphology and Kinesiology, 3(4), 53.
He, J., Lippmann, K., Shakoor, N., Ferrigno, C., & Wimmer, M. A. (2019). Unsupervised gait retraining using a wireless pressure-detecting shoe insole. Gait & Posture, 70, 408-413.
Heyrman, L., Feys, H., Molenaers, G., Jaspers, E., Monari, D., Meyns, P., & Desloovere, K. (2013). Three-dimensional head and trunk movement characteristics during gait in children with spastic diplegia. Gait & Posture, 38(4), 770-776.
Heyrman, L., Feys, H., Molenaers, G., Jaspers, E., Monari, D., Nieuwenhuys, A., & Desloovere, K. (2014). Altered trunk movements during gait in children with spastic diplegia: Compensatory or underlying trunk control deficit? Research in Developmental Disabilities, 35(9), 2044-2052.
Höhne, A., Ali, S., Stark, C., & Brüggemann, G. P. (2012). Reduced plantar cutaneous sensation modifies gait dynamics, lower-limb kinematics and muscle activity during walking. European Journal of Applied Physiology, 112(11), 3829-3838.
Howell, A. M., Kobayashi, T., Hayes, H. A., Foreman, K. B., & Bamberg, S. J. M. (2013). Kinetic gait analysis using a low-cost insole. IEEE Transactions on Biomedical Engineering, 60(12), 3284-3290.
Hsu, W. C., Sugiarto, T., Lin, Y. J., Yang, F. C., Lin, Z. Y., Sun, C. T., ... Chou, K. N. (2018). Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders. Sensors, 18(10), 3397.
Hu, K., Wang, Z., Mei, S., Ehgoetz, K., Yao, T., Lewis, S., & Feng, D. (2019). Vision-based freezing of gait detection with anatomical directed graph representation. IEEE Journal of Biomedical and Health Informatics.
Hu, W., Tan, T., Wang, L., & Maybank, S. (2004). A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(3), 334-352.
Huang, C. H., & Foucher, K. C. (2019). Step Length Asymmetry and its Associations with Mechanical Energy Exchange, Function, and Fatigue after Total Hip Replacement. Journal of Orthopaedic Research®, 37(9), 1937-1947.
Huang, R., Cheng, H., Qiu, J., & Zhang, J. (2019). Learning Physical Human-Robot Interaction with Coupled-Cooperative Primitives for a Lower Exoskeleton. IEEE Transactions on Automation Science and Engineering.
Jagadamma, K. C., Owen, E., Coutts, F. J., Herman, J., Yirrell, J., Mercer, T. H., & Van Der Linden, M. L. (2010). The effects of tuning an ankle-foot orthosis-footwear combination on kinematics and kinetics of the knee joint of an adult with hemiplegia. Prosthetics and Orthotics International, 34(3), 270–276.
Jones, B.A., & Walker, I.D. (2006). Kinematics formulation continuum robots. IEEE Transactions on Robotics, 22(1), 43-55.
Jørgensen, A.N., Aagaard, P., Nielsen, J.L., Christiansen, M., Hvid, L.G., Frandsen, U., & Diederichsen, L.P. (2017). Physical function and muscle strength in sporadic inclusion body myositis. Muscle & Nerve, 56(6), E50-E58.
Kamruzzaman, J., & Begg, R.K. (2006). Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait. IEEE Transactions on Biomedical Engineering, 53(12), 2479-2490.
KhanVardag, M.H., Saeed, A., Hayat, U., FarhatUllah, M., & Hussain, N. (2023). Contextual Urdu Text Emotion Detection Corpus and Experiments using Deep Learning Approaches. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 489-505. https://doi.org/10.14201/adcaij.30128
Kim, K.J., Agrawal, V., Bennett, C., Gaunaurd, I., Feigenbaum, L., & Gailey, R. (2018). Measurement of lower limb segmental excursion using inertial sensors during single limb stance. Journal of Biomechanics, 71, 151-158.
Kim, K.J., Gimmon, Y., Millar, J., & Schubert, M.C. (2019). Using Inertial Sensors to Quantify Postural Sway and Gait Performance during the Tandem Walking Test. Sensors, 19(4), 751.
Kim, Y., Lee, K.M., & Koo, S. (2018). Joint moments and contact forces in the foot during walking. Journal of Biomechanics, 74, 79-85.
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.
Ko, M., Bishop, M.D., & Behrman, A.L. (2011). Effects of limb loading on gait initiation in persons with moderate hemiparesis. Topics in Stroke Rehabilitation, 18(3), 258–268.
Köktaş, N.Ş., Yalabik, N., Yavuzer, G., & Duin, R.P. (2010). A multi-classifier for grading knee osteoarthritis using gait analysis. Pattern Recognition Letters, 31(9), 898-904.
Kormushev, P., Ugurlu, B., Caldwell, D.G., & Tsagarakis, N.G. (2019). Learning to exploit passive-compliance for energy-efficient gait generation on a compliant humanoid. Autonomous Robots, 43(1), 79-95.
Kovač, J., Štruc, V., & Peer, P. (2019). Frame–based classification for cross-speed gait recognition. Multimedia Tools and Applications, 78(5), 5621-5643.
Kunju, N., Kumar, N., Pankaj, D., Dhawan, A., & Kumar, A. (2009). EMG signal analysis for identifying walking patterns of normal healthy individuals. Indian Journal of Biomechanics, 1, 118–122.
Lai, D.T., Begg, R.K., & Palaniswami, M. (2009). Computational intelligence in gait research: a perspective on current applications and future challenges. IEEE Transactions on Information Technology in Biomedicine, 13(5), 687–702.
Lai, D.T., Levinger, P., Begg, R.K., Gilleard, W.L., & Palaniswami, M. (2009). Automatic recognition of gait patterns exhibiting patellofemoral pain syndrome using a support vector machine approach. IEEE Transactions on Information Technology in Biomedicine, 13(5), 810-817.
Lee, T.K., Belkhatir, M., & Sanei, S. (2014). A comprehensive review of past and present vision-based techniques for gait recognition. Multimedia Tools and Applications, 72(3), 2833-2869.
Little, J., & Boyd, J. (1998). Recognizing people by their gait: The shape of motion. Videre: Journal of Computer Vision Research, 1(2), 1-32.
Long, J.T., Klein, J.P., Sirota, N.M., Wertsch, J.J., Janisse, D., & Harris, G.F. (2007). Biomechanics of the double rocker sole shoe: Gait kinematics and kinetics. Journal of Biomechanics, 40(13), 2882-2890.
Lu, J., Wang, G., & Moulin, P. (2014). Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Transactions on Information Forensics and Security, 9(1), 51-61.
Mannini, A., & Sabatini, A.M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10(2), 1154-1175.
Mari, S., Serrao, M., Casali, C., Conte, C., Martino, G., Ranavolo, A., Coppola, G., Draicchio, F., Padua, L., Sandrini, G., & Pierelli, F. (2014). Lower limb antagonist muscle co-activation and its relationship with gait parameters in cerebellar ataxia. The Cerebellum, 13(2), 226-236.
Martinez-Hernandez, U., & Dehghani-Sanij, A.A. (2019). Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. Pattern Recognition Letters, 118, 32-41.
Martínez, M., Villagra, F., Castellote, J., & Pastor, M. (2018). Kinematic and kinetic patterns related to free-walking in Parkinson's disease. Sensors, 18(12), 4224.
Martino, G., Ivanenko, Y.P., Serrao, M., Ranavolo, A., d'Avella, A., Draicchio, F., Conte, C., Casali, C., & Lacquaniti, F. (2014). Locomotor patterns in cerebellar ataxia. Journal of Neurophysiology, 112(11), 2810-2821.
Matić, T., Aghanavesi, S., Memedi, M., Nyholm, D., Bergquist, F., Groznik, V., Žabkar, J., & Sadikov, A. (2019, June). Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease. In Conference on Artificial Intelligence in Medicine in Europe (pp. 420-424). Springer, Cham.
Mirek, E., Filip, M., Chwała, W., Banaszkiewicz, K., Rudzinska-Bar, M., Szymura, J., Pasiut, S., & Szczudlik, A. (2017). Three-Dimensional Trunk and Lower Limbs Characteristics during Gait in Patients with Huntington's Disease. Frontiers in Neuroscience, 11, 566.
Moeslund, T.B., & Granum, E. (2001). A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 81(3), 231-268.
Moeslund, T.B., Hilton, A., & Krüger, V. (2006). A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding, 104(2), 90-126.
Molazadeh, V., Sheng, Z., Bao, X., & Sharma, N. (2019). A Robust Iterative Learning Switching Controller for following Virtual Constraints: Application to a Hybrid Neuroprosthesis. IFAC-PapersOnLine, 51(34), 28-33.
Molloy, M., McDowell, B.C., Kerr, C., & Cosgrove, A.P. (2010). Further evidence of validity of the Gait Deviation Index. Gait & Posture, 31(4), 479–482.
Montanini, L., Del Campo, A., Perla, D., Spinsante, S., & Gambi, E. (2017). A footwear-based methodology for fall detection. IEEE Sensors Journal, 18(3), 1233-1242.
Muro-de la Herran, A., Garcia-Zapirain, B., & Mendez-Zorrilla, A. (2014). Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors, 14(2), 3362–3394.
Muybridge, E., & Mozley, A.V. (1887). Muybridge’s complete human and animal locomotion: all 781 plates-from the 1887 animal locomotion.
Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., & Yagi, Y. (2014). The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognition, 47(1), 228-237.
Nguyen, T.N., Huynh, H.H., & Meunier, J. (2014, December). Extracting silhouette-based characteristics for human gait analysis using one camera. In Proceedings of the Fifth Symposium on Information and Communication Technology (pp. 171-177). ACM.
Nieto-Hidalgo, M., & García-Chamizo, J.M. (2017, November). Classification of Pathologies Using a Vision Based Feature Extraction. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 265-274). Springer, Cham.
Nutt, J., Marsden, C., & Thompson, P. (1993). Human walking and higher-level gait disorders, particularly in the elderly. Neurology, 43(2), 268–268.
Ortells, J., Herrero-Ezquerro, M.T., & Mollineda, R.A. (2018). Vision-based gait impairment analysis for aided diagnosis. Medical & Biological Engineering & Computing, 56(9), 1553-1564.
Patikas, D., Wolf, S., & Döderlein, L. (2005). Electromyographic evaluation of the sound and involved side during gait of spastic hemiplegic children with cerebral palsy. European Journal of Neurology, 12(9), 691–699.
Pereira, J.D., Postolache, O., Viegas, V., & Girão, P.S. (2015, May). A low-cost measurement system to extract kinematic parameters from walker devices. In 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings (pp. 1991-1996). IEEE.
Perry, J., Davids, J.R., et al. (1992). Gait analysis: normal and pathological function. Journal of Pediatric Orthopedics, 12(6), 815.
Phinyomark, A., Hettinga, B.A., Osis, S.T., & Ferber, R. (2014). Gender and age-related differences in bilateral lower extremity mechanics during treadmill running. PLOS One, 9(8), e105246.
Piccinini, L., Cimolin, V., D'Angelo, M.G., Turconi, A.C., Crivellini, M., & Galli, M. (2011). 3D gait analysis in patients with hereditary spastic paraparesis and spastic diplegia: a kinematic, kinetic and EMG comparison. European Journal of Paediatric Neurology, 15(2), 138-145.
Pogorelc, B., Bosnić, Z., & Gams, M. (2012). Automatic recognition of gait-related health problems in the elderly using machine learning. Multimedia Tools and Applications, 58(2), 333-354.
Poppe, R. (2007). Vision-based human motion analysis: An overview. Computer Vision and Image Understanding, 108(1), 4-18.
Prakash, C., Gupta, K., Mittal, A., Kumar, R., & Laxmi, V. (2015). Passive marker-based optical system for gait kinematics for lower extremity. Procedia Computer Science, 45, 176–185.
Qiu, S., Liu, L., Wang, Z., Li, S., Zhao, H., Wang, J., Li, J., & Tang, K. (2019). Body Sensor Network-Based Gait Quality Assessment for Clinical Decision-Support via Multi-Sensor Fusion. IEEE Access, 7, 59884-59894.
Qiu, S., Liu, L., Zhao, H., Wang, Z., & Jiang, Y. (2018). MEMS inertial sensors-based gait analysis for rehabilitation assessment via multi-sensor fusion. Micromachines, 9(9), 442.
Raccagni, C., Gaßner, H., Eschlboeck, S., Boesch, S., Krismer, F., Seppi, K., ... Klucken, J. (2018). Sensor-based gait analysis in atypical parkinsonian disorders. Brain and Behavior, 8(6), e00977.
Rao, A.K., Marder, K.S., Uddin, J., & Rakitin, B.C. (2014). Variability in interval production is due to timing-dependent deficits in Huntington's disease. Movement Disorders, 29(12), 1516-1522.
Rao, S., Saltzman, C., & Yack, H.J. (2006). Ankle ROM and stiffness measured at rest and during gait in individuals with and without diabetic sensory neuropathy. Gait & Posture, 24(3), 295-301.
Reed, L.F., Urry, S.R., & Wearing, S.C. (2013). Reliability of spatiotemporal and kinetic gait parameters determined by a new instrumented treadmill system. BMC Musculoskeletal Disorders, 14(1), 249.
Requejo, P.S., Wahl, D.P., Bontrager, E.L., Newsam, C.J., Gronley, J.K., Mulroy, S.J., & Perry, J. (2005). Upper extremity kinetics during Lofstrand crutch-assisted gait. Medical Engineering & Physics, 27(1), 19-29.
Roy, G., Jacob, T., Bhatia, D., & Bhaumik, S. (2020). Optical Marker- and Vision-Based Human Gait Biomechanical Analysis. In Hybrid Machine Intelligence for Medical Image Analysis (pp. 275-291). Springer, Singapore.
Rucco, R., Agosti, V., Jacini, F., Sorrentino, P., Varriale, P., De Stefano, M., Milan, G., Montella, P., & Sorrentino, G. (2017). Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer’s disease through 3D motion capture. Gait & Posture, 52, 312-317.
Sacco, I.C.N., Hamamoto, A.N., Gomes, A.A., Onodera, A.N., Hirata, R.P., & Hennig, E.M. (2009). Role of ankle mobility in foot rollover during gait in individuals with diabetic neuropathy. Clinical Biomechanics, 24(8), 687-692.
Safizadeh, M., Hussein, M., Yaacob, M., Zain, M.M., Abdullah, M., Kob, M.C., & Samat, K. (2011). Kinematic analysis of powered lower limb orthoses for gait rehabilitation of hemiplegic and hemiparetic patients. Order, 7, 17.
Saini, D. J. B., Kamble, S. D., Shankar, R., Kumar, M. R., Kapila, D., Tripathi, D. P., ... & Rashed, A. N. Z. (2023). Fractal video compression for IOT-based smart cities applications using motion vector estimation. Measurement: Sensors, 26, 100698.
Saini, D. J. B., Sivakami, R., Venkatesh, R., Raghava, C. S., Dwarkanath, P. S., Anwer, T. M. K., ... & Rashed, A. N. Z. (2023). Convolution neural network model for predicting various lesion-based diseases in diabetic macula edema in optical coherence tomography images. Biomedical Signal Processing and Control, 86(B), 105180.
Saini, D. K. J., Siddharth, D., & Kumar, A. (2021). Visualization and Prediction of COVID-19 Using AI and ML. In A. Saxena & S. Chandra (Eds.), Artificial Intelligence and Machine Learning in Healthcare. Springer. https://doi.org/10.1007/978-981-16-0811-7_6
Saini, D. K. J., Siddharth, D., & Kumar, A. (2021). Visualization and Prediction of COVID-19 Using AI and ML. In A. Saxena & S. Chandra (Eds.), Artificial Intelligence and Machine Learning in Healthcare (pp. 247–273). Scrivener Publishing.
Sawacha, Z., Cristoferi, G., Guarneri, G., Corazza, S., Donà, G., Denti, P., Facchinetti, A., Avogaro, A., & Cobelli, C. (2009). Characterizing multisegment foot kinematics during gait in diabetic foot patients. Journal of NeuroEngineering and Rehabilitation, 6(1), 37.
Scheys, L., Desloovere, K., Spaepen, A., Suetens, P., & Jonkers, I. (2011). Calculating gait kinematics using MR-based kinematic models. Gait & Posture, 33(2), 158-164.
Schniepp, R., Möhwald, K., & Wuehr, M. (2019). Clinical and automated gait analysis in patients with vestibular, cerebellar, and functional gait disorders: Perspectives and limitations. Journal of Neurology, 266(2), 298-302.
Segal, A. D., Yeates, K. H., Neptune, R. R., & Klute, G. K. (2018). Foot and ankle joint biomechanical adaptations to an unpredictable coronally uneven surface. Journal of Biomechanical Engineering, 140(3), 031004.
Shahrokhshahi, A., Yousefi-Koma, A., Khadiv, M., Mansouri, S., & Mohtasebi, S. S. (2019). Optimal Stair-Climbing Pattern Generation for Humanoids Using Virtual Slope and Distributed Mass Model. Journal of Intelligent & Robotic Systems, 94(1), 43-59.
Sharma, P., Yadav, R. K., & Saini, D. J. B. (2022). A Survey on the State of Art Approaches for Disease Detection in Plants. International Journal on Recent and Innovation Trends in Computing and Communication, 10(11), 14-21.
Siddharth, D., Saini, D. J. B., Ramchandra, M., Loganathan, S. (2024). Conversational Artificial Intelligence at Industrial Internet of Things. In Conversational Artificial Intelligence (pp. 169-183). 10.1002/9781394200801.ch11
Singh-Bains, M. K., Mehrabi, N. F., Sehji, T., Austria, M. D., Tan, A. Y., Tippett, L. J., Dragunow, M., Waldvogel, H. J., & Faull, R. L. (2019). Cerebellar degeneration correlates with motor symptoms in Huntington disease. Annals of Neurology, 85(3), 396-405.
Šlajpah, S., Kamnik, R., & Munih, M. (2014). Kinematics based sensory fusion for wearable motion assessment in human walking. Computer Methods and Programs in Biomedicine, 116(2), 131-144.
Sorrentino, P., Agosti, V., & Sorrentino, G. (2016). Motor Patterns Recognition in Parkinson’s Disease. Handbook of Human Motion, 1-16.
Stone, A. E., Terza, M. J., Raffegeau, T. E., & Hass, C. J. (2019). Walking through the looking glass: Adapting gait patterns with mirror feedback. Journal of Biomechanics, 83, 104-109.
Stout, R. D., Wittstein, M. W., LoJacono, C. T., & Rhea, C. K. (2016). Gait dynamics when wearing a treadmill safety harness. Gait & Posture, 44, 100-102.
Sutherland, D. H. (2001). The evolution of clinical gait analysis part l: kinesiological EMG. Gait & Posture, 14(1), 61-70.
Sutherland, D. H. (2002). The evolution of clinical gait analysis: Part ii kinematics. Gait & Posture, 16(2), 159–179.
Sutherland, D. H. (2005). The evolution of clinical gait analysis part iii-kinetics and energy assessment. Gait & Posture, 21(4), 447–461.
Swinnen, E., Baeyens, J. P., Van Mulders, B., Verspecht, J., & Degelaen, M. (2018). The influence of the use of ankle-foot orthoses on thorax, spine, and pelvis kinematics during walking in children with cerebral palsy. Prosthetics and Orthotics International, 42(2), 208-213.
Takahashi, T., Ishida, K., Hirose, D., Nagano, Y., Okumiya, K., Nishinaga, M., Doi, Y., & Yamamoto, H. (2004). Vertical ground reaction force shape is associated with gait parameters, timed up and go, and functional reach in elderly females. Journal of Rehabilitation Medicine, 36(1), 42-45.
Tanaka, H., Nankaku, M., Nishikawa, T., Hosoe, T., Yonezawa, H., Mori, H., Kikuchi, T., Nishi, H., Takagi, Y., Miyamoto, S., & Ikeguchi, R. (2019). Spatiotemporal gait characteristic changes with gait training using the hybrid assistive limb for chronic stroke patients. Gait & Posture, 71, 205-210.
Tang, Y., Li, Z., Tian, H., Ding, J., & Lin, B. (2019). Detecting Toe-Off Events Utilizing a Vision-Based Method. Entropy, 21(4), 329.
Tao, W., Liu, T., Zheng, R., & Feng, H. (2012). Gait analysis using wearable sensors. Sensors, 12(2), 2255-2283.
Termsarasab, P., & Frucht, S. J. (2018). The "Stutter-Step": A Peculiar Gait Feature in Advanced Huntington's Disease and Chorea-Acanthocytosis. Movement Disorders Clinical Practice, 5(2), 223.
Terrier, P., & Dériaz, O. (2011). Kinematic variability, fractal dynamics and local dynamic stability of treadmill walking. Journal of Neuro Engineering and Rehabilitation, 8(1), 12.
Teufl, W., Lorenz, M., Miezal, M., Taetz, B., Fröhlich, M., & Bleser, G. (2019). Towards inertial sensor based-mobile gait analysis: event-detection and spatio-temporal parameters. Sensors, 19(1), 38.
Titus, A. W., Hillier, S., Louw, Q. A., & Inglis-Jassiem, G. (2018). An analysis of trunk kinematics and gait parameters in people with stroke. African Journal of Disability (Online), 7, 1–6.
Van Gestel, L., De Laet, T., Di Lello, E., Bruyninckx, H., Molenaers, G., Van Campenhout, A., Aertbeliën, E., Schwartz, M., Wambacq, H., De Cock, P., & Desloovere, K. (2011). Probabilistic gait classification in children with cerebral palsy: A Bayesian approach. Research in Developmental Disabilities, 32(6), 2542-2552.
Verlekar, T. T., De Vroey, H., Claeys, K., Hallez, H., Soares, L. D., & Correia, P. L. (2019). Estimation and validation of temporal gait features using a markerless 2D video system. Computer Methods and Programs in Biomedicine, 175, 45-51.
Verlekar, T., Soares, L., & Correia, P. (2018). Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System. Sensors, 18(9), 2743.
Verma, S. B., Pandey, B., & Kumar Gupta, B. (2023). Containerization and its Architectures: A Study. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 395-409. https://doi.org/10.14201/adcaij.28351
Verma, S., Gupta, N., B C, A., & Chauhan, R. (2023). A Novel Framework for Ancient Text Translation Using Artificial Intelligence. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 411-425. https://doi.org/10.14201/adcaij.28380.
Wafai, L., Zayegh, A., Woulfe, J., & Begg, R. (2014, February). Automated classification of plantar pressure asymmetry during pathological gait using artificial neural network. In 2nd Middle East Conference on Biomedical Engineering (pp. 220-223). IEEE.
Wallard, L., Dietrich, G., Kerlirzin, Y., & Bredin, J. (2017). Robotic-assisted gait training improves walking abilities in diplegic children with cerebral palsy. European Journal of Paediatric Neurology, 21(3), 557-564.
Wang, J., She, M., Nahavandi, S., & Kouzani, A. (2010). A review of vision-based gait recognition methods for human identification. In 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 320-323). IEEE.
Wang, X., & Yan, W. Q. (2019). Cross-view gait recognition through ensemble learning. Neural Computing and Applications, 1-13.
Whittle, M. W. (2014). Gait analysis: An introduction. Butterworth-Heinemann.
Wong, C., McKeague, S., Correa, J., Liu, J., & Yang, G. Z. (2012, May). Enhanced classification of abnormal gait using BSN and depth. In 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (pp. 166-171). IEEE.
Yao, L., Kusakunniran, W., Wu, Q., Zhang, J., Tang, Z., & Yang, W. (2019). Robust gait recognition using hybrid-descriptors based on Skeleton Gait Energy Image. Pattern Recognition Letters.
Yoshida, T., Nozaki, J., Urano, K., Hiroi, K., Yonezawa, T., & Kawaguchi, N. (2019, June). Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (pp. 641-642). ACM.
Yuan, Y., Li, Z., Zhao, T., & Gan, D. (2019). DMP-based Motion Generation for a Walking Exoskeleton Robot Using Reinforcement Learning. IEEE Transactions on Industrial Electronics.
Zato, C., et al. (2012). PANGEA–Platform for Automatic construction of organizations of intelligent agents. In Distributed Computing and Artificial Intelligence (pp. 229–239). Springer.
Zelik, K. E., & Honert, E. C. (2018). Ankle and foot power in gait analysis: Implications for science, technology and clinical assessment. Journal of Biomechanics, 75, 1–12. doi:10.1016/j.jbiomech.2018. 05.028
Zhang, J., Lockhart, T. E., & Soangra, R. (2014). Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Annals of Biomedical Engineering, 42(3), 600-612. doi:10.1007/s10439-013-0914-5
Zhang, Y., & Ma, Y. (2019). Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia. Computers in Biology and Medicine, 106, 33-39. doi:10.1016/j.compbiomed.2019.01.007
Zhao, H., Wang, Z., Qiu, S., Wang, J., Xu, F., Wang, Z., & Shen, Y. (2019). Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion. Information Fusion, 52, 157-166. doi:10.1016/j.inffus.2019.02.012
Zhu, M., Shi, Q., He, T., Yi, Z., Ma, Y., Yang, B., Chen, T., & Lee, C. (2019). Self-powered and self-functional cotton sock using piezoelectric and triboelectric hybrid mechanism for healthcare and sports monitoring. ACS Nano, 13(2), 1940-1952. doi:10.1021/acs nano.8b09257
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