| Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article
- Language: English
- [Med Biol Eng Comput] 2024 Apr; Vol. 62 (4), pp. 1061-1076. <i>Date of Electronic Publication: </i>2023 Dec 23.
- MeSH Terms: Algorithms* ; Humans ; Aged ; Motion ; Biomechanical Phenomena ; Healthy Volunteers
- References: Alghwiri AA, Whitney SL (2012) Geriatric physical therapy. Mosby, Saint Louis, p 353. ; Rubenstein LZ (2006) Falls in older people: epidemiology, risk factors and strategies for prevention, (in English). Age Ageing 35:37–41. (PMID: 10.1093/ageing/afl084) ; Xu T, Zhou Y, Zhu J (2018) New advances and challenges of fall detection systems: a survey. Appl Sci Basel 8(3):418. (PMID: 10.3390/app8030418) ; Wang Y, Wu K, Ni LM (2016) Wifall: Device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594. (PMID: 10.1109/TMC.2016.2557792) ; Tzeng H-W, Chen M-Y, Chen J-Y (2010) Design of fall detection system with floor pressure and infrared image. In: 2010 International Conference on System Science and Engineering, pp 131–135: IEEE. ; Makhlouf A, Boudouane I, Saadia N, Cherif AR (2019) Ambient assistance service for fall and heart problem detection. J Ambient Intell Humanized Comput 10(4):1527–1546. (PMID: 10.1007/s12652-018-0724-4) ; Wang J, Zhang ZQ, Li B, Lee S, Sherratt RS (2014) An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Trans Consum Electron 60(1):23–29. (PMID: 10.1109/TCE.2014.6780921) ; Dang TN, Le TK, Hong TP, Nguyen V (2021) Fast and accurate fall detection and warning system using image processing technology. In: International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam, 2021, pp 207–210. ; Saadeh W, Butt SA, Bin Altaf MA (2019) A patient-specific single sensor IoT-based wearable fall prediction and detection system. IEEE Trans Neural Syst Rehabil Eng 27(5):995–1003. (PMID: 3099847310.1109/TNSRE.2019.2911602) ; Al Nahian MJ et al (2021) Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access 9:39413–39431. (PMID: 10.1109/ACCESS.2021.3056441) ; Panahi L, Ghods V (2018) Human fall detection using machine vision techniques on RGB-D images. Biomed Signal Process Control 44:146–153. (PMID: 10.1016/j.bspc.2018.04.014) ; Min WD, Yao LY, Lin ZR, Liu L (2018) Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle. IET Comput Vision 12(8):1133–1140. (PMID: 10.1049/iet-cvi.2018.5324) ; Hu XY, Qu XD (2016) Pre-impact fall detection. Biomed Eng Online 15:61. (PMID: 27251528488865410.1186/s12938-016-0194-x) ; Yajai A, Rasmequan S (2017) Adaptive directional bounding box from RGB-D information for improving fall detection. J Vis Commun Image Represent 49:257–273. (PMID: 10.1016/j.jvcir.2017.08.008) ; Chen W, Jiang Z, Guo H, Ni XJS (2020) Fall detection based on key points of human-skeleton using openpose. Symmetry 12(5):744. (PMID: 10.3390/sym12050744) ; Tamura T, Yoshimura T, Sekine M, Uchida M, Tanaka O (2009) A wearable airbag to prevent fall injuries. IEEE Trans Inf Technol Biomed 13(6):910–914. (PMID: 1984637910.1109/TITB.2009.2033673) ; Ahn S et al (2018) Optimization of a pre-impact fall detection algorithm and development of hip protection airbag system. Sensors Mater 30:1743–1752. (PMID: 10.18494/SAM.2018.1876) ; Yao M et al (2015) A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication. In: 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN), pp 1–6: IEEE. ; Choi W, Wakeling J, Robinovitch S (2015) Kinematic analysis of video-captured falls experienced by older adults in long-term care. J Biomech 48(6):911–920. (PMID: 2576973010.1016/j.jbiomech.2015.02.025) ; Vallee P, Tisserand R, Robert T (2015) Possible recovery or unavoidable fall? A model to predict the one step balance recovery threshold and its stepping characteristics. J Biomech 48(14):3905–3911. (PMID: 2660237110.1016/j.jbiomech.2015.09.024) ; Cyr M-A, Smeesters C (2009) Kinematics of the threshold of balance recovery are not affected by instructions limiting the number of steps in younger adults. Gait Posture 29(4):628–633. (PMID: 1924394810.1016/j.gaitpost.2009.01.011) ; Smeesters C, Hayes WC, McMahon TA (2001) The threshold trip duration for which recovery is no longer possible is associated with strength and reaction time. J Biomech 34(5):589–595. (PMID: 1131169910.1016/S0021-9290(01)00005-7) ; Xu T, Zhou Y (2018) Elders’ fall detection based on biomechanical features using depth camera. Int J Wavelets Multiresolution Inf Process 16(02):1840005. (PMID: 10.1142/S0219691318400052) ; Lugade V, Lin V, Chou L-S (2011) Center of mass and base of support interaction during gait. Gait Posture 33(3):406–411. (PMID: 2121197710.1016/j.gaitpost.2010.12.013) ; Hof AL, Gazendam MGJ, Sinke WE (2005) The condition for dynamic stability, (in English). J Biomech 38(1):1–8. (PMID: 1551933310.1016/j.jbiomech.2004.03.025) ; Bi QL et al (2017) An automatic camera calibration method based on checkerboard. Traitement Du Signal 34(3–4):209–226. (PMID: 10.3166/ts.34.209-226) ; Albert JA, Owolabi V, Gebel A, Brahms CM, Granacher U, Arnrich B (2020) Evaluation of the pose tracking performance of the Azure Kinect and Kinect v2 for gait analysis in comparison with a gold standard: a pilot study. Sensors 20(18):5104. (PMID: 32911651757121310.3390/s20185104) ; Manghisi VM et al (2020) A body tracking-based low-cost solution for monitoring workers’ hygiene best practices during pandemics. Sensors 20(21):6149. (PMID: 33138092766349310.3390/s20216149) ; Romeo L, Marani R, Malosio M, Perri AG, D'Orazio T, and Ieee (2021) Performance Analysis of Body Tracking with the Microsoft Azure Kinect. In: 29th Mediterranean Conference on Control and Automation (MED), Puglia, Italy, 2021, pp 572–577. ; Berg WP, Alessio HM, Mills EM, Tong C (1997) Circumstances and consequences of falls in independent community-dwelling older adults. Age Ageing 26(4):261–268. (PMID: 927128810.1093/ageing/26.4.261) ; Roudsari BS, Ebel BE, Corso PS, Molinari NAM, Koepsell TD (2005) The acute medical care costs of fall-related injuries among the US older adults. Injury-Int J Care Injured 36(11):1316–1322. (PMID: 10.1016/j.injury.2005.05.024) ; Roos PE, McGuigan MP, Trewartha G (2010) The role of strategy selection, limb force capacity and limb positioning in successful trip recovery. Clin Biomech 25(9):873–878. (PMID: 10.1016/j.clinbiomech.2010.06.016) ; King ST, Eveld ME, Martinez A, Zelik KE, Goldfarb M (2019) A novel system for introducing precisely-controlled, unanticipated gait perturbations for the study of stumble recovery. J Neuroengineering Rehabil 16:69. (PMID: 10.1186/s12984-019-0527-7) ; Shirota C, Simon AM, Kuiken TA (2014) Trip recovery strategies following perturbations of variable duration. J Biomech 47(11):2679–2684. (PMID: 2489402410.1016/j.jbiomech.2014.05.009) ; van Dieen JH, Pijnappels M, Bobbert MF (2005) Age-related intrinsic limitations in preventing a trip and regaining balance after a trip. Saf Sci 43(7):437–453. (PMID: 10.1016/j.ssci.2005.08.008) ; Pijnappels M, Reeves ND, Maganaris CN, Van Dieen JH (2008) Tripping without falling; lower limb strength, a limitation for balance recovery and a target for training in the elderly. J Electromyogr Kinesiol 18(2):188–196. (PMID: 1776143610.1016/j.jelekin.2007.06.004) ; Chen SH, Liu BA, Feng C, Vallespi-Gonzalez C, Wellington C (2021) 3D point cloud processing and learning for autonomous driving: impacting map creation, localization, and perception. IEEE Signal Process Mag 38(1):68–86. (PMID: 10.1109/MSP.2020.2984780) ; Soomro K, Idrees H, Shah M (2018) Online localization and prediction of actions and interactions. IEEE Trans Pattern Anal Mach Intell 41(2):459–472. (PMID: 2999460010.1109/TPAMI.2018.2797266) ; Ghosh P, Song J, Aksan E, Hilliges O, and Ieee (2017) Learning human motion models for long-term predictions. In: International Conference on 3D Vision (3DV), Qingdao, Canada, 2017, pp 458–466. ; Jin X, Guo J, Li Z, Wang RH (2020) Motion prediction of human wearing powered exoskeleton. Math Probl Eng 2020:8899880. (PMID: 10.1155/2020/8899880) ; Xu Z, Wang Y, Long M, Wang J, KLiss M (2018) PredCNN: predictive learning with cascade convolutions. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp 2940–2947. ; Liu XL, Yin JQ, Liu J, Ding PX, Liu J, Liu HP (2021) TrajectoryCNN: a new spatio-temporal feature learning network for human motion prediction. IEEE Trans Circuits Syst Video Technol 31(6):2133–2146. (PMID: 10.1109/TCSVT.2020.3021409) ; Fragkiadaki K, Levine S, Felsen P, Malik J (2015) Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4346–4354. ; Cheng Y, Zhao W, Liu C, Tomizuka M (2019) Human motion prediction using semi-adaptable neural networks. In: 2019 American Control Conference (ACC), pp 4884–4890: IEEE. ; Liu RX, Liu CL (2021) Human motion prediction using adaptable recurrent neural networks and inverse kinematics. IEEE Control Syst Lett 5(5):1651–1656. (PMID: 10.1109/LCSYS.2020.3042609) ; Melzer I, Kurz I, Shahar D, Levi M, Oddsson L (2007) Application of the voluntary step execution test to identify elderly fallers. Age Ageing 36(5):532–537. (PMID: 1754520710.1093/ageing/afm068) ; Tisserand R, Robert T, Chabaud P, Bonnefoy M, Cheze L (2016) Elderly fallers enhance dynamic stability through anticipatory postural adjustments during a choice stepping reaction time. Front Human Neurosci 10:613. (PMID: 10.3389/fnhum.2016.00613) ; Paolo DL (1996) Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters, (in English). J Biomech 29(9):1223–1230. (PMID: 10.1016/0021-9290(95)00178-6) ; Pavol MJ, Owings TM, Foley KT, Grabiner MD (2001) Mechanisms leading to a fall from an induced trip in healthy older adults. J Gerontol Ser a-Biol Sci Med Sci 56(7):M428–M437. ; Pijnappels M, Bobbert MF, van Dieen JH (2004) Contribution of the support limb in control of angular momentum after tripping. J Biomech 37(12):1811–1818. (PMID: 1551958810.1016/j.jbiomech.2004.02.038) ; Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. (PMID: 10.1109/4235.996017) ; Charfi I, Miteran J, Dubois J, Atri M, Tourki R (2013) Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification. J Electron Imaging 22(4):041106. (PMID: 10.1117/1.JEI.22.4.041106) ; Hsiao ET, Robinovitch SN (1999) Biomechanical influences on balance recovery by stepping, (in English). J Biomech 32(10):1099–1106. (PMID: 1047684810.1016/S0021-9290(99)00104-9) ; Li M, Xu G, He B, Ma X, Xie J (2018) Pre-impact fall detection based on a modified zero moment point criterion using data from Kinect sensors. IEEE Sens J 18(13):5522–5531. (PMID: 10.1109/JSEN.2018.2833451) ; Rescio G, Leone A, Siciliano P (2018) Supervised machine learning scheme for electromyography-based pre-fall detection system. Expert Syst Appl 100:95–105. (PMID: 10.1016/j.eswa.2018.01.047) ; Lee CM, Park J, Park S, Kim CH (2020) Fall-detection algorithm using plantar pressure and acceleration data. Int J Precis Eng Manuf 21(4):725–737. (PMID: 10.1007/s12541-019-00268-w) ; Jung H, Koo B, Kim J, Kim T, Nam Y, Kim Y (2020) Enhanced algorithm for the detection of preimpact fall for wearable airbags. Sensors 20(5):1277. (PMID: 32111090708577010.3390/s20051277) ; Yu X, Jang J, Xiong S (2021) A large-scale open motion dataset (KFall) and benchmark algorithms for detecting pre-impact fall of the elderly using wearable inertial sensors. Front Aging Neurosci 13:692865. (PMID: 34335231832272910.3389/fnagi.2021.692865) ; de Sousa F, Escriba C, Bravo EGA, Brossa V, Fourniols JY, Rossi C (2022) Wearable pre-impact fall detection system based on 3D accelerometer and subject’s height. IEEE Sens J 22(2):1738–1745. (PMID: 10.1109/JSEN.2021.3131037) ; Neptune RR, Vistamehr A (2019) Dynamic balance during human movement: measurement and control mechanisms, (in English). J Biomech Eng Trans Asme 141(7):070801. (PMID: 10.1115/1.4042170)
- Grant Information: 2022YFC2009500 National Key Research and Development Program of China; 2022ND0AC01 Key Research Project of Zhejiang Lab
- Contributed Indexing: Keywords: Balance recovery model; Fall Point; Fall warning; Imbalance Point; Motion prediction
- Entry Date(s): Date Created: 20231223 Date Completed: 20240320 Latest Revision: 20240320
- Update Code: 20240320
|
