Advanced 3D movement analysis algorithms for robust functional capacity assessment
29 November 2016
accepted: 11 February 2017
21 December 2017 (online)
Objectives: We developed a novel system for in home functional capacities assessment in frail older adults by analyzing the Timed Up and Go movements. This system aims to follow the older people evolution, potentially allowing a forward detection of motor decompensation in order to trigger the implementation of rehabilitation. However, the pre-experimentations conducted on the ground, in different environments, revealed some problems which were related to KinectTM operation. Hence, the aim of this actual study is to develop methods to resolve these problems.
Methods: Using the KinectTM sensor, we analyze the Timed Up and Go test movements by measuring nine spatio-temporal parameters, identified from the literature. We propose a video processing chain to improve the robustness of the analysis of the various test phases: automatic detection of the sitting posture, patient detection and three body joints extraction. We introduce a realistic database and a set of descriptors for sitting posture recognition. In addition, a new method for skin detection is implemented to facilitate the patient extraction and head detection. 94 experiments were conducted to assess the robustness of the sitting posture detection and the three joints extraction regarding condition changes.
Results: The results showed good performance of the proposed video processing chain: the global error of the sitting posture detection was 0.67%. The success rate of the trunk angle calculation was 96.42%. These results show the reliability of the proposed chain, which increases the robustness of the automatic analysis of the Timed Up and Go.
Conclusions: The system shows good measurements reliability and generates a note reflecting the patient functional level that showed a good correlation with 4 clinical tests commonly used. We suggest that it is interesting to use this system to detect impairment of motor planning processes.
Citation: Hassani A, Kubicki A, Mourey F, Yang F. Advanced 3D movement analysis algorithms for robust functional capacity assessment. Appl Clin Inform 2017; 8: 454–469 https://doi.org/10.4338/ACI-2016-11-RA-0199
KeywordsPatient self-care home care and e-health - clinical informatics - sitting posture recognition - skin detection - 3D real-time video processing
Clinical Relevance Statement
This study presents an innovative system for automatic and real-time analysis of the clinical test Timed Up and Go. It introduces a new method for the sitting posture detection that enables a robust analysis of the TUG. This system allows the automatic functional capacities assessment in older adults with good measurement reliability.
Human Subject Research Approval
- 1 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA. Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001; 56 (03) M146-M156.
- 2 Hassani A, Kubicki A, Brost V, Mourey F, Yang F. Kinematic analysis of motor strategies in frail aged adults during the Timed Up and Go: how to spot the motor frailty?. Clinical Interventions in Aging 2015; 10: 505-513.
- 3 Willner V, Schneider C, Feichtenschlager M. eHealth 2015 Special Issue: Effects of an Assistance Service on the Quality of Life of Elderly Users. Appl Clin Inform 2015; 6 (03) 429-442.
- 4 Frenken T, Vester B, Brell M, Hein A. aTUG: fully-automated timed up and go assessment using ambient sensor technologies. 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth). 2011: 55-62.
- 5 Higashi Y, Yamakoshi k, Fujimoto T, Sekine M, Tamura T. Quantitative evaluation of movement using the timed up-and-go test. IEEE Engineering in Medicine and Biology Magazine 2008; 27 (04) 38-46.
- 6 McGrath D, Greene BR, Doheny EP, McKeown DJ, De Vito G, Caulfield B. Reliability of quantitative TUG measures of mobility for use in falls risk assessment. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011; 466-469.
- 7 Whitney S, Marchetti G, Schade A, Wrisley D. The sensitivity and specificity of the timed ,,Up & go“ and the dynamic gait index for self-reported falls in persons with vestibular disorders. Journal of vestibular research: equilibrium & orientation 2004; 14 (05) 39-409.
- 8 Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the timed up & go test. Physical therapy 2000; 80 (09) 896-903.
- 9 Beauchet O, Fantino B, Allali G, Muir SW, Montero-Odasso M, Annweiler C. Timed Up and Go test and risk of falls in older adults: a systematic review. J Nutr Health Aging 2011; 15 (10) 933-938.
- 10 Greene BR, Kenny RA. Assessment of Cognitive Decline Through Quantitative Analysis of the Timed Up and Go Test. IEEE Transactions on Biomedical Engineering 2012; 59 (04) 988-995.
- 11 Mourey F, Pozzo T, Rouhier-Marcer I, Didier J. A kinematic comparison between elderly and young subjects standing up from and sitting down in a chair. Age and ageing 1998; 27 (02) 137-146.
- 12 Dubost V, Beauchet O, Manckoundia P, Herrmann F, Mourey F. Decreased trunk angular displacement during sitting down: an early feature of aging. Physical therapy 2005; 85 (05) 404-412.
- 13 Mourey F, Grishin A, d’Athis P, Pozzo T, Stapley P. Standing up from a chair as a dynamic equilibrium task: a comparison between young and elderly subjects. J Gerontol A Biol Sci Med Sci 2000; 55: 425-431.
- 14 Chang YJ, Chen SF, Huang JD. A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities 2011; 32 (06) 2566-2570.
- 15 Clark RA, Pua YH, Fortin K, Ritchie C, Webster KE, Denehy L, Bryant AL. Validity of the microsoft kinect for assessment of postural control. Gait & Posture 2012; 36 (03) 372-377.
- 16 Nghiem AT, Auvinet E, Meunier J. Head detection using kinect camera and its application to fall detection. 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) 2012; 164-169.
- 17 Kuipers DA, Wartena BO, Dijkstra BH, Terlouw G, van T Veer JT, van Dijk HW, Prins JT, Pierie JP. iLift: A health behavior change support system for lifting and transfer techniques to prevent lower-back injuries in healthcare. International Journal of Medical Informatics 2012; 96: 11-23.
- 18 Hayat M, Bennamoun M, El-Sallam AA. An RGB-D based image set classifcation for robust face recognition from Kinect data. Neurocomputing 2016; 171: 889-900.
- 19 Skjæret N, Nawaz A, Morat T, Schoene D, Helbostad JL, Vereijken B. Exercise and rehabilitation delivered through exergames in older adults: An integrative review of technologies, safety and efficacy. International Journal of Medical Informatics 2016; 85 (01) 1-16.
- 20 Hassani A, Kubicki A, Brost V, Yang F. Preliminary study on the design of a low-cost movement analysis system: reliability measurement of timed up and go test. VISAPP 2014; 33-38.
- 21 Hassani A. Real-time 3D movements analysis for a medical device intended for maintaining functional independence in aged adults at home. Ph.D. thesis University of Burgundy; (March 2016).
- 22 Persson CU, Danielsson A, Sunnerhagen KS, Grimby-Ekman A, Hansson P-O. Timed Up & Go as a measure for longitudinal change in mobility after stroke –Postural Stroke Study in Gothenburg (POSTGOT). Journal of NeuroEngineering and Rehabilitation 2014; 11: 83.
- 23 Nocera JR, Stegemöller EL, Malaty IA, Okun MS, Marsiske M, Hass CJ. National Parkinson Foundation Quality Improvement Initiative Investigators. Using the Timed Up & Go Test in a Clinical Setting to Predict Falling in Parkinson’s Disease. Archives of physical medicine and rehabilitation 2013; 94 (07) 1300-1305.
- 24 Warzee E, Elbouz L, Seidel L, Petermans J. Evaluation de la marche chez 115 sujets hospitalisées dans le service de gériatrie du CHU de Liège. Annales de Gérontologie 2010; 3 (02) 111-119.
- 25 Bonnechère B, Jansen B, Salvia P, Bouzahouene H, Omelina L, Cornelis J, Rooze M, Van Sint Jan S. What are the current limits of the kinect sensor? 9th Intl Conf. Disability, Virtual Reality & Associated Technologies (ICDVRAT). 2012: 287-294.
- 26 Clark RA, Bower KJ, Mentiplay BF, Paterson K, Pua YH. Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables. Journal of Biomechanics 2013; 46 (15) 2722-2725.
- 27 Manckoundia P, Mourey F, Pfitzenmeyer P, Papaxanthis C. Comparison of motor strategies in sit-to-stand and back-to-sit motions between healthy and Alzheimer‘s disease elderly subjects. Neuroscience 2006; 137 (02) 385-392.
- 28 Liu CL, Lee CH, Lin PM. A fall detection system using k-nearest neighbor classifier. Expert systems with applications 2010; 37 (10) 7174-7181.
- 29 Rougier C, Meunier J, St-Arnaud A, Rousseau J. Robust video surveillance for fall detection based on human shape deformation. IEEE Transactions on Circuits and Systems for Video Technology 2011; 21 (05) 611-622.
- 30 Liao YT, Huang CL, Hsu SC. Slip and fall event detection using Bayesian belief network. Pattern recognition 2012; 45 (01) 24-32.
- 31 Malagón-Borja L, Fuentes O. Object detection using image reconstruction with PCA. Image and Vision Computing 2009; 27 (1–2) 2-9.
- 32 Wang J, Barreto A, Wang L, Chen Y, Rishe N, Andrian J, Adjouadi M. Multilinear principal component analysis for face recognition with fewer features. Neurocomputing 2010; 73 (10–12) 1550-1555.
- 33 Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: Principles and Practice of Background Maintenance. The Proceedings of the Seventh IEEE International Conference on Computer Vision 1999; 1: 255-261.
- 34 Chiranjeevi P, Sengupta S. Spatially correlated background subtraction, based on adaptive background maintenance. Journal of Visual Communication and Image Representation 2012; 23 (06) 948-957.
- 35 Laptev I. On Space-Time Interest Points. International Journal of Computer Vision 2005; 64 (2–3) 107-123.
- 36 Harris C, Stephens M. A combined corner and edge detector. Fourth Alvey Vision Conference 1988; 147-151.
- 37 Föstner MA, Gülch E. A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centers of Circular Features. ISPRS Intercommission Workshop. 1987
- 38 Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 ; 1: I-511-I-518.
- 39 Kang S, Choi B, Jo D. Faces detection method based on skin color modeling. Journal of Systems Architecture 2016; 64: 100-109.
- 40 Huang L, Ji W, Wei Z, Chen BW, Yan CC, Nie J, Yin J, Jiang B. Robust skin detection in real-world images. Journal of Visual Communication and Image Representation 2015; 29: 147-152.
- 41 Nallaperumal K, Ravi S, Babu C, Selvakumar R, Fred A, Seldev C, Vinsley SS. Skin Detection Using Color Pixel Classi_cation with Application to Face Detection: A Comparative Study. International Conference on Conference on Computational Intelligence and Multimedia Applications 2007; 3: 436-441.
- 42 Kovac J, Peer P, Solina F. 2D versus 3D colour space face detection. 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications 2003; 2: 449-454.
- 43 Shaik KB, Ganesan P, Kalist V, Sathish BS, Merlin Mary Jenitha J. Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space. Procedia Computer Science 2015; 57: 4-48.
- 44 Kukharev G, Nowosielski A. Visitor identification –elaborating real time face recognition system. 12th Winther School on Computer Graphics (WSCG) 2004; 157-164.
- 45 Gouinaud H, Gavet Y, Debayle J, Pinoli JC. Color correction in the framework of Color Logarithmic Image Processing. 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA) 2011; 129-133.
- 46 Gouinaud H. Traitement logarithmique d’images couleur. Ph.D. thesis Ecole Nationale Supérieure des Mines de Saint-Etienne. 2013
- 47 Angulo J. Morphological colour operators in totally ordered lattices based on distances: Application to image filtering, enhancement and analysis. Computer Vision and Image Understanding 2007; 107 (12) 56-73.
- 48 Drillis R, Contini R. Body segment parameters. DHEW 1166–03. New York University, School of Engineering and Science; New York: