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Sensor Techniques and Methods for Movement Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (10 October 2020) | Viewed by 54835

Special Issue Editor


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Guest Editor
The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
Interests: wearable sensor systems for human motion capture; magneto-inertial measurement units; computational methods for wearable sensor systems; multisensor fusion
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Special Issue Information

Dear Colleagues

We are pleased to invite you to contribute to this Special Issue of Sensors entitled “Sensor Techniques and Methods for Movement Analysis”. The aim of this Special Issue is to bring about contributions concerning the use of sensors, sensing techniques, and methods for quantitatively assessing the movement of body or limbs in humans (and animals). We will accept contributions in the form of either full-length research papers, systematic reviews or papers reporting new results of performance comparison studies. There will be no restriction with regard to the application fields for which we solicit submissions, including biomechanics, rehabilitation, elderly monitoring, sports, healthcare, and robotics.

Prof. Angelo Maria Sabatini
Guest Editor

Manuscript Submission Information

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Keywords

  • Wearable sensors and wearable sensor systems
  • Optical motion capture
  • Gait and balance
  • Movement disorders
  • Multisensor fusion
  • Neurology
  • IMU
  • Inertial Sensor
  • Biomechanics
  • Aged activity monitoring

Published Papers (13 papers)

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14 pages, 670 KiB  
Article
Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone
by Yashi Nan, Nigel H. Lovell, Stephen J. Redmond, Kejia Wang, Kim Delbaere and Kimberley S. van Schooten
Sensors 2020, 20(24), 7195; https://doi.org/10.3390/s20247195 - 15 Dec 2020
Cited by 23 | Viewed by 3431
Abstract
Activity recognition can provide useful information about an older individual’s activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep [...] Read more.
Activity recognition can provide useful information about an older individual’s activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study. Smartphone accelerometry data of free-living activities, performed by 53 older people (83.8 ± 3.8 years; 38 male) under standardized circumstances, were classified into lying, sitting, standing, transition, walking, walking upstairs, and walking downstairs. A 1D CNN, a multichannel CNN, a CNN-LSTM, and a multichannel CNN-LSTM model were tested. The models were compared on accuracy and computational efficiency. Results show that the multichannel CNN-LSTM model achieved the best classification results, with an 81.1% accuracy and an acceptable model and time complexity. Specifically, the accuracy was 67.0% for lying, 70.7% for sitting, 88.4% for standing, 78.2% for transitions, 88.7% for walking, 65.7% for walking downstairs, and 68.7% for walking upstairs. The findings indicated that the multichannel CNN-LSTM model was feasible for smartphone-based activity recognition in older people. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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17 pages, 2421 KiB  
Article
Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform
by Ran Dong, Dongsheng Cai and Soichiro Ikuno
Sensors 2020, 20(22), 6534; https://doi.org/10.3390/s20226534 - 16 Nov 2020
Cited by 17 | Viewed by 4057
Abstract
Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in [...] Read more.
Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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11 pages, 954 KiB  
Article
Analysis of Postural Control in Sitting by Pressure Mapping in Patients with Multiple Sclerosis, Spinal Cord Injury and Friedreich’s Ataxia: A Case Series Study
by María Mercedes Reguera-García, Raquel Leirós-Rodríguez, Lorena Álvarez-Barrio and Beatriz Alonso-Cortés Fradejas
Sensors 2020, 20(22), 6488; https://doi.org/10.3390/s20226488 - 13 Nov 2020
Cited by 3 | Viewed by 3139
Abstract
The postural control assessments in patients with neurological diseases lack reliability and sensitivity to small changes in patient functionality. The appearance of pressure mapping has allowed quantitative evaluation of postural control in sitting. This study was carried out to determine the evaluations in [...] Read more.
The postural control assessments in patients with neurological diseases lack reliability and sensitivity to small changes in patient functionality. The appearance of pressure mapping has allowed quantitative evaluation of postural control in sitting. This study was carried out to determine the evaluations in pressure mapping and verifying whether they are different between the three sample groups (multiple sclerosis, spinal cord injury and Friedreich’s ataxia), and to determine whether the variables extracted from the pressure mapping analysis are more sensitive than functional tests to evaluate the postural trunk control. A case series study was carried out in a sample of 10 adult patients with multiple sclerosis (n = 2), spinal cord injury (n = 4) and Friedreich’s ataxia (n = 4). The tests applied were: pressure mapping, seated Lateral Reach Test, seated Functional Reach Test, Berg Balance Scale, Posture and Postural Ability Scale, Function in Sitting Test, and Trunk Control Test. The participants with Friedreich’s ataxia showed a tendency to present a higher mean pressure on the seat of subject’s wheelchair compared to other groups. In parallel, users with spinal cord injury showed a tendency to present the highest values of maximum pressure and area of contact. People with different neurological pathologies and similar results in functional tests have very different results in the pressure mapping. Although it is not possible to establish a strong statistical correlation, the relationships between the pressure mapping variables and the functional tests seem to be numerous, especially in the multiple sclerosis group. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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15 pages, 2713 KiB  
Article
A Sensor-Based Screening Tool for Identifying High Pelvic Mobility in Patients Due to Undergo Total Hip Arthroplasty
by Xueyang Wang, Arham Qureshi, Abhinav Vepa, Usama Rahman, Arnab Palit, Mark A. Williams, Richard King and Mark T. Elliott
Sensors 2020, 20(21), 6182; https://doi.org/10.3390/s20216182 - 30 Oct 2020
Cited by 5 | Viewed by 3235
Abstract
There is increasing evidence that pelvic mobility is a critical factor to consider in implant alignment during total hip arthroplasty (THA). Here, we test the feasibility of using an inertial sensor fitted across the sacrum to measure change in pelvic tilt, and hence [...] Read more.
There is increasing evidence that pelvic mobility is a critical factor to consider in implant alignment during total hip arthroplasty (THA). Here, we test the feasibility of using an inertial sensor fitted across the sacrum to measure change in pelvic tilt, and hence screen for patients with high pelvic mobility. Patients (n = 32, mean age: 57.4 years) due to receive THA surgery participated in the study. Measures of pelvic tilt were captured simultaneously using the device and radiograph in three functional positions: Standing, flexed-seated, and step-up. We found a strong correlation between the device and radiograph measures for the change in pelvic tilt measure from standing to flexed-seated position (R2 = 0.911); 75% of absolute errors were under 5 degrees. We demonstrated that the device can be used as a screening tool to rapidly identify patients who would benefit from more detailed surgical planning of implant positioning to reduce future risks of impingement and dislocation. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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20 pages, 1589 KiB  
Article
Centre of Pressure Estimation during Walking Using Only Inertial-Measurement Units and End-To-End Statistical Modelling
by Janez Podobnik, David Kraljić, Matjaž Zadravec and Marko Munih
Sensors 2020, 20(21), 6136; https://doi.org/10.3390/s20216136 - 28 Oct 2020
Cited by 7 | Viewed by 3306
Abstract
Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of [...] Read more.
Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3 mm and the average inter-subject RMS error of 23.7 mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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13 pages, 2760 KiB  
Article
Biomechanical Signals of Varied Modality and Location Contribute Differently to Recognition of Transient Locomotion
by Mahdieh Kazemimoghadam and Nicholas P. Fey
Sensors 2020, 20(18), 5390; https://doi.org/10.3390/s20185390 - 21 Sep 2020
Cited by 3 | Viewed by 2569
Abstract
Intent recognition in lower-limb assistive devices typically relies on neuromechanical sensing of an affected limb acquired through embedded device sensors. It remains unknown whether signals from more widespread sources such as the contralateral leg and torso positively influence intent recognition, and how specific [...] Read more.
Intent recognition in lower-limb assistive devices typically relies on neuromechanical sensing of an affected limb acquired through embedded device sensors. It remains unknown whether signals from more widespread sources such as the contralateral leg and torso positively influence intent recognition, and how specific locomotor tasks that place high demands on the neuromuscular system, such as changes of direction, contribute to intent recognition. In this study, we evaluated the performances of signals from varying mechanical modalities (accelerographic, gyroscopic, and joint angles) and locations (the trailing leg, leading leg and torso) during straight walking, changes of direction (cuts), and cuts to stair ascent with varying task anticipation. Biomechanical information from the torso demonstrated poor performance across all conditions. Unilateral (the trailing or leading leg) joint angle data provided the highest accuracy. Surprisingly, neither the fusion of unilateral and torso data nor the combination of multiple signal modalities improved recognition. For these fused modality data, similar trends but with diminished accuracy rates were reported during unanticipated conditions. Finally, for datasets that achieved a relatively accurate (≥90%) recognition of unanticipated tasks, these levels of recognition were achieved after the mid-swing of the trailing/transitioning leg, prior to a subsequent heel strike. These findings suggest that mechanical sensing of the legs and torso for the recognition of straight-line and transient locomotion can be implemented in a relatively flexible manner (i.e., signal modality, and from the leading or trailing legs) and, importantly, suggest that more widespread sensing is not always optimal. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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16 pages, 10669 KiB  
Article
Evaluation of the Validity, Reliability, and Kinematic Characteristics of Multi-Segment Foot Models in Motion Capture
by Yuka Sekiguchi, Takanori Kokubun, Hiroki Hanawa, Hitomi Shono, Ayumi Tsuruta and Naohiko Kanemura
Sensors 2020, 20(16), 4415; https://doi.org/10.3390/s20164415 - 07 Aug 2020
Cited by 4 | Viewed by 3764
Abstract
This study aimed to evaluate the validity and reliability of our new multi-segment foot model by measuring a dummy foot, and examine the kinematic characteristics of our new multi-segment foot model by measuring the living body. Using our new model and the Rizzoli [...] Read more.
This study aimed to evaluate the validity and reliability of our new multi-segment foot model by measuring a dummy foot, and examine the kinematic characteristics of our new multi-segment foot model by measuring the living body. Using our new model and the Rizzoli model, we conducted two experiments with a dummy foot that was moved within a range from −90 to 90 degrees in all planes; for the living body, 24 participants performed calf raises, gait, and drop jumps. Most three-dimensional (3D) rotation angles calculated according to our new models were strongly positively correlated with true values (r > 0.8, p < 0.01). Most 3D rotation angles had fixed biases; however, most of them were in the range of the limits of agreement. Temporal patterns of foot motion, such as those in the Rizzoli model, were observed in our new model during all dynamic tasks. We concluded that our new multi-segment foot model was valid for motion analysis and was useful for analyzing the foot motion using 3D motion capture during dynamic tasks. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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27 pages, 6742 KiB  
Article
Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data
by Una Pale, Manfredo Atzori, Henning Müller and Alessandro Scano
Sensors 2020, 20(15), 4297; https://doi.org/10.3390/s20154297 - 01 Aug 2020
Cited by 24 | Viewed by 4262
Abstract
Background. Muscle synergy analysis is an approach to understand the neurophysiological mechanisms behind the hypothesized ability of the Central Nervous System (CNS) to reduce the dimensionality of muscle control. The muscle synergy approach is also used to evaluate motor recovery and the evolution [...] Read more.
Background. Muscle synergy analysis is an approach to understand the neurophysiological mechanisms behind the hypothesized ability of the Central Nervous System (CNS) to reduce the dimensionality of muscle control. The muscle synergy approach is also used to evaluate motor recovery and the evolution of the patients’ motor performance both in single-session and longitudinal studies. Synergy-based assessments are subject to various sources of variability: natural trial-by-trial variability of performed movements, intrinsic characteristics of subjects that change over time (e.g., recovery, adaptation, exercise, etc.), as well as experimental factors such as different electrode positioning. These sources of variability need to be quantified in order to resolve challenges for the application of muscle synergies in clinical environments. The objective of this study is to analyze the stability and similarity of extracted muscle synergies under the effect of factors that may induce variability, including inter- and intra-session variability within subjects and inter-subject variability differentiation. The analysis was performed using the comprehensive, publicly available hand grasp NinaPro Database, featuring surface electromyography (EMG) measures from two EMG electrode bracelets. Methods. Intra-session, inter-session, and inter-subject synergy stability was analyzed using the following measures: variance accounted for (VAF) and number of synergies (NoS) as measures of reconstruction stability quality and cosine similarity for comparison of spatial composition of extracted synergies. Moreover, an approach based on virtual electrode repositioning was applied to shed light on the influence of electrode position on inter-session synergy similarity. Results. Inter-session synergy similarity was significantly lower with respect to intra-session similarity, both considering coefficient of variation of VAF (approximately 0.2–15% for inter vs. approximately 0.1% to 2.5% for intra, depending on NoS) and coefficient of variation of NoS (approximately 6.5–14.5% for inter vs. approximately 3–3.5% for intra, depending on VAF) as well as synergy similarity (approximately 74–77% for inter vs. approximately 88–94% for intra, depending on the selected VAF). Virtual electrode repositioning revealed that a slightly different electrode position can lower similarity of synergies from the same session and can increase similarity between sessions. Finally, the similarity of inter-subject synergies has no significant difference from the similarity of inter-session synergies (both on average approximately 84–90% depending on selected VAF). Conclusion. Synergy similarity was lower in inter-session conditions with respect to intra-session. This finding should be considered when interpreting results from multi-session assessments. Lastly, electrode positioning might play an important role in the lower similarity of synergies over different sessions. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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15 pages, 2505 KiB  
Article
Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?
by Jucheol Moon, Nelson Hebert Minaya, Nhat Anh Le, Hee-Chan Park and Sang-Il Choi
Sensors 2020, 20(14), 4001; https://doi.org/10.3390/s20144001 - 18 Jul 2020
Cited by 8 | Viewed by 3493
Abstract
Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this [...] Read more.
Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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12 pages, 1973 KiB  
Article
Reliability of Kinovea® Software and Agreement with a Three-Dimensional Motion System for Gait Analysis in Healthy Subjects
by Pilar Fernández-González, Aikaterini Koutsou, Alicia Cuesta-Gómez, María Carratalá-Tejada, Juan Carlos Miangolarra-Page and Francisco Molina-Rueda
Sensors 2020, 20(11), 3154; https://doi.org/10.3390/s20113154 - 02 Jun 2020
Cited by 66 | Viewed by 11534
Abstract
Gait analysis is necessary to diagnose movement disorders. In order to reduce the costs of three-dimensional motion capture systems, new low-cost methods of motion analysis have been developed. The purpose of this study was to evaluate the inter- and intra-rater reliability of Kinovea [...] Read more.
Gait analysis is necessary to diagnose movement disorders. In order to reduce the costs of three-dimensional motion capture systems, new low-cost methods of motion analysis have been developed. The purpose of this study was to evaluate the inter- and intra-rater reliability of Kinovea® and the agreement with a three-dimensional motion system for detecting the joint angles of the hip, knee and ankle during the initial contact phase of walking. Fifty healthy subjects participated in this study. All participants were examined twice with a one-week interval between the two appointments. The motion data were recorded using the VICON Motion System® and digital video cameras. The intra-rater reliability showed a good correlation for the hip, the knee and the ankle joints (Intraclass Correlation Coefficient, ICC > 0.85) for both observers. The ICC for the inter-rater reliability was >0.90 for the hip, the knee and the ankle joints. The Bland–Altman plots showed that the magnitude of disagreement was approximately ±5° for intra-rater reliability, ±2.5° for inter-rater reliability and around ±2.5° to ±5° for Kinovea® versus Vicon®. The ICC was good for the hip, knee and ankle angles registered with Kinovea® during the initial contact of walking for both observers (intra-rater reliability) and higher for the agreement between observers (inter-rater reliability). However, the Bland–Altman plots showed disagreement between observers, measurements and systems (Kinovea® vs. three-dimensional motion system) that should be considered in the interpretation of clinical evaluations. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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12 pages, 1969 KiB  
Article
Impact of Visual Biofeedback of Trunk Sway Smoothness on Motor Learning during Unipedal Stance
by Carlos Cruz-Montecinos, Antonio Cuesta-Vargas, Cristian Muñoz, Dante Flores, Joseph Ellsworth, Carlos De la Fuente, Joaquín Calatayud, Gonzalo Rivera-Lillo, Verónica Soto-Arellano, Claudio Tapia and Xavier García-Massó
Sensors 2020, 20(9), 2585; https://doi.org/10.3390/s20092585 - 01 May 2020
Cited by 6 | Viewed by 3491
Abstract
The assessment of trunk sway smoothness using an accelerometer sensor embedded in a smartphone could be a biomarker for tracking motor learning. This study aimed to determine the reliability of trunk sway smoothness and the effect of visual biofeedback of sway smoothness on [...] Read more.
The assessment of trunk sway smoothness using an accelerometer sensor embedded in a smartphone could be a biomarker for tracking motor learning. This study aimed to determine the reliability of trunk sway smoothness and the effect of visual biofeedback of sway smoothness on motor learning in healthy people during unipedal stance training using an iPhone 5 measurement system. In the first experiment, trunk sway smoothness in the reliability group (n = 11) was assessed on two days, separated by one week. In the second, the biofeedback group (n = 12) and no-biofeedback group (n = 12) were compared during 7 days of unipedal stance test training and one more day of retention (without biofeedback). The intraclass correlation coefficient score 0.98 (0.93–0.99) showed that this method has excellent test–retest reliability. Based on the power law of practice, the biofeedback group showed greater improvement during training days (p = 0.003). Two-way mixed analysis of variance indicates a significant difference between groups (p < 0.001) and between days (p < 0.001), as well as significant interaction (p < 0.001). Post hoc analysis shows better performance in the biofeedback group from training days 2 and 7, as well as on the retention day (p < 0.001). Motor learning objectification through visual biofeedback of trunk sway smoothness enhances postural control learning and is useful and reliable for assessing motor learning. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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11 pages, 809 KiB  
Article
Percentiles and Reference Values for the Accelerometric Assessment of Static Balance in Women Aged 50–80 Years
by Raquel Leirós-Rodríguez, Vicente Romo-Pérez, Jose Luis García-Soidán and Jesús García-Liñeira
Sensors 2020, 20(3), 940; https://doi.org/10.3390/s20030940 - 10 Feb 2020
Cited by 17 | Viewed by 3100
Abstract
The identification of factors that alter postural stability is fundamental in the design of interventions to maintain independence and mobility. This is especially important for women because of their longer life expectancy and higher incidence of falls compared to men. The objective of [...] Read more.
The identification of factors that alter postural stability is fundamental in the design of interventions to maintain independence and mobility. This is especially important for women because of their longer life expectancy and higher incidence of falls compared to men. The objective of this study was to construct the percentile box charts and determine the values of reference for the accelerometric assessment of the static balance in women. For this, an observational and cross-sectional study with a sample composed of 496 women (68.8 ± 10.4 years old) was conducted. The measurement of accelerations used a triaxial accelerometer during three tests: two tests on the ground in monopodal support and a test on a mat with monopodal support for 30 s each. In all of the variables, an increase in the magnitude of the accelerations was detected as the age advanced. The box charts of the percentiles of the tests show the amplitude of the interquartile ranges, which increased as the age advanced. The values obtained can be used to assess changes in static balance due to aging, trauma and orthopaedic and neurodegenerative alterations that may alter postural stability and increase the risk of falling. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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9 pages, 644 KiB  
Letter
Mechanical Properties of Treadmill Surfaces Compared to Other Overground Sport Surfaces
by Enrique Colino, Jose Luis Felipe, Bas Van Hooren, Leonor Gallardo, Kenneth Meijer, Alejandro Lucia, Jorge Lopez-Fernandez and Jorge Garcia-Unanue
Sensors 2020, 20(14), 3822; https://doi.org/10.3390/s20143822 - 09 Jul 2020
Cited by 14 | Viewed by 4303
Abstract
The mechanical properties of the surfaces used for exercising can affect sports performance and injury risk. However, the mechanical properties of treadmill surfaces remain largely unknown. The aim of this study was, therefore, to assess the shock absorption (SA), vertical deformation (VD) and [...] Read more.
The mechanical properties of the surfaces used for exercising can affect sports performance and injury risk. However, the mechanical properties of treadmill surfaces remain largely unknown. The aim of this study was, therefore, to assess the shock absorption (SA), vertical deformation (VD) and energy restitution (ER) of different treadmill models and to compare them with those of other sport surfaces. A total of 77 treadmills, 30 artificial turf pitches and 30 athletics tracks were assessed using an advanced artificial athlete device. Differences in the mechanical properties between the surfaces and treadmill models were evaluated using a repeated-measures ANOVA. The treadmills were found to exhibit the highest SA of all the surfaces (64.2 ± 2; p < 0.01; effect size (ES) = 0.96), while their VD (7.6 ± 1.3; p < 0.01; ES = 0.87) and ER (45 ± 11; p < 0.01; ES = 0.51) were between the VDs of the artificial turf and track. The SA (p < 0.01; ES = 0.69), VD (p < 0.01; ES = 0.90) and ER (p < 0.01; ES = 0.89) were also shown to differ between treadmill models. The differences between the treadmills commonly used in fitness centers were much lower than differences between the treadmills and track surfaces, but they were sometimes larger than the differences with artificial turf. The treadmills used in clinical practice and research were shown to exhibit widely varying mechanical properties. The results of this study demonstrate that the mechanical properties (SA, VD and ER) of treadmill surfaces differ significantly from those of overground sport surfaces such as artificial turf and athletics track surfaces but also asphalt or concrete. These different mechanical properties of treadmills may affect treadmill running performance, injury risk and the generalizability of research performed on treadmills to overground locomotion. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Movement Analysis)
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