journal article·2025-04-20Open access

Markerless Upper Body Movement Tracking During Gait in Children with HIV Encephalopathy: A Pilot Study

Maaike M. Eken, Pieter Meyns, Robert P. Lamberts, Nelleke G. Langerak

Applied Sciences· Volume 15· Issue 8

Abstract

The aim of this pilot study was to investigate the feasibility of markerless tracking to assess upper body movements of children with and without human immunodeficiency virus encephalopathy (HIV-E). Sagittal and frontal video recordings were used to track anatomical landmarks with the DeepLabCut pre-trained human model in five children with HIV-E and five typically developing (TD) children to calculate shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension and trunk lateral sway. Differences in joint angle trajectories of the two cohorts were investigated using a one-dimensional statistical parametric mapping method. Children with HIV-E showed a larger range of motion in shoulder abduction and trunk sway than TD children. In addition, they showed more shoulder extension and more lateral trunk sway compared to TD children. Markerless tracking was feasible for 2D movement analysis and sensitive to observe expected differences in upper limb and trunk sway movements between children with and without HIVE. Therefore, it could serve as a useful alternative in settings where expensive gait laboratory instruments are unavailable, for example, in clinical centers in low- to middle-income countries. Future research is needed to explore 3D markerless movement analysis systems and investigate the reliability and validity of these systems against the gold standard 3D marker-based systems that are currently used in clinical practice.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cerebral Palsy and Movement Disorders · Autoimmune and Inflammatory Disorders Research · Neurogenetic and Muscular Disorders Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Sport and Exercise Medicine (ISEM), Department of Exercise, Sport and Lifestyle Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, Cape Town 7505, South Africa

Institute of Orthopaedics and Rheumatology, Mediclinic Winelands Orthopaedic Hospital, Stellenbosch, Cape Town 7600, South Africa

REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3500 Hasselt, Belgium

Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 Groningen, GZ, The Netherlands

Neuroscience Institute and Division of Neurosurgery, Faculty of Health Sciences, University of Cape Town, Cape Town 7935, South Africa

Department of Research, Sint Maartenskliniek, 6532 Nijmegen, SZ, The Netherlands

Funding

DIOS mobility programme 2019 · International AIDS Society (Collaborative Initiative for Paediatric HIV Education and Research (CIPHER) Grant Programme) · South African National Research Foundation · Neuroscience Institute at University of Cape Town · Hasselt University

References (40)

Show source-provided references
1. SIAMOC position paper on gait analysis in clinical practice: General requirements, methods and appropriateness. Results of an Italian consensus conference10.1016/j.gaitpost.2017.08.003 ↗
2. A 2D Markerless Gait Analysis Methodology: Validation on Healthy Subjects10.1155/2015/186780 ↗
3. Fernández-González, P., Koutsou, A., Cuesta-Gómez, A., Carratalá-Tejada, M., Miangolarra-Page, J.C., and Molina-Rueda, F. (2020). Reliability of Kinovea® Software and Agreement with a Three-Dimensional Motion System for Gait Analysis in Healthy Subjects. Sensors, 20.10.3390/s20113154 ↗
4. Reliability of video-based quantification of the knee- and hip angle at foot strike during running
5. Neurologic and neurodevelopmental manifestations of pediatric HIV/AIDS: A global perspective10.1016/j.ejpn.2006.10.006 ↗
6. UNAIDS (2025, April 19). UNAIDS Data. Available online: https://www.unaids.org/en/resources/documents/2019/2019-UNAIDS-data.
7. Tindyebwa, D., Kayita, J., Musoke, P., Eley, B., Nduati, R., Coovadia, H., Bobart, R., Mbori-Ngacha, D., and Kieffer, P.M. (2017). Handbook on Paediatric AIDS in Africa, African Network for the Care of Children Affected by HIV/AIDS—ANECCA Publishers. [4th ed.].
8. Differences in contractile behaviour between the soleus and medial gastrocnemius muscles during human walking
9. Spastic diplegia in children with HIV encephalopathy: First description of gait and physical status10.1111/dmcn.12319 ↗
10. HIV encephalopathy with bilateral lower limb spasticity: Upper limb motor function and level of activity and participation10.1111/dmcn.13236 ↗
11. Deep neural networks enable quantitative movement analysis using single-camera videos10.1038/s41467-020-17807-z ↗
12. DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning10.1038/s41593-018-0209-y ↗
13. Using DeepLabCut for 3D markerless pose estimation across species and behaviors10.1038/s41596-019-0176-0 ↗
14. Altered arm posture in children with cerebral palsy is related to instability during walking10.1016/j.ejpn.2012.01.011 ↗
15. Ankle foot orthoses in cerebral palsy: Effects of ankle stiffness on trunk kinematics, gait stability and energy cost of walking10.1016/j.ejpn.2020.02.009 ↗
16. The arm posture in children with unilateral Cerebral Palsy is mainly related to antero-posterior gait instability10.1016/j.gaitpost.2016.06.033 ↗
17. Delabastita, T., Desloovere, K., and Meyns, P. (2016). Restricted Arm Swing Affects Gait Stability and Increased Walking Speed Alters Trunk Movements in Children with Cerebral Palsy. Front. Hum. Neurosc, 10.10.3389/fnhum.2016.00354 ↗
18. Markerless movement tracking using a machine-learning algorithm to assess arm movements during gait in children with HIV encephalopathy10.1016/j.gaitpost.2020.07.074 ↗
19. HIV encephalopathy with bilateral lower limb spasticity: Gross motor function and antiretroviral therapy10.1111/dmcn.13377 ↗
20. Revised classification system for human immunodeficiency virus infection in children less than 13 years of age
21. Validation of a Model of Gross Motor Function for Children with Cerebral Palsy10.1093/ptj/80.10.974 ↗
22. World Medical Association (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310, 2191–2194.10.1001/jama.2013.281053 ↗
23. Evaluating upper body movements during gait in healthy children and children with diplegic cerebral palsy10.1097/BPB.0b013e32801405bf ↗
24. De Vlieger, D., Defour, A., Bar-On, L., Cambier, D., Swinnen, E., Van der Looven, R., and Van Bladel, A. (2025). Speed-dependent changes in the arm swing during independent walking in individuals after stroke. PLoS ONE, 20.10.1371/journal.pone.0315332 ↗
25. Trunk biomechanics during hemiplegic gait after stroke: A systematic review10.1016/j.gaitpost.2017.03.004 ↗
26. Chaparro-Rico, B.D.M., Cafolla, D., Tortola, P., and Galardi, G. (2020). Assessing Stiffness, Joint Torque, and ROM for Paretic and Non-Paretic Lower Limbs during the Subacute Phase of Stroke Using Lokomat Tools. Appl. Sci., 10.10.3390/app10186168 ↗
27. Quantitative trunk sway analysis under challenging gait conditions in early and untreated Parkinson’s disease10.1007/s10072-021-05699-w ↗
28. Zhou, J., Butler, E.E., and Rose, J. (2017). Neurologic Correlates of Gait Abnormalities in Cerebral Palsy: Implications for Treatment. Front. Hum. Neurosci., 11.10.3389/fnhum.2017.00103 ↗
29. Selective dorsal rhizotomy in ambulant children with cerebral palsy10.1302/1863-2548.12.180123 ↗
30. Resonant frequencies of arms and legs identify different walking patterns10.1016/S0021-9290(00)00020-8 ↗
31. Dynamics of pathological gait10.1016/0167-9457(94)90049-3 ↗
32. Defining characteristics of independent walking persons after stroke presenting with different arm swing coordination patterns10.1016/j.humov.2023.103174 ↗
33. Puig-Diví, A., Escalona-Marfil, C., Padullés-Riu, J.M., Busquets, A., Padullés-Chando, X., and Marcos-Ruiz, D. (2019). Validity and reliability of the Kinovea program in obtaining angles and distances using coordinates in 4 perspectives. PLoS ONE, 14.10.1371/journal.pone.0216448 ↗
34. Verification of Reliability and Validity of Trunk Forward Tilt Angle Measurement During Gait Using 2-Dimensional Motion Analysis10.1016/j.jcm.2022.04.008 ↗
35. The evaluation of an inexpensive, 2D, video based gait assessment system for clinical use10.1016/j.gaitpost.2013.01.018 ↗
36. Capturing quality clinical videos for two-dimensional motion analysis10.1097/JPO.0000000000000051 ↗
37. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics10.7717/peerj.12995 ↗
38. Needham, L., Evans, M., Cosker, D.P., Wade, L., McGuigan, P.M., Bilzon, J.L., and Colyer, S.L. (2021). The accuracy of several pose estimation methods for 3D joint centre localisation. Sci. Rep., 11.10.1038/s41598-021-00212-x ↗
39. van den Bogaart, M., Jacobs, N., Hallemans, A., and Meyns, P. (2025). Validity of Deep Learning-Based Motion Capture Using DeepLabCut to Assess Proprioception in Children. Appl. Sci., 15.10.3390/app15073428 ↗
40. Sipari, D., Chaparro-Rico, B.D.M., and Cafolla, D. (2022). SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph191610032 ↗