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      • Record: found
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      A multi-resolution investigation for postural transition detection and quantification using a single wearable

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          Highlights

          • •

            Evaluated wavelet algorithm for PT detection with a wearable on the lower back.

          • •

            A ‘one size fits all’ algorithm for PT is insufficient.

          • •

            Different PT types require different wavelet algorithm manipulations.

          • •

            PT detection is accurate across a number of wavelets in controlled settings.

          • •

            Temporal calculations unstable & require development prior to use in free-living.

          Abstract

          Background

          Multi-resolution analyses involving wavelets are commonly applied to data derived from accelerometer-based wearable technologies (wearables) to identify and quantify postural transitions (PTs). Previous studies fail to provide rationale to inform their choice of wavelet and scale approximation when utilising discrete wavelet transforms. This study examines varying combinations of those parameters to identify best practice recommendations for detecting and quantifying sit-to-stand (SiSt) and stand-to-sit (StSi) PTs.

          Methods

          39 young and 37 older participants completed three SiSt and StSi PTs on supported and unsupported chair types while wearing a single tri-axial accelerometer-based wearable on the lower back. Transition detection and duration were calculated through peak detection within the signal vector magnitude for a range of wavelets and scale approximations. A laboratory reference measure (2D video) was used for comparative analysis.

          Results

          Detection accuracy of wavelet and scale combinations for the transitions was excellent for both SiSt (87–97%) and StSi (82–86%) PT-types. The duration of PTs derived from the wearable showed considerable bias and poor agreement compared with the reference videos. No differences were observed between chair types and age groups respectively.

          Conclusions

          Improved detection of PTs could be achieved through the incorporation of different wavelet and scale combinations for the assessment of specific PT types in clinical and free-living settings. An upper threshold of 5th scale approximations is advocated for improved detection of multiple PT-types. However, care should be taken estimating the duration of PTs using wearables.

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

          B Najafi, K. Aminian, A. Paraschiv-Ionescu … (2003)
          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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            Direct measurement of human movement by accelerometry.

            A. Godfrey, R. Conway, D. Meagher … (2008)
            Human movement has been the subject of investigation since the fifth century when early scientists and researchers attempted to model the human musculoskeletal system. The anatomical complexities of the human body have made it a constant source of research to this day with many anatomical, physiological, mechanical, environmental, sociological and psychological studies undertaken to define its key elements. These studies have utilised modern day techniques to assess human movement in many illnesses. One such modern technique has been direct measurement by accelerometry, which was first suggested in the 1970s but has only been refined and perfected during the last 10-15 years. Direct measurement by accelerometry has seen the introduction of the successful implementation of low power, low cost electronic sensors that have been employed in clinical and home environments for the constant monitoring of patients (and their controls). The qualitative and quantitative data provided by these sensors make it possible for engineers, clinicians and physicians to work together to be able to help their patients in overcoming their physical disability. This paper presents the underlying biomechanical elements necessary to understand and study human movement. It also reflects on the sociological elements of human movement and why it is important in patient life and well being. Finally the concept of direct measurement by accelerometry is presented with past studies and modern techniques used for data analysis.
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              Determinants of the sit-to-stand movement: a review.

              Wim G. M. Janssen, Hans Bussmann, Henk Stam (2002)
              The sit-to-stand (STS) movement is a skill that helps determine the functional level of a person. Assessment of the STS movement has been done using quantitative and semiquantitative techniques. The purposes of this study were to identify the determinants of the STS movement and to describe their influence on the performance of the STS movement. A search was made using MEDLINE (1980-2001) and the Science Citation Index Expanded of the Institute for Scientific Information (1988-2001) using the key words "chair," "mobility," "rising," "sit-to-stand," and "standing." Relevant references such as textbooks, presentations, and reports also were included. Of the 160 identified studies, only those in which the determinants of STS movement performance were examined using an experimental setup (n=39) were included in this review. The literature indicates that chair seat height, use of armrests, and foot position have a major influence on the ability to do an STS movement. Using a higher chair seat resulted in lower moments at knee level (up to 60%) and hip level (up to 50%); lowering the chair seat increased the need for momentum generation or repositioning of the feet to lower the needed moments. Using the armrests lowered the moments needed at the hip by 50%, probably without influencing the range of motion of the joints. Repositioning of feet influenced the strategy of the STS movement, enabling lower maximum mean extension moments at the hip (148.8 N m versus 32.7 N m when the foot position changed from anterior to posterior). The ability to do an STS movement, according to the research reviewed, is strongly influenced by the height of the chair seat, use of armrests, and foot position. More study of the interaction among the different determinants is needed. Failing to account for these variables may lead to erroneous measurements of changes in STS performance.
              Article 0 comments Cited 94 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Alan Godfrey
                Journal
                Journal ID (nlm-ta): Gait Posture
                Journal ID (iso-abbrev): Gait Posture
                Title: Gait & Posture
                Publisher: Elsevier Sciencem
                ISSN (Print): 0966-6362
                ISSN (Electronic): 1879-2219
                Publication date PMC-release: 1 September 2016
                Publication date (Print): September 2016
                Volume: 49
                Pages: 411-417
                Affiliations
                [a ]Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
                [b ]Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
                [c ]Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK
                [d ]Human Nutrition Research Centre, Newcastle University, Newcastle upon Tyne, UK
                Author notes
                [* ]Corresponding author at: Institute for Neuroscience, Clinical Ageing Research Unit, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK. alan.godfrey@ 123456ncl.ac.uk
                Article
                Publisher ID: S0966-6362(16)30472-6
                DOI: 10.1016/j.gaitpost.2016.07.328
                PMC ID: 5038932
                PubMed ID: 27513738
                SO-VID: 51c5baf5-d332-4812-9f0d-7a803b19e032
                Copyright © © 2016 The Authors
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 19 January 2016
                Date revision received : 26 July 2016
                Date accepted : 29 July 2016
                Categories
                Subject: Full Length Article

                ScienceOpen disciplines: Orthopedics
                Keywords: postural transition,accelerometer,wearables,wavelet,discrete wavelet transform
                Data availability:
                ScienceOpen disciplines: Orthopedics
                Keywords: postural transition, accelerometer, wearables, wavelet, discrete wavelet transform

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