Fiktion oder baldige Realität?

Zusammenfassung

Das Gesundheitssystem befindet sich im demografischen Wandel, und gleichzeitig lässt der medizinische Fortschritt der letzten Jahrzehnte die Nachfrage nach Gesundheitsdienstleistungen anwachsen. Angesichts knapper werdender Ressourcen müssen innovative Lösungen und Technologien weiterentwickelt werden, um die Verfügbarkeit von Gesundheitsdienstleistungen zu gewährleisten. Die vorgestellten Technologien können wertvolle Ansätze liefern, um Krankheitsverläufe und Therapiefortschritte engmaschig und mithilfe selbstlernender Methoden der KI zu dokumentieren.

Publication History

Article published online:
22 April 2025

• Literatur

• 1 Bohannon RW, Andrews AW, Smith MB. Rehabilitation goals of patients with hemiplegia. Int J Rehabil Res 1988; 11: 181-184
• 2 Hornby TG, Reisman DS, Ward IG. et al. Clinical practice guideline to improve locomotor function following chronic stroke, incomplete spinal cord injury, and brain injury. J Neurol Phys Ther JNPT 2020; 44: 49-100
• 3 Mehrholz J, Pohl M, Kugler J. et al. The improvement of walking ability following stroke. Dtsch Arztebl Int 2018; 115: 639-645
• 4 Elsner B, Mehrholz J. et al. Gehen Sie zurück auf Los. neuroreha 2019; 11: 59-64
• 5 Shafrin J, Sullivan J, Goldman DP. et al. The association between observed mobility and quality of life in the near elderly. PLOS ONE 2017; 12: 1-13
• 6 Davis JC, Stirling B, Li LC. et al. Mobility and cognition are associated with wellbeing and health related quality of life among older adults: A cross-sectional analysis of the Vancouver Falls Prevention Cohort. BMC Geriatr 2015; 15: 75
• 7 Higgs J, Jones MA, Loftus S. et al. Clinical reasoning in the health professions. London: Elsevier Health Sciences UK; 2014
• 8 Klemme B, Siegmann G. Clinical Reasoning. Stuttgart Thieme 2015;
• 9 Elsner B, Mehrholz J. Algorithmen vs. Experten in der Neuroreha: Wer macht den besseren Job?. neuroreha 2021; 13: 15-20
• 10 Perry J. Pathologic gait. Instr Course Lect 1990; 39: 325-331
• 11 Baker R, Esquenazi A, Benedetti MG. et al. Gait analysis: Clinical facts. Eur J Phys Rehabil Med 2016; 52: 560-574
• 12 Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 2014; 14: 3362-3394
• 13 Baker R, Hart HM. Measuring walking. London: Mac Keith Press; 2013
• 14 McGinley JL, Baker R, Wolfe R. et al. The reliability of three-dimensional kinematic gait measurements: A systematic review. Gait & Posture 2009; 29: 360-369
• 15 Wren TAL, Tucker CA, Rethlefsen SA. et al. Clinical efficacy of instrumented gait analysis: Systematic review 2020 update. Gait & Posture 2020; 80: 274-279
• 16 Götz-Neumann K. Gehen verstehen. Stuttgart Thieme 2015;
• 17 Zhang H, Li X, Gong Y. et al. Three-dimensional gait analysis and sEMG measures for robotic-assisted gait training in subacute stroke: A randomized controlled trial. BioMed Res Int 2023; 2023: 7563802
• 18 Mukaino M, Ohtsuka K, Tanikawa H. et al. Clinical-oriented three-dimensional gait analysis method for evaluating gait disorder. J Vis Exp 2018; e57063
• 19 Lam WWT, Tang YM, Fong KNK. A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation. Neuroeng Rehabil 2023; 20: 57
• 20 Jeyasingh-Jacob J, Crook-Rumsey M, Shah H. et al. Markerless motion capture to quantify functional performance in neurodegeneration: Systematic review. JMIR Aging 2024; 7: e52582
• 21 Kanko RM, Laende EK, Strutzenberger G. et al. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech 2021; 122: 110414
• 22 Wishaupt K, Schallig W, van Dorst MH. et al. The applicability of markerless motion capture for clinical gait analysis in children with cerebral palsy. Sci Rep 2024; 14: 11910
• 23 Ben Gamra M, Akhloufi MA. A review of deep learning techniques for 2 D and 3 D human pose estimation. Image Vis Comput 2021; 114: 104282
• 24 Cronin NJ. Using deep neural networks for kinematic analysis: Challenges and opportunities. J Biomech 2021; 123: 110460
• 25 Mathis A, Schneider S, Lauer J. et al. A primer on motion capture with deep learning: Principles, pitfalls, and perspectives. Neuron 2020; 108: 44-65
• 26 Wade L, Needham L, McGuigan P. et al. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ 2022; 10: e12995
• 27 Harsted S, Holsgaard-Larsen A, Hestbæk L. et al. Concurrent validity of lower extremity kinematics and jump characteristics captured in pre-school children by a markerless 3 D motion capture system. Chiropr Man Ther 2019; 27: 39
• 28 Kanko RM, Laende EK, Davis EM. et al. Concurrent assessment of gait kinematics using marker-based and markerless motion capture. J Biomech 2021; 127: 110665
• 29 Horsak B, Eichmann A, Lauer K. et al. Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait. J Biomech 2023; 159: 111801
• 30 Wren TAL, Isakov P, Rethlefsen SA. Comparison of kinematics between Theia markerless and conventional marker-based gait analysis in clinical patients. Gait & Posture 2023; 104: 9-14
• 31 D‘Antonio E, Taborri J, Mileti I. et al. Validation of a 3 D markerless system for gait analysis based on open pose and two RGB webcams. IEEE Sens J 2021; 21: 17064-17075
• 32 Mehrholz J, Elsner B, Thomas S. Virtuelle Realität: Was ist im Einsatz?. neuroreha 2017; 09: 9-14
• 33 Prajjwal P, Chandrasekar KK, Battula P. et al. The efficacy of virtual reality-based rehabilitation in improving motor function in patients with stroke: A systematic review and meta-analysis. Ann Med Surg 2024; 86: 5425-5438
• 34 Huber M, Janssen C, Erzer Lüscher F. et al. Motorisches Lernen in der Neuroreha. Stuttgart Thieme 2023;
• 35 Sachverständigenrat Gesundheit und Pflege. Fachkräfte im Gesundheitswesen. Nachhaltiger Einsatz einer knappen Ressource. Gutachten 2024; 332
• 36 Horak F, King L, Mancini M. Role of body-worn movement monitor technology for balance and gait rehabilitation. Phys Ther 2015; 95: 461-470
• 37 Happe L, Lau S, Koschate J. et al. Machbarkeit und Akzeptanz videobasierter Physiotherapie: Neues Versorgungsangebot für ältere Menschen während der COVID-19-Pandemie. Z Gerontol Geriatr 2021; 54: 346-352
• 38 Langemak S. Telerehabilitation als Chance für eine bessere Versorgung während und nach der Pandemie. neuroreha 2021; 13: 32-34

• Quelle

• Brust AK. KI-unterstützte Bewegungsanalyse in der Neurorehabilitation – Fiktion oder baldige Realität?. neuroreha 2024; 16: 167-172