scholarly journals Estimating Movement Smoothness From Inertial Measurement Units

Author(s):  
Alejandro Melendez-Calderon ◽  
Camila Shirota ◽  
Sivakumar Balasubramanian

Inertial measurement units (IMUs) are increasingly used to estimate movement quality and quantity to the infer the nature of motor behavior. The current literature contains several attempts to estimate movement smoothness using data from IMUs, many of which assume that the translational and rotational kinematics measured by IMUs can be directly used with the smoothness measures spectral arc length (SPARC) and log dimensionless jerk (LDLJ-V). However, there has been no investigation of the validity of these approaches. In this paper, we systematically evaluate the use of these measures on the kinematics measured by IMUs. We show that: (a) SPARC and LDLJ-V are valid measures of smoothness only when used with velocity; (b) SPARC and LDLJ-V applied on translational velocity reconstructed from IMU is highly error prone due to drift caused by integration of reconstruction errors; (c) SPARC can be applied directly on rotational velocities measured by a gyroscope, but LDLJ-V can be error prone. For discrete translational movements, we propose a modified version of the LDLJ-V measure, which can be applied to acceleration data (LDLJ-A). We evaluate the performance of these measures using simulated and experimental data. We demonstrate that the accuracy of LDLJ-A depends on the time profile of IMU orientation reconstruction error. Finally, we provide recommendations for how to appropriately apply these measures in practice under different scenarios, and highlight various factors to be aware of when performing smoothness analysis using IMU data.

2020 ◽  
Author(s):  
Alejandro Melendez-Calderon ◽  
Camila Shirota ◽  
Sivakumar Balasubramanian

AbstractThere is an increasing trend in using inertial measurement units (IMUs) to estimate movement quality and quantity, and infer the nature of motor behavior. The current literature contains several attempts to estimate movement smoothness using data from IMUs, most of which assume that the translational and rotational kinematics measured by IMUs can be directly used with existing smoothness measures - spectral arc length (SPARC) and log dimensionless jerk (LDLJ-V). However, there has been no investigation of the validity of these approaches. In this paper, we systematically evaluate the appropriateness of the using these measures on the kinematics measured by an IMU. We show that: (a) current measures (SPARC and LDLJ-V) are inappropriate for translational movements; and (b) SPARC and LDLJ-V can be used rotational kinematics measured by an IMU. For discrete translational movements, we propose a modified version of the LDLJ-V measure, which can be applied to acceleration data (LDLJ-A), while roughly maintaining the properties of the original measure. However, accuracy of LDLJ-A depends on the IMU orientation estimation error. We evaluate the performance of these measures using simulated and experimental data. We then provide recommendations for how to appropriately apply these measures in practice, and the various factors to be aware of when performing smoothness analysis using IMU data.


Author(s):  
Rhiannon A Campbell ◽  
Elizabeth J Bradshaw ◽  
Nick Ball ◽  
Adam Hunter ◽  
Wayne Spratford

The magnitude of loading artistic gymnasts experience during training is currently unknown. Inertial measurement units (IMUs) could assess loading, although the reliability of these devices must be established prior to implementation into the training environment. This study aimed to determine inter-trial reliability of using IMeasureU Blue Thunder IMUs to assess upper and lower limb loading when performing foundation gymnastics skills. A secondary aim investigated the effect of raw and filtered acceleration signals on reliability results. Sixteen competitive level artistic gymnasts (male, n = 8; female, n = 8) performed seven gymnastics skills while wearing four IMUs (upper back, lower back, forearm and tibia). The peak resultant acceleration (PRA) during ground contact for all skills was exported from raw and filtered acceleration data (fourth-order zero-lag Butterworth filter with 85 Hz cut-off). Descriptive statistics (median and inter-quartile range), Friedman’s ANOVA, intra-class correlations, coefficient of variation, mean difference and Cohen’s effect sizes were calculated. Overall, the IMU PRA measures showed very good inter-trial reliability, however filtered signals improved reliability statistics for five variables compared to raw. The forearm- and tibia-mounted IMUs demonstrated improved reliability (very good reliability) compared to back positions (good reliability). IMUs are considered reliable devices to measure upper and lower limb loading in gymnastics.


2021 ◽  
Author(s):  
Ann David ◽  
Tanya Subash ◽  
SKM Varadhan ◽  
Alejandro Melendez-Calderon ◽  
Sivakumar Balasubramanian

AbstractThe ultimate goal of any upper-limb neurorehabilitation procedure is to improve upper-limb functioning in daily life. While clinic-based assessments provide an assessment of what a patient can do, they do not completely reflect what a patient does in his/her daily life. The compensatory use of the less affected upper-limb (e.g. “learned non-use”) in daily life is a common behavioral pattern seen in patients with hemiparesis. To this end, there has been an increasing interest in the use of wearable sensors to objectively assess upper-limb functioning. This paper presents a framework for assessing upper-limb functioning using sensors by providing: (a) a set of definitions of important construct associated with upper-limb functioning; (b) presenting different visualization methods for evaluating upper-limb functioning, along ways to qualitatively analyze different visualization methods; and (c) two new measures for quantifying how much an upper-limb is used and the relative bias in the use of the two upper-limbs. The demonstration of some of these components is presented using data collected from inertial measurement units from a previous study. The proposed framework can help guide the future technical and clinical work in this area to realize a valid, objective, and robust tool for assessing upper-limb functioning. This will in turn drive the refinement and standardization of the assessment of upper-limb functioning.


2021 ◽  
Vol 15 ◽  
Author(s):  
Ann David ◽  
Tanya Subash ◽  
S. K. M. Varadhan ◽  
Alejandro Melendez-Calderon ◽  
Sivakumar Balasubramanian

The ultimate goal of any upper-limb neurorehabilitation procedure is to improve upper-limb functioning in daily life. While clinic-based assessments provide an assessment of what a patient can do, they do not completely reflect what a patient does in his/her daily life. The use of compensatory strategies such as the use of the less affected upper-limb or excessive use of trunk in daily life is a common behavioral pattern seen in patients with hemiparesis. To this end, there has been an increasing interest in the use of wearable sensors to objectively assess upper-limb functioning. This paper presents a framework for assessing upper-limb functioning using sensors by providing: (a) a set of definitions of important constructs associated with upper-limb functioning; (b) different visualization methods for evaluating upper-limb functioning; and (c) two new measures for quantifying how much an upper-limb is used and the relative bias in their use. The demonstration of some of these components is presented using data collected from inertial measurement units from a previous study. The proposed framework can help guide the future technical and clinical work in this area to realize valid, objective, and robust tools for assessing upper-limb functioning. This will in turn drive the refinement and standardization of the assessment of upper-limb functioning.


Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2767 ◽  
Author(s):  
Eva Repnik ◽  
Urška Puh ◽  
Nika Goljar ◽  
Marko Munih ◽  
Matjaž Mihelj

In patients after stroke, ability of the upper limb is commonly assessed with standardised clinical tests that provide a complete upper limb assessment. This paper presents quantification of upper limb movement during the execution of Action research arm test (ARAT) using a wearable system of inertial measurement units (IMU) for kinematic quantification and electromyography (EMG) sensors for muscle activity analysis. The test was executed with each arm by a group of healthy subjects and a group of patients after stroke allocated into subgroups based on their clinical scores. Tasks were segmented into movement and manipulation phases. Each movement phase was quantified with a set of five parameters: movement time, movement smoothness, hand trajectory similarity, trunk stability, and muscle activity for grasping. Parameters vary between subject groups, between tasks, and between task phases. Statistically significant differences were observed between patient groups that obtained different clinical scores, between healthy subjects and patients, and between the unaffected and the affected arm unless the affected arm shows normal performance. Movement quantification enables differentiation between different subject groups within movement phases as well as for the complete task. Spearman’s rank correlation coefficient shows strong correlations between patient’s ARAT scores and movement time as well as movement smoothness. Weak to moderate correlations were observed for parameters that describe hand trajectory similarity and trunk stability. Muscle activity correlates well with grasping activity and the level of grasping force in all groups.


2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.


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