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Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 56160

Special Issue Editor

Prof. Dr. Wolfgang Potthast

E-Mail Website
Guest Editor
Institute of Biomechanics and Orthopaedics, Clinical and Technological Biomechanics, German Sport University Cologne, Cologne, Germany
Interests: biomechanics; sports biomechanics; sports technology; clinical biomechanics; sports injuries; rehabilitation; sports science; posture; exercise science; movement analysis; musculoskeletal disorders; muscle function; footwear biomechanics

Special Issue Information

Dear Colleagues,

Wearable technology in movement science is not only a promising future field of technological development but, in fact, already surrounding top elite and recreational athletes on an everyday basis. Wearable technology is used to analyze current levels of performance, deduce recommendations for training, control and monitor training effects and performance, estimate injury risks and identify the effects of therapeutic or training-related interventions or technological interventions.

Wearable technology for analyzing human movement can range from simple step counters, sensors tracking human activities, recording walking and running distances, tracking running speed, etc., to more advanced biomechanical sensors such as inertial measurement units (IMUs), which determine joint angles and their changes over time. Force and pressure sensors are used for a variety of different diagnostic purposes and, together with IMUs, for estimating body internal loading on the musculoskeletal system.

Metabolic and exercise physiology-related sensor technology has spread from a traditionally laboratory-based domain to more scenarios, where the related parameters are measured and analyzed in real time during the actual activity in the field. Lactate concentrations, water contents, glucose concentrations and other biomarkers are analyzed based on the characteristics of sweat or other sources by noninvasive or minimally invasive detection. Related technological developments are currently being pushed by powerful interdisciplinary international research and development consortia. Their advances will serve recreational and top athletes in the near future.

In all fields of wearable technology, the accuracy and validity of the sensors are key quality factors, but there are huge challenges in miniaturization, and problems with energy supply and exposure to physical influences such as vibrations, humidity, temperature changes or impacts. Research developments need to address those issues and find solutions. The use of machine learning and artificial intelligence is one approach.

The fusion of wearable technology for physiological–metabolic sensors and devices for biomechanical–movement analysis will allow for comprehensive analyses of human movement, performance potential and also injury risk as well as health status. A comprehensive analysis of human athletic activities cannot be performed with biomechanics or physiology in isolation but must integrate both fields of research. This Special Issue on “Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology” will address both aspects to provide insights into current developments, opportunities and challenges.

Prof. Dr. Wolfgang Potthast
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • biomechanics
  • sports injuries
  • physiology
  • exercise physiology
  • health
  • public health
  • sport biomechanics
  • sports technology
  • rehabilitation
  • sports science
  • posture
  • exercise science
  • movement analysis
  • musculoskeletal disorders
  • muscle function
  • wearable sensors

Published Papers (17 papers)

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14 pages, 11289 KiB  
Article
Location Matters—Can a Smart Golf Club Detect Where the Club Face Hits the Ball?
by Bernhard Hollaus, Yannic Heyer, Johannes Steiner and Gerda Strutzenberger
Sensors 2023, 23(24), 9783; https://0-doi-org.brum.beds.ac.uk/10.3390/s23249783 - 12 Dec 2023
Viewed by 834
Abstract
In golf, the location of the impact, where the clubhead hits the ball, is of imperative nature for a successful ballflight. Direct feedback to the athlete where he/she hits the ball could improve a practice session. Currently, this information can be measured via, [...] Read more.
In golf, the location of the impact, where the clubhead hits the ball, is of imperative nature for a successful ballflight. Direct feedback to the athlete where he/she hits the ball could improve a practice session. Currently, this information can be measured via, e.g., dual laser technology; however, this is a stationary and external method. A mobile measurement method would give athletes the freedom to gain the information of the impact location without the limitation to be stationary. Therefore, the aim of this study was to investigate whether it is possible to detect the impact location via a motion sensor mounted on the shaft of the golf club. To answer the question, an experiment was carried out. Within the experiment data were gathered from one athlete performing 282 golf swings with an 7 iron. The impact location was recorded and labeled during each swing with a Trackman providing the classes for a neural network. Simultaneously, the motion of the golf club was gathered with an IMU from the Noraxon Ultium Motion Series. In the next step, a neural network was designed and trained to estimate the impact location class based on the motion data. Based on the motion data, a classification accuracy of 93.8% could be achieved with a ResNet architecture. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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14 pages, 981 KiB  
Article
Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression
by Mengsi Wang, Zhenlei Chen, Haoran Zhan, Jiyu Zhang, Xinglong Wu, Dan Jiang and Qing Guo
Sensors 2023, 23(23), 9576; https://0-doi-org.brum.beds.ac.uk/10.3390/s23239576 - 02 Dec 2023
Viewed by 854
Abstract
The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine [...] Read more.
The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below 15%, most Pearson correlation coefficient (R) values exceed 0.85, and most decisive factor (R2) values surpass 0.75. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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19 pages, 2464 KiB  
Article
The Reliability and Validity of a Portable Three-Dimensional Scanning System to Measure Leg Volume
by Jack Ashby, Martin Lewis, Caroline Sunderland, Laura A. Barrett and John G. Morris
Sensors 2023, 23(22), 9177; https://0-doi-org.brum.beds.ac.uk/10.3390/s23229177 - 14 Nov 2023
Viewed by 816
Abstract
(1) Background: The study examined the reliability (test–retest, intra- and inter-day) and validity of a portable 3D scanning method when quantifying human leg volume. (2) Methods: Fifteen males volunteered to participate (age, 24.6 ± 2.0 years; stature, 178.9 ± 4.5 cm; body mass, [...] Read more.
(1) Background: The study examined the reliability (test–retest, intra- and inter-day) and validity of a portable 3D scanning method when quantifying human leg volume. (2) Methods: Fifteen males volunteered to participate (age, 24.6 ± 2.0 years; stature, 178.9 ± 4.5 cm; body mass, 77.4 ± 6.5 kg; mean ± standard deviation). The volume of the lower and upper legs was examined using a water displacement method (the criterion) and two consecutive 3D scans. Measurements were taken at baseline, 1 h post-baseline (intra-day) and 24 h post-baseline (inter-day). Reliability and validity of the 3D scanning method was assessed using Bland–Altman limits of agreement and Pearson’s product moment correlations. (3) Results: With respect to the test–retest reliability, the 3D scanning method had smaller systematic bias and narrower limits of agreement (±1%, and 3–5%, respectively) compared to the water displacement method (1–2% and 4–7%, respectively), when measuring lower and upper leg volume in humans. The correlation coefficients for all reliability comparisons (test–retest, intra-day, inter-day) would all be regarded as ‘very strong’ (all 0.94 or greater). (4) Conclusions: The study’s results suggest that a 3D scanning method is a reliable and valid method to quantify leg volume. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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12 pages, 2440 KiB  
Article
Full-Body Kinematics and Vertical Ground Reaction Forces in Elite Ten-Pin Bowling: A Field Study
by Bo Eitel Seiferheld, Jeppe Frost, Thorstein Brynildsen Østergaard, Mathias Sønder Krog, Kent Kongsøre Klitgaard and Mark de Zee
Sensors 2023, 23(19), 8284; https://0-doi-org.brum.beds.ac.uk/10.3390/s23198284 - 07 Oct 2023
Cited by 1 | Viewed by 9374
Abstract
The purpose was to investigate full-body kinematics and vertical ground reaction forces in the lower extremities of the delivery and to determine delivery changes over time after many deliveries in ten-pin bowling. Six male elite ten-pin bowlers completed six bouts of twelve bowling [...] Read more.
The purpose was to investigate full-body kinematics and vertical ground reaction forces in the lower extremities of the delivery and to determine delivery changes over time after many deliveries in ten-pin bowling. Six male elite ten-pin bowlers completed six bouts of twelve bowling deliveries, all strike attempts, while measuring full-body kinematics and vertical ground reaction forces. Full-body joint angles, peak vertical ground reaction forces in the feet, vertical breaking impulse, centre of mass velocity, bowling score, and ball release velocity (BRvel) were measured. Results revealed that the BRvel was significantly decreased over bouts (p < 0.001). Additionally, increased flexion of the dominant wrist (p < 0.001) and elbow (p = 0.004) prior to ball release (BR) and increased pronation of the dominant wrist during BR (p = 0.034) were observed at later bouts. It was concluded that these kinematic changes in the dominant wrist and elbow prior to and during BR were performed to compensate for the change in traction between ball and lane during a bowling match. This, in turn, caused a decrease in BRvel. A conservation of energy perspective was discussed to highlight training applications and possibilities to enhance elite athletes’ bowling performance. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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14 pages, 2796 KiB  
Article
A Wearable Multi-Modal Digital Upper Limb Assessment System for Automatic Musculoskeletal Risk Evaluation
by Abdullah Tahir, Shaoping Bai and Ming Shen
Sensors 2023, 23(10), 4863; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104863 - 18 May 2023
Cited by 3 | Viewed by 1630
Abstract
Continuous ergonomic risk assessment of the human body is critical to avoid various musculoskeletal disorders (MSDs) for people involved in physical jobs. This paper presents a digital upper limb assessment (DULA) system that automatically performs rapid upper limb assessment (RULA) in real-time for [...] Read more.
Continuous ergonomic risk assessment of the human body is critical to avoid various musculoskeletal disorders (MSDs) for people involved in physical jobs. This paper presents a digital upper limb assessment (DULA) system that automatically performs rapid upper limb assessment (RULA) in real-time for the timely intervention and prevention of MSDs. While existing approaches require human resources for computing the RULA score, which is highly subjective and untimely, the proposed DULA achieves automatic and objective assessment of musculoskeletal risks using a wireless sensor band embedded with multi-modal sensors. The system continuously tracks and records upper limb movements and muscle activation levels and automatically generates musculoskeletal risk levels. Moreover, it stores the data in a cloud database for in-depth analysis by a healthcare expert. Limb movements and muscle fatigue levels can also be visually seen using any tablet/computer in real-time. In the paper, algorithms of robust limb motion detection are developed, and an explanation of the system is provided along with the presentation of preliminary results, which validate the effectiveness of the new technology. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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11 pages, 2445 KiB  
Article
A New Approach to Quantifying Muscular Fatigue Using Wearable EMG Sensors during Surgery: An Ergonomic Case Study
by Johan Merbah, Bertrand R. Caré, Philippe Gorce, François Gadea and François Prince
Sensors 2023, 23(3), 1686; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031686 - 03 Feb 2023
Cited by 2 | Viewed by 2363
Abstract
(1) Background: Surgeons are exposed to musculoskeletal loads that are comparable to those of industrial workers. These stresses are harmful for the joints and muscles and can lead to musculoskeletal disorders (MSD) and working incapacity for surgeons. In this paper, we propose a [...] Read more.
(1) Background: Surgeons are exposed to musculoskeletal loads that are comparable to those of industrial workers. These stresses are harmful for the joints and muscles and can lead to musculoskeletal disorders (MSD) and working incapacity for surgeons. In this paper, we propose a novel ergonomic and visualization approach to assess muscular fatigue during surgical procedures. (2) Methods: The activity of eight muscles from the shoulder girdle and the cervical/lumbar spines were evaluated using position and electromyographic wearable sensors while a surgeon performed an arthroscopic rotator-cuff surgery on a patient. The time and frequency-domain variables of the root-mean-square amplitude and mean power frequency, respectively, were calculated from an electromyographic signal. (3) Results: The entire surgical procedure lasted 73 min and was divided into 10 sub-phases associated with specific level of muscular activity and fatigue. Most of the muscles showed activity above 60%, while the middle trapezius muscles were almost constantly activated (>20%) throughout the surgical procedure. (4) Conclusion: Wearable sensors can be used during surgical procedure to assess fatigue. Periods of low-to-high activity and fatigue can be evaluated and visualized during surgery. Micro-breaks throughout surgical procedures are suggested to avoid fatigue and to prevent the risk of developing MSD. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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11 pages, 1068 KiB  
Article
Reliability of Running Stability during Treadmill and Overground Running
by Dominik Fohrmann, Daniel Hamacher, Alberto Sanchez-Alvarado, Wolfgang Potthast, Patrick Mai, Steffen Willwacher and Karsten Hollander
Sensors 2023, 23(1), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010347 - 29 Dec 2022
Cited by 4 | Viewed by 1694
Abstract
Running stability is the ability to withstand naturally occurring minor perturbations during running. It is susceptible to external and internal running conditions such as footwear or fatigue. However, both its reliable measurability and the extent to which laboratory measurements reflect outdoor running remain [...] Read more.
Running stability is the ability to withstand naturally occurring minor perturbations during running. It is susceptible to external and internal running conditions such as footwear or fatigue. However, both its reliable measurability and the extent to which laboratory measurements reflect outdoor running remain unclear. This study aimed to evaluate the intra- and inter-day reliability of the running stability as well as the comparability of different laboratory and outdoor conditions. Competitive runners completed runs on a motorized treadmill in a research laboratory and overground both indoors and outdoors. Running stability was determined as the maximum short-term divergence exponent from the raw gyroscope signals of wearable sensors mounted to four different body locations (sternum, sacrum, tibia, and foot). Sacrum sensor measurements demonstrated the highest reliabilities (good to excellent; ICC = 0.85 to 0.91), while those of the tibia measurements showed the lowest (moderate to good; ICC = 0.55 to 0.89). Treadmill measurements depicted systematically lower values than both overground conditions for all sensor locations (relative bias = −9.8% to −2.9%). The two overground conditions, however, showed high agreement (relative bias = −0.3% to 0.5%; relative limits of agreement = 9.2% to 15.4%). Our results imply moderate to excellent reliability for both overground and treadmill running, which is the foundation of further research on running stability. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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15 pages, 2883 KiB  
Article
Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose
by Marion Mundt, Zachery Born, Molly Goldacre and Jacqueline Alderson
Sensors 2023, 23(1), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010078 - 21 Dec 2022
Cited by 7 | Viewed by 4715
Abstract
The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by [...] Read more.
The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source pose estimation models (AlphaPose, BlazePose, and OpenPose) across varying movements, camera views, and trial lengths. Second, this study aimed to assess the suitability and interchangeability of keypoints detected by each pose estimation model when used as inputs into machine learning models for the estimation of GRFs. The keypoint detection rate of BlazePose was distinctly lower than that of AlphaPose and OpenPose. All pose estimation models achieved a high keypoint detection rate at the centre of an image frame and a lower detection rate in the true sagittal plane camera field of view, compared with slightly anteriorly or posteriorly located quasi-sagittal plane camera views. The three-dimensional ground reaction force, instantaneous loading rate, and peak force for running could be estimated using the keypoints of all three pose estimation models. However, only AlphaPose and OpenPose keypoints could be used interchangeably with a machine learning model trained to estimate GRFs based on AlphaPose keypoints resulting in a high estimation accuracy when OpenPose keypoints were used as inputs and vice versa. The findings of this study highlight the need for further evaluation of computer vision-based pose estimation models for application in biomechanical human modelling, and the limitations of machine learning-based GRF estimation models that rely on 2D keypoints. This is of particular relevance given that machine learning models informing athlete monitoring guidelines are being developed for application related to athlete well-being. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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12 pages, 58509 KiB  
Article
Dynamical Synergies of Multidigit Hand Prehension
by Dingyi Pei, Parthan Olikkal, Tülay Adali and Ramana Vinjamuri
Sensors 2022, 22(11), 4177; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114177 - 31 May 2022
Cited by 2 | Viewed by 1897
Abstract
Hand prehension requires highly coordinated control of contact forces. The high-dimensional sensorimotor system of the human hand operates at ease, but poses several challenges when replicated in artificial hands. This paper investigates how the dynamical synergies, coordinated spatiotemporal patterns of contact forces, contribute [...] Read more.
Hand prehension requires highly coordinated control of contact forces. The high-dimensional sensorimotor system of the human hand operates at ease, but poses several challenges when replicated in artificial hands. This paper investigates how the dynamical synergies, coordinated spatiotemporal patterns of contact forces, contribute to the hand grasp, and whether they could potentially capture the force primitives in a low-dimensional space. Ten right-handed subjects were recruited to grasp and hold mass-varied objects. The contact forces during this multidigit prehension were recorded using an instrumented grip glove. The dynamical synergies were derived using principal component analysis (PCA). The contact force patterns during the grasps were reconstructed using the first few synergies. The significance of the dynamical synergies, the influence of load forces and task configurations on the synergies were explained. This study also discussed the contribution of biomechanical constraints on the first few synergies and the current challenges and possible applications of the dynamical synergies in the design and control of exoskeletons. The integration of the dynamical synergies into exoskeletons will be realized in the near future. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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15 pages, 12552 KiB  
Article
Detection of Typical Compensatory Movements during Autonomously Performed Exercises Preventing Low Back Pain (LBP)
by Asaad Sellmann, Désirée Wagner, Lucas Holtz, Jörg Eschweiler, Christian Diers, Sybele Williams and Catherine Disselhorst-Klug
Sensors 2022, 22(1), 111; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010111 - 24 Dec 2021
Cited by 5 | Viewed by 2582
Abstract
With the growing number of people seeking medical advice due to low back pain (LBP), individualised physiotherapeutic rehabilitation is becoming increasingly relevant. Thirty volunteers were asked to perform three typical LBP rehabilitation exercises (Prone-Rocking, Bird-Dog and Rowing) in two categories: clinically prescribed exercise [...] Read more.
With the growing number of people seeking medical advice due to low back pain (LBP), individualised physiotherapeutic rehabilitation is becoming increasingly relevant. Thirty volunteers were asked to perform three typical LBP rehabilitation exercises (Prone-Rocking, Bird-Dog and Rowing) in two categories: clinically prescribed exercise (CPE) and typical compensatory movement (TCM). Three inertial sensors were used to detect the movement of the back during exercise performance and thus generate a dataset that is used to develop an algorithm that detects typical compensatory movements in autonomously performed LBP exercises. The best feature combinations out of 50 derived features displaying the highest capacity to differentiate between CPE and TCM in each exercise were determined. For classifying exercise movements as CPE or TCM, a binary decision tree was trained with the best performing features. The results showed that the trained classifier is able to distinguish CPE from TCM in Bird-Dog, Prone-Rocking and Rowing with up to 97.7% (Head Sensor, one feature), 98.9% (Upper back Sensor, one feature) and 80.5% (Upper back Sensor, two features) using only one sensor. Thus, as a proof-of-concept, the introduced classification models can be used to detect typical compensatory movements in autonomously performed LBP exercises. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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14 pages, 2670 KiB  
Communication
Validation of a Novel Boxing Monitoring System to Detect and Analyse the Centre of Pressure Movement on the Boxer’s Fist
by Tobias Menzel and Wolfgang Potthast
Sensors 2021, 21(24), 8394; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248394 - 16 Dec 2021
Cited by 8 | Viewed by 2779
Abstract
The examination of force distribution and centre of pressure (CoP) displacement is a common method to analyse motion, load, and load distribution in biomechanical research. In contrast to gait analysis, the force progression in boxing punches is a new field of investigation. The [...] Read more.
The examination of force distribution and centre of pressure (CoP) displacement is a common method to analyse motion, load, and load distribution in biomechanical research. In contrast to gait analysis, the force progression in boxing punches is a new field of investigation. The centre of pressure displacement and distribution of forces on the surface of the fist during a boxing punch is of great interest and crucial to understanding the effect of the punch on the biological structures of the hand as well as the technical biomechanical aspects of the punching action. This paper presents a new method to display the CoP progression on the boxer’s fist Therefore, this study presents the validation of the developed novel boxing monitoring system in terms of CoP displacement. In addition, the CoP progression of different punching techniques in boxing is analysed on the athlete’s fist. The accuracy of the examination method of the CoP course was validated against the gold standard of a Kistler force plate. High correlations were detected between the developed sensor system and the force plate CoP with a Pearson correlation coefficient ranging from 0.93 to 0.97. The information obtained throughout the experimental study is of great importance in order to gain further knowledge into the technical execution of boxing punches as well as to provide a novel measuring method for determining CoP on the surface of the fist, to improve the understanding of the etiology of boxing-related hand injuries. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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22 pages, 2736 KiB  
Article
Application of a Validated Innovative Smart Wearable for Performance Analysis by Experienced and Non-Experienced Athletes in Boxing
by Tobias Menzel and Wolfgang Potthast
Sensors 2021, 21(23), 7882; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237882 - 26 Nov 2021
Cited by 7 | Viewed by 2264
Abstract
An athlete’s sporting performance depends to a large extent on the technical execution of the athletic motion in order to achieve maximum effectiveness in physical performance. Performance analysis provides an important means of classifying and quantifying athletic prowess in terms of the significant [...] Read more.
An athlete’s sporting performance depends to a large extent on the technical execution of the athletic motion in order to achieve maximum effectiveness in physical performance. Performance analysis provides an important means of classifying and quantifying athletic prowess in terms of the significant performance aspects of the sport to provide objective feedback. This study aimed to analyze technical execution in terms of punch trajectory, force, velocity and time, considering the expert-novice paradigm by investigating the technical execution of 31 experienced and non-experienced athletes for the four main punching techniques of the cross, jab, uppercut and hook strike. The kinetic and kinematic data were collected by means of a boxing monitoring system developed and validated for in-field use. The research revealed significant correlation for executed punching trajectory and punch force in intragroup comparison and significant differences in intergroup comparison. No significant differences were detected for punch velocity in either inter- or intra-group paradigms. This study, through use of the sensor system, aligns with the results of existing publications conducted in laboratory conditions, in the assessment of punch force, punch speed and punch time and thus extends the state of research by use of a smart wearable in field method. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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12 pages, 1476 KiB  
Communication
Validation of a Unique Boxing Monitoring System
by Tobias Menzel and Wolfgang Potthast
Sensors 2021, 21(21), 6947; https://0-doi-org.brum.beds.ac.uk/10.3390/s21216947 - 20 Oct 2021
Cited by 6 | Viewed by 3072
Abstract
Much development work and scientific research has been conducted in recent years in the field of detecting human activity and the measurement of biomechanical performance parameters using portable sensor technologies, so-called wearable systems. Despite the fact that boxers participating in one of the [...] Read more.
Much development work and scientific research has been conducted in recent years in the field of detecting human activity and the measurement of biomechanical performance parameters using portable sensor technologies, so-called wearable systems. Despite the fact that boxers participating in one of the most vigorous and complex disciplines of all sports, it is one of the disciplines where no noteworthy, advanced performance analytic tools are used for training or for competition purposes worldwide. This research aimed to develop and validate a comprehensive punch performance sensor system for the measurement and analysis of biomechanical parameters in the sport of boxing. A comprehensive validation study on linear regression was conducted following the development of the sensor system, between the gold standard of a Kistler force plate and Vicon motion capture system, to compare sensor-derived measurements with the gold standard-derived measurements. The developed sensor system demonstrated high accuracies ranging from R2 = 0.97 to R2 = 0.99 for punch force, acceleration, velocity and punch-time data. The validation experiments conducted demonstrated the significant accuracy of the sensor-derived measurements for predicting boxing-specific biomechanical movement parameters while punching in field use. Thus, this paper presents a unique sensor system for comprehensive measurements of biomechanical parameters using the developed mobile measurement system in the field of combat sports Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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17 pages, 4639 KiB  
Article
Enhanced Breathing Pattern Detection during Running Using Wearable Sensors
by Eric Harbour, Michael Lasshofer, Matteo Genitrini and Hermann Schwameder
Sensors 2021, 21(16), 5606; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165606 - 20 Aug 2021
Cited by 20 | Viewed by 3801
Abstract
Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with [...] Read more.
Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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Review

Jump to: Research, Other

19 pages, 4249 KiB  
Review
Objective Measurement of Walking Activity Using Wearable Technologies in People with Parkinson Disease: A Systematic Review
by Mathias Baptiste Correno, Clint Hansen, Thomas Carlin and Nicolas Vuillerme
Sensors 2022, 22(12), 4551; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124551 - 16 Jun 2022
Cited by 3 | Viewed by 2035
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease with a multitude of disease variations including motor and non-motor symptoms. Quality of life and symptom management may be improved with physical activity. Due to technological advancement, development of small new wearable devices recently emerged [...] Read more.
Parkinson’s disease (PD) is a complex neurodegenerative disease with a multitude of disease variations including motor and non-motor symptoms. Quality of life and symptom management may be improved with physical activity. Due to technological advancement, development of small new wearable devices recently emerged and allowed objective measurement of walking activity in daily life. This review was specifically designed to synthesize literature on objective walking activity measurements using wearable devices of patients with PD. Inclusion criteria included patients with a diagnosis of PD and exclusion criteria included studies using animal models or mixed syndromes. Participants were not required to undergo any type of intervention and the studies must have reported at least one output that quantifies daily walking activity. Three databases were systematically searched with no limitation on publication date. Twenty-six studies were eligible and included in the systematic review. The most frequently used device was the ActiGraph GT3X which was used in 10 studies. Duration of monitoring presented a range from 8 h to one year. Nevertheless, 11 studies measured walking activity during a 7-day period. On-body sensor wearing location differed throughout the included studies showing eight positions, with the waist, ankle, and wrist being the most frequently used locations. The main procedures consisted of measurement of walking hours during a 2-day period or more, equipped with a triaxial accelerometer at the dominant hip or ankle. It is also important for further research to take care of different factors such as the population, their pathology, the period, and the environment. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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Other

Jump to: Research, Review

36 pages, 1290 KiB  
Systematic Review
Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review
by Luca Marotta, Bouke L. Scheltinga, Robbert van Middelaar, Wichor M. Bramer, Bert-Jan F. van Beijnum, Jasper Reenalda and Jaap H. Buurke
Sensors 2022, 22(8), 3008; https://0-doi-org.brum.beds.ac.uk/10.3390/s22083008 - 14 Apr 2022
Cited by 10 | Viewed by 3151
Abstract
Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an [...] Read more.
Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson’s correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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10 pages, 499 KiB  
Perspective
Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports
by Roman Holzer, Wilhelm Bloch and Christian Brinkmann
Sensors 2022, 22(5), 2030; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052030 - 05 Mar 2022
Cited by 20 | Viewed by 10291
Abstract
Introduction: Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health [...] Read more.
Introduction: Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports. Results: CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise. Conclusions: CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Movement Analysis Related to Biomechanics and Exercise Physiology)
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