A Comparison of Smoothing and Filtering Approaches Using Simulated Kinematic Data of Human Movements

Gulde, Philipp; Hermsdörfer, Joachim

doi:10.1007/978-3-319-67846-7_10

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…All post-processing was computed with MatLab (MATLAB R2017a, MathWorks, MA). After differentiation the data were smoothed using a 0.1s local regression filter (“loess”) ( 33 ). This short smoothing window (12 frames) was chosen in order to preserve as much information as possible, to not corrupt the outcomes of spectral analysis, and in order to not fully eliminate noise, which can have a specific impact on jerk metrics ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Smoothness Metrics in Complex Movement Tasks

Gulde

Hermsdörfer

2018

Front. Neurol.

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Smoothness is a main characteristic of goal-directed human movements. The suitability of approaches quantifying movement smoothness is dependent on the analyzed signal's structure. Recently, activities of daily living (ADL) received strong interest in research on aging and neurorehabilitation. Such tasks have complex signal structures and kinematic parameters need to be adapted. In the present study we examined four different approaches to quantify movement smoothness in ADL. We tested the appropriateness of these approaches, namely the number of velocity peaks per meter (NoP), the spectral arc length (SAL), the speed metric (SM) and the log dimensionless jerk (LDJ), by comparing movement signals from eight healthy elderly (67.1a ± 7.1a) with eight healthy young (26.9a ± 2.1a) participants performing an activity of daily living (making a cup of tea). All approaches were able to identify group differences in smoothness (Cohen's d NoP = 2.53, SAL = 1.95, SM = 1.69, LDJ = 4.19), three revealed high to very high sensitivity (z-scores: NoP = 1.96 ± 0.55, SAL = 1.60 ± 0.64, SM = 3.41 ± 3.03, LDJ = 5.28 ± 1.52), three showed low within-group variance (NoP = 0.72, SAL = 0.60, SM = 0.11, LDJ = 0.71), two showed strong correlations between the first and the second half of the task execution (intra-trial R2s: NoP = 0.22 n.s., SAL = 0.33, SM = 0.36, LDJ = 0.91), and one was independent of other kinematic parameters (SM), while three showed strong models of multiple linear regression (R2s: NoP = 0.61, SAL = 0.48, LDJ = 0.70). Based on our results we make suggestion toward use examined smoothness measures. In total the log dimensionless jerk proved to be the most appropriate in ADL, as long as trial durations are controlled.

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Section: Methodsmentioning

confidence: 99%

Smoothness Metrics in Complex Movement Tasks

Gulde

Hermsdörfer

2018

Front. Neurol.

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“…The absolute acceleration vector was calculated by the Euclidean mean. The gravitation was subtracted as a constant (see Discussion), and the signal was smoothed using a 420 ms local regression algorithm [50]. The analysis was based on kinematic parameters using acceleration data identified in our previous study [46].…”

Section: Sensor-based Kinematic Parametersmentioning

confidence: 99%

Kinematic analysis of activities of daily living performance in frail elderly

et al. 2022

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Background Frailty is accompanied by limitations of activities of daily living (ADL) and frequently associated with reduced quality of life, institutionalization, and higher health care costs. Despite the importance of ADL performance for the consequence of frailty, movement analyses based on kinematic markers during the performance of complex upper extremity-based manual ADL tasks in frail elderly is still pending. The main objective of this study was to evaluate if ADL task performance of two different tasks in frail elderlies can be assessed by an activity measurement based on an acceleration sensor integrated into a smartwatch, and further to what degree kinematic parameters would be task independent. Methods ADL data was obtained from twenty-seven elderly participants (mean age 81.6 ± 7.0 years) who performed two ADL tasks. Acceleration data of the dominant hand was collected using a smartwatch. Participants were split up in three groups, F (frail, n = 6), P (pre-frail, n = 13) and R (robust, n = 8) according to a frailty screening. A variety of kinematic measures were calculated from the vector product reflecting activity, agility, smoothness, energy, and intensity. Results Measures of agility, smoothness, and intensity revealed significant differences between the groups (effect sizes combined over tasks η2p = 0.18 – 0.26). Smoothness was particularly affected by frailty in the tea making task, while activity, agility, a different smoothness parameter and two intensity measures were related to frailty in the gardening task. Four of nine parameters revealed good reliability over both tasks (r = 0.44 – 0.69). Multiple linear regression for the data combined across tasks showed that only the variability of the magnitude of acceleration peaks (agility) contributed to the prediction of the frailty score (R2 = 0.25). Conclusion The results demonstrate that ADL task performance can be assessed by smartwatch-based measures and further shows task-independent differences between the three levels of frailty. From the pattern of impaired and preserved performance parameters across the tested tasks, we concluded that in persons with frailty ADL performance was more impaired by physiological deficiencies, i.e., physical power and endurance, than by cognitive functioning or sensorimotor control.

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“…Our work also brings to light the importance of the quality of smoothing. Gulde and Hermsdörfer (2018a) reported that minimal window spans yielded the largest relative deviations, whereas window spans between 280 and 690 ms had the lowest relative deviations. One limitation of this study is that the effect of derivative calculations was not examined since jerk-based motion smoothness metrics were not computed.…”

Section: Does the Degree Of Smoothing Significantly Affect The Computation Of Motion Smoothness Metrics?mentioning

confidence: 99%

“…As an example, PL, a commonly used metric, only measures the total length of the motion traversed during a motion; it does not quantify how smooth that motion was. Some studies demonstrate moderate utility for Pks to examine smoothness of motion, with limited usage due to lack of generalizability and robustness [Balasubramanian et al (2012), Balasubramanian et al (2015), Estrada et al (2016), Gulde and Hermsdörfer (2018a)]. In the following subsections, we detail more advanced formulations of motion smoothness metrics that are based on higher-order derivatives of position data.…”

Section: Motion Smoothnessmentioning

confidence: 99%

“…To our knowledge, no study thus far has presented the effect of the degree of smoothing for measuring clinical skill. A related study demonstrated that, for computing SPARC and PL, optimal filtering was obtained for a specific window span range and for a specific type of filter [Gulde and Hermsdörfer (2018a)]. This study, however, did not include computation of LDLJ (or related metrics) wherein obtaining stable higher-order derivatives of position is critical for accuracy and interpretability.…”

Section: Motivation Of Studymentioning

confidence: 99%

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Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

et al. 2021

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Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop “smart” training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (LDLJ) and spectral arc length (SPARC) are increasingly being applied in medical simulators. However, two key questions remain about the efficacy of such metrics: how do these metrics relate to clinical skill, and how to best compute these metrics from sensor data and relate them with similar metrics? This study addresses these questions in the context of hemodialysis cannulation by enrolling 52 clinicians who performed cannulation in a simulated arteriovenous (AV) fistula. For clinical skill, results demonstrate that the objective outcome metric flash ratio (FR), developed to measure the quality of task completion, outperformed traditional skill indicator metrics (years of experience and global rating sheet scores). For computing motion smoothness metrics for skill assessment, we observed that the lowest amount of smoothing could result in unreliable metrics. Furthermore, the relative efficacy of motion smoothness metrics when compared with other process metrics in correlating with skill was similar for FR, the most accurate measure of skill. These results provide guidance for the computation and use of motion-based metrics for clinical skill assessment, including utilizing objective outcome metrics as ideal measures for quantifying skill.

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A Comparison of Smoothing and Filtering Approaches Using Simulated Kinematic Data of Human Movements

Cited by 7 publications

References 23 publications

Smoothness Metrics in Complex Movement Tasks

Smoothness Metrics in Complex Movement Tasks

Kinematic analysis of activities of daily living performance in frail elderly

Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

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