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      Estimation of human trunk movements by wearable strain sensors and improvement of sensor’s placement on intelligent biomedical clothes

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          Abstract

          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.

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          Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention.

          Fabrice Axisa, Pierre Michael Schmitt, Claudine Gehin … (2005)
          Improvement of the quality and efficiency of healthcare in medicine, both at home and in hospital, is becoming more and more important for patients and society at large. As many technologies (micro technologies, telecommunication, low-power design, new textiles, and flexible sensors) are now available, new user-friendly devices can be developed to enhance the comfort and security of the patient. As clothes and textiles are in direct contact with about 90% of the skin surface, smart sensors and smart clothes with noninvasive sensors are an attractive solution for home-based and ambulatory health monitoring. Moreover, wearable devices or smart homes with exosensors are also potential solutions. All these systems can provide a safe and comfortable environment for home healthcare, illness prevention, and citizen medicine.
          Article 0 comments Cited 40 times – based on 0 reviews     Review now
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            Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation.

            Paolo Tormene, Toni Giorgino, Silvana Quaglini … (2009)
            The purpose of this study was to assess the performance of a real-time ("open-end") version of the dynamic time warping (DTW) algorithm for the recognition of motor exercises. Given a possibly incomplete input stream of data and a reference time series, the open-end DTW algorithm computes both the size of the prefix of reference which is best matched by the input, and the dissimilarity between the matched portions. The algorithm was used to provide real-time feedback to neurological patients undergoing motor rehabilitation. We acquired a dataset of multivariate time series from a sensorized long-sleeve shirt which contains 29 strain sensors distributed on the upper limb. Seven typical rehabilitation exercises were recorded in several variations, both correctly and incorrectly executed, and at various speeds, totaling a data set of 840 time series. Nearest-neighbour classifiers were built according to the outputs of open-end DTW alignments and their global counterparts on exercise pairs. The classifiers were also tested on well-known public datasets from heterogeneous domains. Nonparametric tests show that (1) on full time series the two algorithms achieve the same classification accuracy (p-value =0.32); (2) on partial time series, classifiers based on open-end DTW have a far higher accuracy (kappa=0.898 versus kappa=0.447;p<10(-5)); and (3) the prediction of the matched fraction follows closely the ground truth (root mean square <10%). The results hold for the motor rehabilitation and the other datasets tested, as well. The open-end variant of the DTW algorithm is suitable for the classification of truncated quantitative time series, even in the presence of noise. Early recognition and accurate class prediction can be achieved, provided that enough variance is available over the time span of the reference. Therefore, the proposed technique expands the use of DTW to a wider range of applications, such as real-time biofeedback systems.
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              Trunk sway measurements during stance and gait tasks in Parkinson's disease.

              A.L. Adkin, B.R. Bloem, J.H.J. Allum (2005)
              To achieve a unified assessment of postural instability in Parkinson's disease (PD) over a range of clinical stance and gait tasks, which may provide an insight into a tendency to fall, we measured trunk sway in the anterior-posterior and medial-lateral directions in freely moving PD patients and age-matched controls. We also measured task duration as time to complete the task or time to loss of balance. Patients had larger amplitudes of trunk sway velocities for stance tasks (e.g. mean pitch velocity when standing on two-legs eyes closed equalled 19.1 +/- 6.4 for PD patients on medication versus 4.8 +/- 0.3 degrees/s for controls, p = 0.0003) and for an expected (following prior warning) retropulsion test (mean roll angle equalled 4.3 +/- 0.5 degrees for PD patients versus 2.2 +/- 0.6 degrees for controls, p = 0.0003) than controls. Patients were more likely to fall earlier for stance tasks, and took longer to complete gait tasks (e.g. walking 3 m eyes closed, mean time 6.8 +/- 0.6 sees versus 4.9 +/- 0.1 sees, p = 0.0001). These differences between patients and controls were, in most cases, independent of medication. Based on these results we defined a simple test battery of stance and gait tasks that could discriminate between PD patients who had recent falls and controls. These results indicate that trunk sway measures recorded during stance and gait tasks provide useful information on balance deficits leading to falls in PD patients.
              Article 0 comments Cited 34 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Biomed Eng Online
                Journal ID (iso-abbrev): Biomed Eng Online
                Title: BioMedical Engineering OnLine
                Publisher: BioMed Central
                ISSN (Electronic): 1475-925X
                Publication date Collection: 2012
                Publication date (Electronic): 14 December 2012
                Volume: 11
                Page: 95
                Affiliations
                [1 ]Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy
                [2 ]Neurorehabilitation Unit, IRCCS Neurological Mediterranean Institute NEUROMED, Pozzilli (Isernia), Italy
                [3 ]Department of Neurological Science, University of Pavia, Pavia, Italy
                [4 ]Neurorehabilitation Unit, Neurological National Institute Casimiro Mondino Foundation, IRCCS, Pavia, Italy
                Article
                Publisher ID: 1475-925X-11-95
                DOI: 10.1186/1475-925X-11-95
                PMC ID: 3528414
                PubMed ID: 23237732
                SO-VID: 8c0480d6-ed69-44ab-bc0c-64c0812b3ff6
                Copyright © Copyright ©2012 Tormene et al.; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 5 September 2012
                Date accepted : 12 November 2012
                Categories
                Subject: Research

                ScienceOpen disciplines: Biomedical engineering
                Keywords: wearable strain sensors,trunk,intelligent biomedical clothes,rehabilitation
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: wearable strain sensors, trunk, intelligent biomedical clothes, rehabilitation

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