Special Issue "Recent Advances in Motion Analysis"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 August 2020).

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A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Francesco Di Nardo
E-Mail Website
Guest Editor
Department of Information Engineering, Marche Polytechnic University, 60100 Ancona, Italy
Interests: EMG signal processing (filtering, feature extraction, pattern recognition, time–frequency analysis) and interpretation (physiology, clinics, sport); gait analysis; static and perturbed posturography; machine learning applications in motion analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Sandro Fioretti
E-Mail Website
Guest Editor
Department of Information Engineering,Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: stereophotogrammetry; linear and nonlinear filtering; joint kinematics; analysis and identification of postural control; static and perturbed posturography; gait analysis; dynamic electromyography; wearable devices for motion analysis

Special Issue Information

Dear Colleagues,

The advances in the technology and methodology for human movement capture and analysis have been remarkable over the last decade. Besides acknowledged approaches for kinematic, dynamic, and EMG analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Thus, the synergy of classic instrumentation and novel smart devices has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, recognition, characterization, and interpretation of motion metrics and behaviors from sensor data are representing a challenging problem, not only in laboratories, but also at home and in the community. 

This Special Issue is designed to comprehensively cover the open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application (clinics, sports, ergonomics, etc.). Computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, contributions in this field are also welcome. This Special Issue will consider original research, reviews, and applications in clinics, sports, and ergonomics. Areas of interest include, but are not limited to, the following: 

  • Recent advances in kinematic and dynamic analysis
  • Recent advances in EMG signal processing
  • Recent advances in static and dynamic posturography
  • Recent advances in gait analysis
  • Wearables and inertial measurement units in motion analysis
  • Artificial neural networks for motion analysis

Dr. Francesco Di Nardo
Prof. Dr. Sandro Fioretti
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Motion analysis
  • Movement biomechanics
  • Electromyography (EMG)
  • Inertial measurement units (IMU)
  • Gait analysis
  • Static and perturbed posture
  • Machine learning applications in motion analysis

Published Papers (11 papers)

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Research

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Article
Rate-Invariant Modeling in Lie Algebra for Activity Recognition
Electronics 2020, 9(11), 1888; https://doi.org/10.3390/electronics9111888 - 10 Nov 2020
Cited by 1 | Viewed by 620
Abstract
Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. [...] Read more.
Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)××SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
A Statistical Approach for the Assessment of Muscle Activation Patterns during Gait in Parkinson’s Disease
Electronics 2020, 9(10), 1641; https://doi.org/10.3390/electronics9101641 - 05 Oct 2020
Cited by 1 | Viewed by 862
Abstract
Recently, the statistical analysis of muscle activation patterns highlighted that not only one, but several activation patterns can be identified in the gait of healthy adults, with different occurrence. Although its potential, the application of this approach in pathological populations is still limited [...] Read more.
Recently, the statistical analysis of muscle activation patterns highlighted that not only one, but several activation patterns can be identified in the gait of healthy adults, with different occurrence. Although its potential, the application of this approach in pathological populations is still limited and specific implementation issues need to be addressed. This study aims at applying a statistical approach to analyze muscle activation patterns of gait in Parkinson’s Disease, integrating gait symmetry and co-activation. Surface electromyographic signal of tibialis anterior and gastrocnemius medialis were recorded during a 6-min walking test in 20 patients. Symmetry between right and left stride time series was verified, different activation patterns identified, and their occurrence (number and timing) quantified, as well as the co-activation of antagonist muscles. Gastrocnemius medialis presented five activation patterns (mean occurrence ranging from 2% to 43%) showing, with respect to healthy adults, the presence of a first shorted and delayed activation (between flat foot contact and push off, and in the final swing) and highlighting a new second region of anticipated activation (during early/mid swing). Tibialis anterior presented five activation patterns (mean occurrence ranging from 3% to 40%) highlighting absent or delayed activity at the beginning of the gait cycle, and generally shorter and anticipated activations during the swing phase with respect to healthy adults. Three regions of co-contraction were identified: from heel strike to mid-stance, from the pre- to initial swing, and during late swing. This study provided a novel insight in the analysis of muscle activation patterns in Parkinson’s Disease patients with respect to the literature, where unique, at times conflicting, average patterns were reported. The proposed integrated methodology is meant to be generalized for the analysis of muscle activation patterns in pathologic subjects. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
Variability of Muscular Recruitment in Hemiplegic Walking Assessed by EMG Analysis
Electronics 2020, 9(10), 1572; https://doi.org/10.3390/electronics9101572 - 25 Sep 2020
Viewed by 600
Abstract
Adaptive variability during walking is typical of child motor development. It has been reported that neurological disorders could affect this physiological phenomenon. The present work is designed to assess the adaptive variability of muscular recruitment during hemiplegic walking and to detect possible changes [...] Read more.
Adaptive variability during walking is typical of child motor development. It has been reported that neurological disorders could affect this physiological phenomenon. The present work is designed to assess the adaptive variability of muscular recruitment during hemiplegic walking and to detect possible changes compared to control populations. In the attempt of limiting the complexity of computational procedure, the easy-to-measure coefficient of variation (CV) index is adopted to assess surface electromyography (sEMG) variability. The target population includes 34 Winters’ type I and II hemiplegic children (H-group). Two further healthy populations, 34 age-matched children (C-group) and 34 young adults (A-group), are involved as controls. Results show a significant decrease (p < 0.05) of mean CV for gastrocnemius lateralis (GL) in H-group compared to both C-group (15% reduction) and A-group (35% reduction). Reductions of mean CV are detected also for tibialis anterior (TA) in H-group compared to C-group (7% reduction, p > 0.05) and A-group (15% reduction, p < 0.05). Lower CVs indicate a decreased intra-subject variability of ankle-muscle activity compared to controls. Novel contribution of the study is twofold: (1) To propose a CV-based approach for an easy-to-compute assessment of sEMG variability in hemiplegic children, useful in different experimental environments and different clinical purposes; (2) to provide a quantitative assessment of the reduction of intra-subject variability of ankle-muscle activity in mild-hemiplegic children compared to controls (children and adults), suggesting that hemiplegic children present a limited capability of adapting their muscle recruitment to the different stimuli met during walking task. This finding could be very useful in deepening the knowledge of this neurological disorder. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
A Wireless Body Sensor Network for Clinical Assessment of the Flexion-Relaxation Phenomenon
Electronics 2020, 9(6), 1044; https://doi.org/10.3390/electronics9061044 - 24 Jun 2020
Cited by 4 | Viewed by 1749
Abstract
An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon [...] Read more.
An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
The Influence of Different Levels of Cognitive Engagement on the Seated Postural Sway
Electronics 2020, 9(4), 601; https://doi.org/10.3390/electronics9040601 - 31 Mar 2020
Viewed by 1135
Abstract
In this paper, we introduced and tested a new system based on a sensorized seat, to evaluate the sitting dynamics and sway alterations caused by different cognitive engagement conditions. An office chair was equipped with load cells, and a digital and software interface [...] Read more.
In this paper, we introduced and tested a new system based on a sensorized seat, to evaluate the sitting dynamics and sway alterations caused by different cognitive engagement conditions. An office chair was equipped with load cells, and a digital and software interface was developed to extract the Center of Pressure (COP). A population of volunteers was recruited to evaluate alterations to their seated posture when undergoing a test specifically designed to increase the cognitive engagement and the level of stress. Relevant parameters of postural sway were extracted from the COP data, and significant alterations were found in all of them, highlighting the ability of the system to capture the emergence of a different dynamic behavior in postural control when increasing the complexity of the cognitive engagement. The presented system can thus be used as a valid and reliable instrument to monitor the postural patterns of subjects involved in tasks performed in a seated posture, and this may prove useful for a variety of applications, including those associated with improving the quality of working conditions. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
People Walking Classification Using Automotive Radar
Electronics 2020, 9(4), 588; https://doi.org/10.3390/electronics9040588 - 30 Mar 2020
Cited by 8 | Viewed by 1358
Abstract
Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of [...] Read more.
Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach
Electronics 2020, 9(2), 355; https://doi.org/10.3390/electronics9020355 - 20 Feb 2020
Cited by 6 | Viewed by 1172
Abstract
Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per [...] Read more.
Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis
Electronics 2020, 9(1), 43; https://doi.org/10.3390/electronics9010043 - 28 Dec 2019
Cited by 8 | Viewed by 1207
Abstract
To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults have been measured. After processing the experimental [...] Read more.
To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults have been measured. After processing the experimental data, the estimation model for knee movement from sEMG was established using the novel method of random forest with principal component analysis (RFPCA). The influence of the sample size and the previous sEMG data on the prediction efficiency was analyzed. The estimation model was not sensitive to the sample size when samples increased to a certain value, and the results of different previous sEMG showed that the prediction accuracy of the estimation models did not always improve with the increasing features of input. By comparing the estimation model of back propagation neural network with principal component analysis (BPPCA), it was found that RFPCA was suitable for all participants in the experiment with less execution time, and the root mean square error was around 5° which was lower than BPPCA with errors varying from 7° to 25°. Therefore, it was concluded that the RFPCA method for the estimation of knee movement from sEMG is feasible and could be used for motion analysis and the control of exoskeleton. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors
Electronics 2019, 8(11), 1304; https://doi.org/10.3390/electronics8111304 - 07 Nov 2019
Cited by 5 | Viewed by 1570
Abstract
Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into [...] Read more.
Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Article
Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations
Electronics 2019, 8(10), 1083; https://doi.org/10.3390/electronics8101083 - 24 Sep 2019
Cited by 10 | Viewed by 1457
Abstract
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. [...] Read more.
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Review

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Review
Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis
Electronics 2020, 9(9), 1357; https://doi.org/10.3390/electronics9091357 - 21 Aug 2020
Cited by 3 | Viewed by 1430
Abstract
The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality [...] Read more.
The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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