scholarly journals Using Inertial Measurement Units and Electromyography to Quantify Movement during Action Research Arm Test Execution

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2767 ◽  
Author(s):  
Eva Repnik ◽  
Urška Puh ◽  
Nika Goljar ◽  
Marko Munih ◽  
Matjaž Mihelj
Keyword(s):  
Upper Limb ◽  
Muscle Activity ◽  
Healthy Subjects ◽  
Movement Time ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Trunk Stability ◽  
Movement Smoothness ◽  
Trajectory Similarity ◽  
Measurement Units

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.

scholarly journals Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1804
Author(s):  
Jorge Posada-Ordax ◽  
Julia Cosin-Matamoros ◽  
Marta Elena Losa-Iglesias ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Laura Esteban-Gonzalo ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
High Speeds ◽  
Controlled Conditions ◽  
Gait Parameters ◽  
Measurement Units ◽  
Accuracy And Repeatability

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

scholarly journals Template-Based Step Detection with Inertial Measurement Units

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4033 ◽  
Author(s):  
Laurent Oudre ◽  
Rémi Barrois-Müller ◽  
Thomas Moreau ◽  
Charles Truong ◽  
Aliénor Vienne-Jumeau ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Detection Algorithm ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Step Detection ◽  
Medical Context ◽  
Measurement Units ◽  
End Times

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.

scholarly journals Estimating Movement Smoothness From Inertial Measurement Units

2021 ◽  
Vol 8 ◽  
Author(s):  
Alejandro Melendez-Calderon ◽  
Camila Shirota ◽  
Sivakumar Balasubramanian
Keyword(s):  
Motor Behavior ◽  
Reconstruction Error ◽  
Time Profile ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Movement Smoothness ◽  
Acceleration Data ◽  
Rotational Kinematics ◽  
Measurement Units ◽  
Using Data

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.

scholarly journals Estimating Movement Smoothness from Inertial Measurement Units

2020 ◽  
Author(s):  
Alejandro Melendez-Calderon ◽  
Camila Shirota ◽  
Sivakumar Balasubramanian
Keyword(s):  
Motor Behavior ◽  
Estimation Error ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Movement Smoothness ◽  
Acceleration Data ◽  
Rotational Kinematics ◽  
Measurement Units ◽  
Using Data ◽  
Original Measure

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.

scholarly journals Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach

2016 ◽  
Vol 2 (1) ◽  
pp. 715-718 ◽  
Author(s):  
David Graurock ◽  
Thomas Schauer ◽  
Thomas Seel
Keyword(s):  
Sensor Networks ◽  
Lower Limb ◽  
Healthy Subjects ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Lower Limbs ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Correct Pairing ◽  
Measurement Units

AbstractInertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.

scholarly journals A Comparative Evaluation of Inertial Sensors for Gait and Jump Analysis

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 5990
Author(s):  
Isaia Andrenacci ◽  
Riccardo Boccaccini ◽  
Alice Bolzoni ◽  
Giulio Colavolpe ◽  
Cosimo Costantino ◽  
...  
Keyword(s):  
Standardized Tests ◽  
Healthy Subjects ◽  
Comparative Evaluation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Motor Symptoms ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Physical Tests ◽  
Measurement Units

Gait and jump anomalies are often used as indicators to identify the presence and state of disorders that involve motor symptoms. Physical tests are often performed in specialized laboratories, which offer reliable and accurate results, but require long and costly analyses performed by specialized personnel. The use of inertial sensors for gait and jump evaluation offers an easy-to-use low-cost alternative, potentially applicable by the patients themselves at home. In this paper, we compared three inertial measurement units that are available on the market by means of well-known standardized tests for the evaluation of gait and jump behavior. The aim of the study was to highlight the strengths and weaknesses of each of the tested sensors, considered in different tests, by comparing data collected on two healthy subjects. Data were processed to identify the phases of the movement and the possible inaccuracies of each sensor. The analysis showed that some of the considered inertial units could be reliably used to identify the gait and jump phases and could be employed to detect anomalies, potentially suggesting the presence of disorders.

scholarly journals A Framework for Sensor-based Assessment of Upper-limb Functioning

2021 ◽  
Author(s):  
Ann David ◽  
Tanya Subash ◽  
SKM Varadhan ◽  
Alejandro Melendez-Calderon ◽  
Sivakumar Balasubramanian
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Wearable Sensors ◽  
Clinical Work ◽  
Behavioral Pattern ◽  
Upper Limbs ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Using Data

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.

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