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Wearable Sensors for Risk Assessment and Injury Prevention
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Wearable Sensors for Risk Assessment and Injury Prevention

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Wearables".

Viewed by 41350

Editors

Prof. Dr. Pietro Picerno

E-Mail Website
Collection Editor
Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
Interests: human movement analysis; gait analysis; sport biomechanics; inertial sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Mannini

E-Mail Website
Collection Editor
The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
Interests: wearable sensors; machine learning; activity recognition; inertial sensors; movement analysis; gait parameters estimation; automatic early detection of gait alterations; sports bioengineering; mobile health
Special Issues, Collections and Topics in MDPI journals
Dr. Clive D’Souza

E-Mail Website
Collection Editor
Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: occupational ergonomics; functional biomechanics; ergonomics exposure assessment; human movement analysis; rehabilitation

Topical Collection Information

Dear Colleagues,

30 years of development of algorithms for human movement analysis by means of inertial sensors have made such sensors a viable alternative to traditional motion capture, especially when the analysis of the motor task is required in non-standard environments and for long periods of time (e.g., ambulatory settings or during daily life activities). Meanwhile, technological advances have made “wearable” an increasing number of sensors typologies for the measurement of human motion-related mechanical and physiological variables (e.g., force/pressure sensors, surface electromyography, positioning systems, heart rate monitors). We can safely say that, at the beginning of 2020, the use of wearable sensors for the assessment of motor disorders in clinical settings and human activities in free-living environments is now well-established. Having come this far, the aim of this collection is to gather contributions to help delineate this new and emerging field that makes wearable sensors a tool for biomechanical risk assessment and injury prevention during work-, home-, sport- and leisure-related activities. Risk assessment and injury prevention both need to be performed in real-life environments and often through continuous monitoring, and wearable sensors are well suited for this purpose. This collection welcomes original research articles and narrative and systematic reviews focused on the use of the following typologies of wearable sensors:

  • motion sensors;
  • force/pressure sensors;
  • EMG sensors;
  • heart rate sensors;
  • light/noise sensors;
  • humidity/temperature sensors;
  • position sensors.

for risk assessment and injury prevention of people at work, at home or during their sport and leisure activities. Single-sensor solutions, smart-devices, wearable sensor networks and sensorized wearable-robotics are key technologies for this collection.

Prof. Dr. Pietro Picerno
Dr. Andrea Mannini
Dr. Clive D’Souza
Collection Editors

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Keywords

  • wearable
  • motion analysis
  • ergonomics
  • assistive technologies
  • fall detection
  • biomechanical risk
  • smart sensors
  • tele-rehabilitation
  • continuous monitoring
  • traumatology
  • sport biomechanics

Published Papers (16 papers)

Download All Papers

2024

Jump to: 2022, 2021, 2020

11 pages, 2674 KiB  
Article
Development and Evaluation of a Hybrid Measurement System to Determine the Kinematics of the Wrist
by Jason Dellai, Martine A. Gilles, Olivier Remy, Laurent Claudon and Gilles Dietrich
Sensors 2024, 24(8), 2543; https://doi.org/10.3390/s24082543 - 16 Apr 2024
Viewed by 328
Abstract
Optical Motion Capture Systems (OMCSs) are considered the gold standard for kinematic measurement of human movements. However, in situations such as measuring wrist kinematics during a hairdressing activity, markers can be obscured, resulting in a loss of data. Other measurement methods based on [...] Read more.
Optical Motion Capture Systems (OMCSs) are considered the gold standard for kinematic measurement of human movements. However, in situations such as measuring wrist kinematics during a hairdressing activity, markers can be obscured, resulting in a loss of data. Other measurement methods based on non-optical data can be considered, such as magneto-inertial measurement units (MIMUs). Their accuracy is generally lower than that of an OMCS. In this context, it may be worth considering a hybrid system [MIMU + OMCS] to take advantage of OMCS accuracy while limiting occultation problems. The aim of this work was (1) to propose a methodology for coupling a low-cost MIMU (BNO055) to an OMCS in order to evaluate wrist kinematics, and then (2) to evaluate the accuracy of this hybrid system [MIMU + OMCS] during a simple hairdressing gesture. During hair cutting gestures, the root mean square error compared with the OMCS was 4.53° (1.45°) for flexion/extension, 5.07° (1.30°) for adduction/abduction, and 3.65° (1.19°) for pronation/supination. During combing gestures, they were significantly higher, but remained below 10°. In conclusion, this system allows for maintaining wrist kinematics in case of the loss of hand markers while preserving an acceptable level of precision (<10°) for ergonomic measurement or entertainment purposes. Full article
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2022

Jump to: 2024, 2021, 2020

11 pages, 1523 KiB  
Article
Assessment of Three-Dimensional Kinematics of High- and Low-Calibre Hockey Skaters on Synthetic Ice Using Wearable Sensors
by Aminreza Khandan, Ramin Fathian, Jason P. Carey and Hossein Rouhani
Sensors 2023, 23(1), 334; https://doi.org/10.3390/s23010334 - 28 Dec 2022
Viewed by 1637
Abstract
Hockey skating objective assessment can help coaches detect players’ performance drop early and avoid fatigue-induced injuries. This study aimed to calculate and experimentally validate the 3D angles of lower limb joints of hockey skaters obtained by inertial measurement units and explore the effectiveness [...] Read more.
Hockey skating objective assessment can help coaches detect players’ performance drop early and avoid fatigue-induced injuries. This study aimed to calculate and experimentally validate the 3D angles of lower limb joints of hockey skaters obtained by inertial measurement units and explore the effectiveness of the on-ice distinctive features measured using these wearable sensors in differentiating low- and high-calibre skaters. Twelve able-bodied individuals, six high-calibre and six low-calibre skaters, were recruited to skate forward on a synthetic ice surface. Five IMUs were placed on their dominant leg and pelvis. The 3D lower-limb joint angles were obtained by IMUs and experimentally validated against those obtained by a motion capture system with a maximum root mean square error of 5 deg. Additionally, among twelve joint angle-based distinctive features identified in other on-ice studies, only three were significantly different (p-value < 0.05) between high- and low-calibre skaters in this synthetic ice experiment. This study thus indicated that skating on synthetic ice alters the skating patterns such that the on-ice distinctive features can no longer differentiate between low- and high-calibre skating joint angles. This wearable technology has the potential to help skating coaches keep track of the players’ progress by assessing the skaters’ performance, wheresoever. Full article
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12 pages, 1803 KiB  
Article
Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling
by Antoine Muller, Hakim Mecheri, Philippe Corbeil, André Plamondon and Xavier Robert-Lachaine
Sensors 2022, 22(17), 6454; https://doi.org/10.3390/s22176454 - 26 Aug 2022
Cited by 4 | Viewed by 1661
Abstract
Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper [...] Read more.
Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper aims to develop and evaluate an IMC-based approach to estimate back loading during manual material handling (MMH) tasks. During various representative workplace MMH tasks performed by nine participants, this approach was evaluated by comparing the results with the ones computed from optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found between both computations on indicators based on L5/S1 peak and cumulative flexion moments, while lower correlations were found on indicators based on asymmetric moments. Since no force measurement or load kinematics measurement is needed, this study shows the potential of using only the handler’s kinematics measured by IMC to estimate kinetics variables. The assessment of workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics evaluation and will improve the ecological validity compared to laboratory studies, where the situations are often simplified and standardized. Full article
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10 pages, 1973 KiB  
Article
Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites
by Seunghee Lee, Bummo Koo, Sumin Yang, Jongman Kim, Yejin Nam and Youngho Kim
Sensors 2022, 22(16), 6107; https://doi.org/10.3390/s22166107 - 16 Aug 2022
Cited by 4 | Viewed by 2415
Abstract
Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen [...] Read more.
Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen general working movements (NF: non-fall), five low-hazard-fall movements, (LF), and five high-hazard-FFH movements (HF) were performed by twenty male subjects and a dummy. An IMU sensor was attached to the T7 position of the subject to measure the three-axis acceleration and angular velocity. The peak acceleration value, calculated from the IMU data, was 4 g or less in general work movements and 9 g or more in FFHs. Regression analysis was performed by applying various deep learning models, including 1D-CNN, 2D-CNN, LSTM, and Conv-LSTM, to the risk prediction, and then comparing them in terms of their mean absolute error (MAE) and mean squared error (MSE). The FFH risk level was estimated based on the predicted peak acceleration. The Conv-LSTM model trained by MAE showed the smallest error (MAE: 1.36 g), and the classification with the predicted peak acceleration showed the best accuracy (97.6%). This study successfully predicted the FFH risk levels and could be helpful to reduce fatal injuries at construction sites. Full article
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2021

Jump to: 2024, 2022, 2020

13 pages, 2717 KiB  
Article
Monitoring Flexions and Torsions of the Trunk via Gyroscope-Calibrated Capacitive Elastomeric Wearable Sensors
by Gabriele Frediani, Federica Vannetti, Leonardo Bocchi, Giovanni Zonfrillo and Federico Carpi
Sensors 2021, 21(20), 6706; https://doi.org/10.3390/s21206706 - 09 Oct 2021
Cited by 4 | Viewed by 1665
Abstract
Reliable, easy-to-use, and cost-effective wearable sensors are desirable for continuous measurements of flexions and torsions of the trunk, in order to assess risks and prevent injuries related to body movements in various contexts. Piezo-capacitive stretch sensors, made of dielectric elastomer membranes coated with [...] Read more.
Reliable, easy-to-use, and cost-effective wearable sensors are desirable for continuous measurements of flexions and torsions of the trunk, in order to assess risks and prevent injuries related to body movements in various contexts. Piezo-capacitive stretch sensors, made of dielectric elastomer membranes coated with compliant electrodes, have recently been described as a wearable, lightweight and low-cost technology to monitor body kinematics. An increase of their capacitance upon stretching can be used to sense angular movements. Here, we report on a wearable wireless system that, using two sensing stripes arranged on shoulder straps, can detect flexions and torsions of the trunk, following a simple and fast calibration with a conventional tri-axial gyroscope on board. The piezo-capacitive sensors avoid the errors that would be introduced by continuous sensing with a gyroscope, due to its typical drift. Relative to stereophotogrammetry (non-wearable standard system for motion capture), pure flexions and pure torsions could be detected by the piezo-capacitive sensors with a root mean square error of ~8° and ~12°, respectively, whilst for flexion and torsion components in compound movements, the error was ~13° and ~15°, respectively. Full article
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15 pages, 4787 KiB  
Article
DATSURYOKU Sensor—A Capacitive-Sensor-Based Belt for Predicting Muscle Tension: Preliminary Results
by Akihiko Murai, Shusuke Kanazawa, Ko Ayusawa, Sohei Washino, Manabu Yoshida and Masaaki Mochimaru
Sensors 2021, 21(19), 6669; https://doi.org/10.3390/s21196669 - 07 Oct 2021
Viewed by 2564
Abstract
Excessive muscle tension is implicitly caused by inactivity or tension in daily activities, and it results in increased joint stiffness and vibration, and thus, poor performance, failure, and injury in sports. Therefore, the routine measurement of muscle tension is important. However, a co-contraction [...] Read more.
Excessive muscle tension is implicitly caused by inactivity or tension in daily activities, and it results in increased joint stiffness and vibration, and thus, poor performance, failure, and injury in sports. Therefore, the routine measurement of muscle tension is important. However, a co-contraction observed in excessive muscle tension cannot be easily detected because it does not appear in motion owing to the counteracting muscle tension, and it cannot be measured by conventional motion capture systems. Therefore, we focused on the physiological characteristics of muscle, that is, the increase in muscle belly cross-sectional area during activity and softening during relaxation. Furthermore, we measured muscle tension, especially co-contraction and relaxation, using a DATSURYOKU sensor, which measures the circumference of the applied part. The experiments showed high interclass correlation between muscle activities and circumference across maximal voluntary co-contractions of the thigh muscles and squats. Moreover, the circumference sensor can measure passive muscle deformation that does not appear in muscle activities. Therefore, the DATSURYOKU sensor showed the potential to routinely measure muscle tension and relaxation, thus avoiding the risk of failure and injury owing to excessive muscle tension and can contribute to the realization of preemptive medicine by measuring daily changes. Full article
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18 pages, 5222 KiB  
Article
A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm
by Yanchao Wang, Ye Tian, Jinying Zhu, Haotian She, Hiroshi Yokoi, Yinlai Jiang and Qiang Huang
Sensors 2021, 21(18), 6234; https://doi.org/10.3390/s21186234 - 17 Sep 2021
Cited by 8 | Viewed by 2094
Abstract
Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working [...] Read more.
Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future. Full article
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13 pages, 2428 KiB  
Article
Wearable Detection of Trunk Flexions: Capacitive Elastomeric Sensors Compared to Inertial Sensors
by Gabriele Frediani, Leonardo Bocchi, Federica Vannetti, Giovanni Zonfrillo and Federico Carpi
Sensors 2021, 21(16), 5453; https://doi.org/10.3390/s21165453 - 12 Aug 2021
Cited by 7 | Viewed by 2164
Abstract
Continuous monitoring of flexions of the trunk via wearable sensors could help various types of workers to reduce risks associated with incorrect postures and movements. Stretchable piezo-capacitive elastomeric sensors based on dielectric elastomers have recently been described as a wearable, lightweight and cost-effective [...] Read more.
Continuous monitoring of flexions of the trunk via wearable sensors could help various types of workers to reduce risks associated with incorrect postures and movements. Stretchable piezo-capacitive elastomeric sensors based on dielectric elastomers have recently been described as a wearable, lightweight and cost-effective technology to monitor human kinematics. Their stretching causes an increase of capacitance, which can be related to angular movements. Here, we describe a wearable wireless system to detect flexions of the trunk, based on such sensors. In particular, we present: (i) a comparison of different calibration strategies for the capacitive sensors, using either an accelerometer or a gyroscope as an inclinometer; (ii) a comparison of the capacitive sensors’ performance with those of the accelerometer and gyroscope; to that aim, the three types of sensors were evaluated relative to stereophotogrammetry. Compared to the gyroscope, the capacitive sensors showed a higher accuracy. Compared to the accelerometer, their performance was lower when used as quasi-static inclinometers but also higher in case of highly dynamic accelerations. This makes the capacitive sensors attractive as a complementary, rather than alternative, technology to inertial sensors. Full article
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16 pages, 716 KiB  
Article
Post-Drive Standing Balance of Vehicle Passengers Using Wearable Sensors: The Effect of On-Road Driving and Task Performance
by Victor C. Le, Monica L. H. Jones and Kathleen H. Sienko
Sensors 2021, 21(15), 4997; https://doi.org/10.3390/s21154997 - 23 Jul 2021
Viewed by 1830
Abstract
Postural sway has been demonstrated to increase following exposure to different types of motion. However, limited prior studies have investigated the relationship between exposure to normative on-road driving conditions and standing balance following the exposure. The purpose of this on-road study was to [...] Read more.
Postural sway has been demonstrated to increase following exposure to different types of motion. However, limited prior studies have investigated the relationship between exposure to normative on-road driving conditions and standing balance following the exposure. The purpose of this on-road study was to quantify the effect of vehicle motion and task performance on passengers’ post-drive standing balance performance. In this study, trunk-based kinematic data were captured while participants performed a series of balance exercises before and after an on-road driving session in real-time traffic. Postural sway for all balance exercises increased following the driving session. Performing a series of ecologically relevant visual-based tasks led to increases in most post-drive balance metrics such as sway position and velocity. However, the post-drive changes following the driving session with a task were not significantly different compared to changes observed following the driving session without a task. The post-drive standing balance performance changes observed in this study may increase vulnerable users’ risk of falling. Wearable sensors offer an opportunity to monitor postural sway following in-vehicle exposures. Full article
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10 pages, 20592 KiB  
Communication
Accuracy of a Low-Cost 3D-Printed Wearable Goniometer for Measuring Wrist Motion
by Calvin Young, Sarah DeDecker, Drew Anderson, Michele L. Oliver and Karen D. Gordon
Sensors 2021, 21(14), 4799; https://doi.org/10.3390/s21144799 - 14 Jul 2021
Cited by 3 | Viewed by 2144
Abstract
Wrist motion provides an important metric for disease monitoring and occupational risk assessment. The collection of wrist kinematics in occupational or other real-world environments could augment traditional observational or video-analysis based assessment. We have developed a low-cost 3D printed wearable device, capable of [...] Read more.
Wrist motion provides an important metric for disease monitoring and occupational risk assessment. The collection of wrist kinematics in occupational or other real-world environments could augment traditional observational or video-analysis based assessment. We have developed a low-cost 3D printed wearable device, capable of being produced on consumer grade desktop 3D printers. Here we present a preliminary validation of the device against a gold standard optical motion capture system. Data were collected from 10 participants performing a static angle matching task while seated at a desk. The wearable device output was significantly correlated with the optical motion capture system yielding a coefficient of determination (R2) of 0.991 and 0.972 for flexion/extension (FE) and radial/ulnar deviation (RUD) respectively (p < 0.0001). Error was similarly low with a root mean squared error of 4.9° (FE) and 3.9° (RUD). Agreement between the two systems was quantified using Bland–Altman analysis, with bias and 95% limits of agreement of 3.1° ± 7.4° and −0.16° ± 7.7° for FE and RUD, respectively. These results compare favourably with current methods for occupational assessment, suggesting strong potential for field implementation. Full article
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13 pages, 1584 KiB  
Article
The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions
by Bummo Koo, Jongman Kim, Yejin Nam and Youngho Kim
Sensors 2021, 21(14), 4638; https://doi.org/10.3390/s21144638 - 06 Jul 2021
Cited by 6 | Viewed by 2575
Abstract
In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external [...] Read more.
In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training. Full article
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17 pages, 2940 KiB  
Article
Combining Ergonomic Risk Assessment (RULA) with Inertial Motion Capture Technology in Dentistry—Using the Benefits from Two Worlds
by Christian Maurer-Grubinger, Fabian Holzgreve, Laura Fraeulin, Werner Betz, Christina Erbe, Doerthe Brueggmann, Eileen M. Wanke, Albert Nienhaus, David A. Groneberg and Daniela Ohlendorf
Sensors 2021, 21(12), 4077; https://doi.org/10.3390/s21124077 - 13 Jun 2021
Cited by 22 | Viewed by 4414
Abstract
Traditional ergonomic risk assessment tools such as the Rapid Upper Limb Assessment (RULA) are often not sensitive enough to evaluate well-optimized work routines. An implementation of kinematic data captured by inertial sensors is applied to compare two work routines in dentistry. The surgical [...] Read more.
Traditional ergonomic risk assessment tools such as the Rapid Upper Limb Assessment (RULA) are often not sensitive enough to evaluate well-optimized work routines. An implementation of kinematic data captured by inertial sensors is applied to compare two work routines in dentistry. The surgical dental treatment was performed in two different conditions, which were recorded by means of inertial sensors (Xsens MVN Link). For this purpose, 15 (12 males/3 females) oral and maxillofacial surgeons took part in the study. Data were post processed with costume written MATLAB® routines, including a full implementation of RULA (slightly adjusted to dentistry). For an in-depth comparison, five newly introduced levels of complexity of the RULA analysis were applied, i.e., from lowest complexity to highest: (1) RULA score, (2) relative RULA score distribution, (3) RULA steps score, (4) relative RULA steps score occurrence, and (5) relative angle distribution. With increasing complexity, the number of variables times (the number of resolvable units per variable) increased. In our example, only significant differences between the treatment concepts were observed at levels that are more complex: the relative RULA step score occurrence and the relative angle distribution (level 4 + 5). With the presented approach, an objective and detailed ergonomic analysis is possible. The data-driven approach adds significant additional context to the RULA score evaluation. The presented method captures data, evaluates the full task cycle, and allows different levels of analysis. These points are a clear benefit to a standard, manual assessment of one main body position during a working task. Full article
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14 pages, 1980 KiB  
Article
Design and Development of a Hemorrhagic Trauma Simulator for Lower Limbs: A Pilot Study
by Blanca Larraga-García, Aurora Pérez-Jiménez, Santiago Ros-Dopico, Javier Rubio-Bolívar, Manuel Quintana-Diaz and Álvaro Gutiérrez
Sensors 2021, 21(11), 3816; https://doi.org/10.3390/s21113816 - 31 May 2021
Viewed by 2076
Abstract
One of the main preventable leading causes of death after a trauma injury is the hemorrhagic shock. Therefore, it is extremely important to learn how to control hemorrhages. In this paper, a hemorrhagic trauma simulator for lower limb has been developed and a [...] Read more.
One of the main preventable leading causes of death after a trauma injury is the hemorrhagic shock. Therefore, it is extremely important to learn how to control hemorrhages. In this paper, a hemorrhagic trauma simulator for lower limb has been developed and a pilot study has been accomplished to trail the simulator. Four different bleeding scenarios have been tested per participant, gathering information about the manual pressure exerted to control the bleeding. Data, altogether, from 54 hemorrhagic scenarios managed by final year medical students and doctors were gathered. Additionally, a post-simulation questionnaire, related to the usability of the simulator, was completed. All the participants managed to control the simulated bleeding scenarios, but the pressure exerted to control the four different scenarios is different depending if the trainee is a student or a doctor, especially in deep venous hemorrhages. This research has highlighted the different approach to bleeding control treatment between medical students and doctors. Moreover, this pilot study demonstrated the need to deliver a more effective trauma treatment teaching for hemorrhagic lesions and that hemorrhagic trauma simulators can be used to train and evaluate different scenarios. Full article
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10 pages, 1700 KiB  
Article
Actigraphic Measurement of the Upper Limbs for the Prediction of Ischemic Stroke Prognosis: An Observational Study
by Giuseppe Reale, Silvia Giovannini, Chiara Iacovelli, Stefano Filippo Castiglia, Pietro Picerno, Aurelia Zauli, Marco Rabuffetti, Maurizio Ferrarin, Giulio Maccauro and Pietro Caliandro
Sensors 2021, 21(7), 2479; https://doi.org/10.3390/s21072479 - 02 Apr 2021
Cited by 8 | Viewed by 2336
Abstract
Background: It is often challenging to formulate a reliable prognosis for patients with acute ischemic stroke. The most accepted prognostic factors may not be sufficient to predict the recovery process. In this view, describing the evolution of motor deficits over time via sensors [...] Read more.
Background: It is often challenging to formulate a reliable prognosis for patients with acute ischemic stroke. The most accepted prognostic factors may not be sufficient to predict the recovery process. In this view, describing the evolution of motor deficits over time via sensors might be useful for strengthening the prognostic model. Our aim was to assess whether an actigraphic-based parameter (Asymmetry Rate Index for the 24 h period (AR2_24 h)) obtained in the acute stroke phase could be a predictor of a 90 d prognosis. Methods: In this observational study, we recorded and analyzed the 24 h upper limb movement asymmetry of 20 consecutive patients with acute ischemic stroke during their stay in a stroke unit. We recorded the motor activity of both arms using two programmable actigraphic systems positioned on patients’ wrists. We clinically evaluated the stroke patients by NIHSS in the acute phase and then assessed them across 90 days using the modified Rankin Scale (mRS). Results: We found that the AR2_24 h parameter positively correlates with the 90 d mRS (r = 0.69, p < 0.001). Moreover, we found that an AR2_24 h > 32% predicts a poorer outcome (90 d mRS > 2), with sensitivity = 100% and specificity = 89%. Conclusions: Sensor-based parameters might provide useful information for predicting ischemic stroke prognosis in the acute phase. Full article
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25 pages, 3134 KiB  
Systematic Review
Overuse-Related Injuries of the Musculoskeletal System: Systematic Review and Quantitative Synthesis of Injuries, Locations, Risk Factors and Assessment Techniques
by Amaranta Orejel Bustos, Valeria Belluscio, Valentina Camomilla, Leandro Lucangeli, Francesco Rizzo, Tommaso Sciarra, Francesco Martelli and Claudia Giacomozzi
Sensors 2021, 21(7), 2438; https://doi.org/10.3390/s21072438 - 01 Apr 2021
Cited by 9 | Viewed by 5739
Abstract
Overuse-related musculoskeletal injuries mostly affect athletes, especially if involved in preseason conditioning, and military populations; they may also occur, however, when pathological or biological conditions render the musculoskeletal system inadequate to cope with a mechanical load, even if moderate. Within the MOVIDA (Motor [...] Read more.
Overuse-related musculoskeletal injuries mostly affect athletes, especially if involved in preseason conditioning, and military populations; they may also occur, however, when pathological or biological conditions render the musculoskeletal system inadequate to cope with a mechanical load, even if moderate. Within the MOVIDA (Motor function and Vitamin D: toolkit for risk Assessment and prediction) Project, funded by the Italian Ministry of Defence, a systematic review of the literature was conducted to support the development of a transportable toolkit (instrumentation, protocols and reference/risk thresholds) to help characterize the risk of overuse-related musculoskeletal injury. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach was used to analyze Review papers indexed in PubMed and published in the period 2010 to 2020. The search focused on stress (overuse) fracture or injuries, and muscle fatigue in the lower limbs in association with functional (biomechanical) or biological biomarkers. A total of 225 Review papers were retrieved: 115 were found eligible for full text analysis and led to another 141 research papers derived from a second-level search. A total of 183 papers were finally chosen for analysis: 74 were classified as introductory to the topics, 109 were analyzed in depth. Qualitative and, wherever possible, quantitative syntheses were carried out with respect to the literature review process and quality, injury epidemiology (type and location of injuries, and investigated populations), risk factors, assessment techniques and assessment protocols. Full article
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2020

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15 pages, 2750 KiB  
Article
Assessment of Biomechanical Response to Fatigue through Wearable Sensors in Semi-Professional Football Referees
by Luigi Truppa, Michelangelo Guaitolini, Pietro Garofalo, Carlo Castagna and Andrea Mannini
Sensors 2021, 21(1), 66; https://doi.org/10.3390/s21010066 - 24 Dec 2020
Cited by 4 | Viewed by 3004
Abstract
Quantifying muscle fatigue is a key aspect of everyday sport practice. A reliable and objective solution that can fulfil this task would be deeply important for two main reasons: (i) it would grant an objective indicator to adjust the daily training load for [...] Read more.
Quantifying muscle fatigue is a key aspect of everyday sport practice. A reliable and objective solution that can fulfil this task would be deeply important for two main reasons: (i) it would grant an objective indicator to adjust the daily training load for each player and (ii) it would provide an innovative tool to reduce the risk of fatigue-related injuries. Available solutions for objectively quantifying the fatigue level of fatigue can be invasive for the athlete; they could alter the performance or they are not compatible with daily practice on the playground. Building on previous findings that identified fatigue-related parameters in the kinematic of the counter-movement jump (CMJ), this study evaluates the physical response to a fatigue protocol (i.e., Yo-Yo Intermittent Recovery Test Level 1) in 16 football referees, by monitoring CMJ performance with wearable magneto-inertial measurement units (MIMU). Nineteen kinematic parameters were selected as suitable indicators for fatigue detection. The analysis of their variations allowed us to distinguish two opposites but coherent responses to the fatigue protocol. Indeed, eight out of sixteen athletes showed reduced performance (e.g., an effective fatigue condition), while the other eight athletes experienced an improvement of the execution likely due to the so-called Post-Activation Potentiation. In both cases, the above parameters were significantly influenced by the fatigue protocol (p < 0.05), confirming their validity for fatigue monitoring. Interesting correlations between several kinematic parameters and muscular mass were highlighted in the fatigued group. Finally, a “fatigue approximation index” was proposed and validated as fatigue quantifier. Full article
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