期刊论文详细信息
BioMedical Engineering OnLine
Estimation of human trunk movements by wearable strain sensors and improvement of sensor’s placement on intelligent biomedical clothes
Giorgio Sandrini1  Cristina Tassorelli1  Silvana Quaglini3  Federica Fecchio3  Alessandro M De Nunzio4  Michelangelo Bartolo2  Paolo Tormene3 
[1]Neurorehabilitation Unit, Neurological National Institute Casimiro Mondino Foundation, IRCCS, Pavia, Italy
[2]Department of Neurological Science, University of Pavia, Pavia, Italy
[3]Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy
[4]Neurorehabilitation Unit, IRCCS Neurological Mediterranean Institute NEUROMED, Pozzilli (Isernia), Italy
关键词: Rehabilitation;    Intelligent biomedical clothes;    Trunk;    Wearable strain sensors;   
Others  :  797970
DOI  :  10.1186/1475-925X-11-95
 received in 2012-09-05, accepted in 2012-11-12,  发布年份 2012
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【 摘 要 】

Background

The aim of this study was to evaluate the concept of a wearable device and, specifically: 1) to design and implement analysis procedures to extract clinically relevant information from data recorded using the wearable system; 2) to evaluate the design and placement of the strain sensors.

Methods

Different kinds of trunk movements performed by a healthy subject were acquired as a comprehensive data set of 639 multivariate time series and off-line analyzed. The space of multivariate signals recorded by the strain sensors was reduced by means of Principal Components Analysis, and compared with the univariate angles contemporaneously measured by an inertial sensor.

Results

Very high correlation between the two kinds of signals showed the usefulness of the garment for the quantification of the movements’ range of motion that caused at least one strain sensor to lengthen or shorten accordingly. The repeatability of signals was also studied. The layout of a next garment prototype was designed, with additional strain sensors placed across the front and hips, able to monitor a wider set of trunk motor tasks.

Conclusions

The proposed technologies and methods would offer a low-cost and unobtrusive approach to trunk motor rehabilitation.

【 授权许可】

   
2012 Tormene et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Dickstein R, Shefi S, Marcovitz E, Villa Y: Anticipatory postural adjustment in selected trunk muscles in post stroke hemiparetic patients. Arch Phys Med Rehabil 2004, 85:261-267.
  • [2]Bartolo M, Serrao M, Tassorelli C, Don R, Ranavolo A, Draicchio F, Pacchetti C, Buscone S, Perrotta A, Furnari A, et al.: Four-week trunk-specific rehabilitation treatment improves lateral trunk flexion in Parkinson's disease. Mov Disord 2010, 25:325-331.
  • [3]Jacobs JV, Henry SM, Nagle KJ: Low back pain associates with altered activity of the cerebral cortex prior to arm movements that require postural adjustment. Clin Neurophysiol 2010, 121:431-440.
  • [4]Schmid M, De Nunzio AM, Schieppati M: Trunk muscle proprioceptive input assists steering of locomotion. Neurosci Lett 2005, 384:127-132.
  • [5]Courtine G, De Nunzio AM, Schmid M, Beretta MV, Schieppati M: Stance- and locomotion-dependent processing of vibration-induced proprioceptive inflow from multiple muscles in humans. J Neurophysiol 2007, 97:772-779.
  • [6]Adkin AL, Bloem BR, Allum JH: Trunk sway measurements during stance and gait tasks in Parkinson's disease. Gait Posture 2005, 22:240-249.
  • [7]de Seze M, Wiart L, Bon-Saint-Come A, Debelleix X, de Seze M, Joseph PA, Mazaux JM, Barat M: Rehabilitation of postural disturbances of hemiplegic patients by using trunk control retraining during exploratory exercises. Arch Phys Med Rehabil 2001, 82:793-800.
  • [8]Wade DT, Skilbeck CE, Hewer RL: Predicting Barthel ADL score at 6 months after an acute stroke. Arch Phys Med Rehabil 1983, 64:24-28.
  • [9]Kwakkel G, Wagenaar RC, Kollen BJ, Lankhorst GJ: Predicting disability in stroke–a critical review of the literature. Age Ageing 1996, 25:479-489.
  • [10]Bartolo M, Don R, Ranavolo A, Serrao M, Sandrini G: Kinematic and neurophysiological models: future applications in neurorehabilitation. J Rehabil Med 2009, 41:986-987.
  • [11]Ring H: Technology in rehabilitation. Eur Med Phys 2003, 39:3-6.
  • [12]Giorgino T, Tormene P, Maggioni G, Capozzi D, Quaglini S, Pistarini C: Assessment of sensorized garments as a flexible support to self-administered post-stroke physical rehabilitation. Eur J Phys Rehabil Med 2009, 45:75-84.
  • [13]De Rossi D, Veltink P: Wearable technology for biomechanics: e-textile or micromechanical sensors? IEEE Eng Med Biol Mag 2010, 29:37-43.
  • [14]Axisa F, Schmitt PM, Gehin C, Delhomme G, McAdams E, Dittmar A: Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. IEEE Trans Inf Technol Biomed 2005, 9:325-336.
  • [15]Chiari L: Wearable systems with minimal set-up for monitoring and training of balance and mobility. Conf Proc IEEE Eng Med Biol Soc 2011, 2011:5828-5832.
  • [16]Preece SJ, Kenney LP, Major MJ, Dias T, Lay E, Fernandes BT: Automatic identification of gait events using an instrumented sock. J Neuroeng Rehabil 2011, 8:32. BioMed Central Full Text
  • [17]Giorgino T, Lorussi F, De Rossi D, Quaglini S: Posture classification via wearable strain sensors for neurological rehabilitation. Conf Proc IEEE Eng Med Biol Soc 2006, 1:6273-6276.
  • [18]Bonato P: Clinical applications of wearable technology. Conf Proc IEEE Eng Med Biol Soc 2009, 2009:6580-6583.
  • [19]De Rossi D, Carpi F, Lorussi F, Scilingo EP, Tognetti A: Wearable kinesthetic systems and emerging technologies in actuation for upperlimb neurorehabilitation. Conf Proc IEEE Eng Med Biol Soc 2009, 2009:6830-6833.
  • [20]Lorussi F, Scilingo EP, Tesconi M, Tognetti A, De Rossi D: Strain sensing fabric for hand posture and gesture monitoring. IEEE Trans Inf Technol Biomed 2005, 9:372-381.
  • [21]Brennan A, Zhang J, Deluzio K, Li Q: Quantification of inertial sensor-based 3D joint angle measurement accuracy using an instrumented gimbal. Gait Posture 2011, 34:320-323.
  • [22]Tormene P, Giorgino T, Quaglini S, Stefanelli M: Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med 2009, 45:11-34.
  • [23]Giorgino T, Tormene P, Quaglini S: A multivariate time-warping based classifier for gesture recognition with wearable strain sensors. Conf Proc IEEE Eng Med Biol Soc 2007, 2007:4903-4906.
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