Quantification of upper body movements during gait in older adults and in those with Parkinson’s disease: impact of acceleration realignment methodologies

Buckley, Chris; Galna, Brook; Rochester, Lynn; Mazzà, Claudia

doi:10.1016/j.gaitpost.2016.11.047

Cited by 18 publications

(20 citation statements)

References 26 publications

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“…Therefore, if physical and economical resources are limited, models created from upper body accelerations could equally be used to classify PD gait. For this purpose, a sensor placed upon the pelvis may be the most applicable due to its methodically preferable location and ability to detect stride timing information in a variety of environments [16,25]. Furthermore if placed at the pelvis, the variables in the current investigations can potentially be combined with further variables such as stability measures [26] and turning characteristics [27], which were not included in this study due to methodological limitations.…”

Section: Discussionmentioning

confidence: 99%

“…using a limited enclosed space) and their ability to describe different domains of movement. Acceleration signals were realigned to the earth's gravitational constant [15,16], and a low-pass Butterworth filter with a cut-off frequency of 20 Hz was applied using MATLAB (version 8.4.0, R2014b) [7]. All variables were calculated on a single stride basis…”

Section: Introductionmentioning

confidence: 99%

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Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease

et al. 2019

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease

et al. 2019

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“…To ensure that the steady-state gait was analyzed, the initial and final three steps were removed from the signal. Prior to the calculation of additional variables, the acceleration signals were realigned to the earth's gravitational constant [29,30] and a low-pass Butterworth filter with a cut-off frequency of 20 Hz. A full description of the following variables and the required algorithms is the supplied by the provided references.…”

Section: Description Of Acceleration-derived Variablesmentioning

confidence: 99%

Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

Buckley

Micó-Amigo

Dunne-Willows

et al. 2019

Sensors

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Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test–retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test–retest reliability (Spearman’s rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry.

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“…The effect could possibly be seen by quantifying the characteristics of involuntary movements in the DCP target population. Similar quantification has been implemented in the involuntary tremor movement characteristics of patients diagnosed with Parkinson's disease [48][49][50].…”

Section: Discussionmentioning

confidence: 99%

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy

Gakopoulos

Nica

Bekteshi

et al. 2019

Sensors

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The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more independent mobility and better quality of life for people with motor disabilities. Especially, for people with complex movement disorders, such as dyskinetic cerebral palsy (DCP) who lack the ability to walk or to handle objects, wheelchairs offer a means of integration into daily life. The mobility of DCP patients is based on a head-foot wheelchair steering system. In this work, a data logging system is proposed to capture data from human-wheelchair interaction for the head-foot steering system. Additionally, the data logger provides an interface to multiple Inertial Measurement Units (IMUs) placed on the body of the wheelchair user. The system provides accurate and real-time information from head-foot navigation system pressure sensors on the wheelchair during driving. This system was used as a tool to obtain further insights into wheelchair control and steering behavior of people diagnosed with DCP in comparison with a healthy subject.

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Quantification of upper body movements during gait in older adults and in those with Parkinson’s disease: impact of acceleration realignment methodologies

Cited by 18 publications

References 26 publications

Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease

Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease

Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy

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