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      Inertial Sensor Based Analysis of Lie-to-Stand Transfers in Younger and Older Adults

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          Abstract

          Many older adults lack the capacity to stand up again after a fall. Therefore, to analyse falls it is relevant to understand recovery patterns, including successful and failed attempts to get up from the floor in general. This study analysed different kinematic features of standing up from the floor. We used inertial sensors to describe the kinematics of lie-to-stand transfer patterns of younger and healthy older adults. Fourteen younger (20–50 years of age, 50% men) and 10 healthy older community dwellers (≥60 years; 50% men) conducted four lie-to-stand transfers from different initial lying postures. The analysed temporal, kinematic, and elliptic fitting complexity measures of transfer performance were significantly different between younger and older subjects (i.e., transfer duration, angular velocity (RMS), maximum vertical acceleration, maximum vertical velocity, smoothness, fluency, ellipse width, angle between ellipses). These results show the feasibility and potential of analysing kinematic features to describe the lie-to-stand transfer performance, to help design interventions and detection approaches to prevent long lies after falls. It is possible to describe age-related differences in lie-to-stand transfer performance using inertial sensors. The kinematic analysis remains to be tested on patterns after real-world falls.

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          Most cited references19

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          Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly.

          A new method of physical activity monitoring is presented, which is able to detect body postures (sitting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking," 8.2% for sitting, and 1.3% for lying.
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            Sensitivity of smoothness measures to movement duration, amplitude, and arrests.

            Studies of sensory-motor performance, including those concerned with changes because of age, disease, or therapeutic intervention, often use measures based on jerk, the time derivative of acceleration, to quantify smoothness and coordination. However, results have been mixed: some researchers report sensitive discrimination of subtle differences, whereas others fail to find significant differences even when they are obviously present. One reason for this is that different measures have been used with different scaling factors. These measures are sensitive to movement amplitude or duration to different degrees. The authors show that jerk-based measures with dimensions vary counterintuitively with movement smoothness, whereas a dimensionless jerk-based measure properly quantifies common deviations from smooth, coordinated movement.
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              A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

              Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                12 August 2016
                August 2016
                : 16
                : 8
                : 1277
                Affiliations
                [1 ]Department of Clinical Gerontology, Robert-Bosch Hospital, Stuttgart 70376, Germany; ronald.boos@ 123456googlemail.com (R.B.); jochen.klenk@ 123456rbk.de (J.K.); clemens.becker@ 123456rbk.de (C.B.)
                [2 ]Institute of Epidemiology and Medical Biometry, Ulm University, Ulm 89081, Germany
                [3 ]Department of Neuroscience, Norwegian University of Science and Technology, Trondheim NO-7491, Norway; alan.bourke@ 123456ntnu.no
                [4 ]Institute of Movement and Sport Gerontology, German Sport University Cologne, Cologne 50933, Germany; zijlstra@ 123456dshs-koeln.de
                Author notes
                [* ]Correspondence: lars.schwickert@ 123456rbk.de ; Tel.: +49-711-8101-6074
                Article
                sensors-16-01277
                10.3390/s16081277
                5017442
                27529249
                e4f24089-5d84-4eb8-ac0b-fd3ded43726f
                © 2016 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 June 2016
                : 09 August 2016
                Categories
                Article

                Biomedical engineering
                recovery,lie-to-standing transfer,inertial sensors,signal analysis,kinematic analysis,fall detection

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