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      Stair-Walking Performance in Adolescents with Intellectual Disabilities

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          Abstract

          Most individuals with intellectual disabilities (ID) demonstrate problems in learning and movement coordination. Consequently, they usually have difficulties in activities such as standing, walking, and stair climbing. To monitor the physical impairments of these children, regular gross motor evaluation is crucial. Straight-line level walking is the most frequently used test of their mobility. However, numerous studies have found that unless the children have multiple disabilities, no significant differences can be found between the children with ID and typically-developed children in this test. Stair climbing presents more challenges than level walking because it is associated with numerous physical factors, including lower extremity strength, cardiopulmonary endurance, vision, balance, and fear of falling. Limited ability in those factors is one of the most vital markers for children with ID. In this paper, we propose a sensor-based approach for measuring stair-walking performance, both upstairs and downstairs, for adolescents with ID. Particularly, we address the problem of sensor calibration to ensure measurement accuracy. In total, 62 participants aged 15 to 21 years, namely 32 typically-developed (TD) adolescents, 20 adolescents with ID, and 10 adolescents with multiple disabilities (MD), participated. The experimental results showed that stair-walking is more sensitive than straight-line level walking in capturing gait characteristics for adolescents with ID.

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          Optimal Placement of Accelerometers for the Detection of Everyday Activities

          This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.
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            Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing

            User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.
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              Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients.

              A detailed and quantitative gait analysis can provide evidence of various gait impairments in elderly people. To provide an objective decision-making basis for gait analysis, simple applicable tests analyzing a high number of strides are required. A mobile gait analysis system, which is mounted on shoes, can fulfill these requirements. This paper presents a method for computing clinically relevant temporal and spatial gait parameters. Therefore, an accelerometer and a gyroscope were positioned laterally below each ankle joint. Temporal gait events were detected by searching for characteristic features in the signals. To calculate stride length, the gravity compensated accelerometer signal was double integrated, and sensor drift was modeled using a piece-wise defined linear function. The presented method was validated using GAITRite-based gait parameters from 101 patients (average age 82.1 years). Subjects performed a normal walking test with and without a wheeled walker. The parameters stride length and stride time showed a correlation of 0.93 and 0.95 between both systems. The absolute error of stride length was 6.26 cm on normal walking test. The developed system as well as the GAITRite showed an increased stride length, when using a four-wheeled walker as walking aid. However, the walking aid interfered with the automated analysis of the GAITRite system, but not with the inertial sensor-based approach. In summary, an algorithm for the calculation of clinically relevant gait parameters derived from inertial sensors is applicable in the diagnostic workup and also during long-term monitoring approaches in the elderly population.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                11 July 2016
                July 2016
                : 16
                : 7
                : 1066
                Affiliations
                [1 ]Department of Computer Science and Information Engineering, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan; wyshieh@ 123456mail.cgu.edu.tw
                [2 ]Department of Adapted Physical Education, National Taiwan Sport University, No. 250, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan; yanju@ 123456ntsu.edu.tw
                [3 ]Department of Rehabilitation, National Taoyuan Special School, Tao-Yuan 330, Taiwan; ot.spring.yu@ 123456gmail.com
                [4 ]Graduate Institute of Medical Mechatronics, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan; aa1030520023@ 123456gmail.com
                [5 ]Graduate Institute of Early Intervention, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan; vickiy7711@ 123456gmail.com
                [6 ]Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, 5 Fu-Hsing Street, Kwei-Shan, Tao-Yuan 333, Taiwan
                Author notes
                [* ]Correspondence: kcheng@ 123456mail.cgu.edu.tw ; Tel.: +886-3-211-8800 (ext. 3667)
                Article
                sensors-16-01066
                10.3390/s16071066
                4970113
                27409621
                21381f30-becd-4781-b7e4-ee77ec2f28a7
                © 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
                : 21 March 2016
                : 08 July 2016
                Categories
                Article

                Biomedical engineering
                intellectual disabilities,mobility,gait,stair-walking,motion sensor
                Biomedical engineering
                intellectual disabilities, mobility, gait, stair-walking, motion sensor

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