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Sensor Technology for Improving Human Movements and Postures: Part II

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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 19703

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Dr. Winson Lee

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Guest Editor

School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, Australia
Interests: movement and gait analysis; rehabilitation engineering; smart prosthetic and orthotic devices; sports biomechanics
Special Issues, Collections and Topics in MDPI journals

Dr. Emre Sariyildiz

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Guest Editor

School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: motion control; robotics and biomectronics

Special Issue Information

Dear Colleagues,

Sensor technology can be used to measure movements and postures. Such measurements can potentially improve musculoskeletal health, leading to better quality of life in areas of gerontology, physical rehabilitation, sports, and occupation requiring physical movements or prolonged static postures. For example, sensors can be used to:

Assist or encourage walking and prevent fall of older adults;
Enable exoskeletal or robotic devices to improve mobility of people with neuro-musculoskeletal disorder;
Detect sport-specific movements to improve sports performance and reduce risk of injuries;
Improve occupational biomechanics and ergonomics.

Examples of sensors include accelerometers, gyroscopes, magnetometers, and force sensors. They can be wearable or laboratory based.

This Special Issue focuses on developments, uses, and/or outcome measurement of sensor technology, including wearable sensors with or without biofeedback, lab-based sensing systems for forces and motions, biorobotic sensors, and smart prosthetic and orthotic devices, which ultimately aim to improve human movements and/or sport performance. Original research and review papers in these areas are encouraged.

Dr. Winson Lee
Dr. Emre Sariyildiz
Guest Editors

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Keywords

wearable sensors
robotic sensors
motion analysis
rehabilitation
aging
sports and injury

Related Special Issue

Sensor Technology for Improving Human Movements and Postures in Sensors (16 articles)

Published Papers (9 papers)

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Research

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25 pages, 10632 KiB

Open AccessArticle

Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns

by Dovin Kiernan, Brandon Ng and David A. Hawkins

Sensors 2023, 23(21), 8719; https://doi.org/10.3390/s23218719 - 25 Oct 2023

Cited by 1 | Viewed by 1594

Abstract

Twenty-seven methods of estimating vertical ground reaction force first peak, loading rate, second peak, average, and/or time series from a single wearable accelerometer worn on the shank or approximate center of mass during running were compared. Force estimation errors were quantified for 74 participants across different running surfaces, speeds, and foot strike angles and biases, repeatability coefficients, and limits of agreement were modeled with linear mixed effects to quantify the accuracy, reliability, and precision. Several methods accurately and reliably estimated the first peak and loading rate, however, none could do so precisely (the limits of agreement exceeded ±65% of target values). Thus, we do not recommend first peak or loading rate estimation from accelerometers with the methods currently available. In contrast, the second peak, average, and time series could all be estimated accurately, reliably, and precisely with several different methods. Of these, we recommend the ‘Pogson’ methods due to their accuracy, reliability, and precision as well as their stability across surfaces, speeds, and foot strike angles. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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13 pages, 1737 KiB

Open AccessArticle

Experimental Evaluation of a Hybrid Sensory Feedback System for Haptic and Kinaesthetic Perception in Hand Prostheses

by Emre Sariyildiz, Fergus Hanss, Hao Zhou, Manish Sreenivasa, Lucy Armitage, Rahim Mutlu and Gursel Alici

Sensors 2023, 23(20), 8492; https://doi.org/10.3390/s23208492 - 16 Oct 2023

Viewed by 1728

Abstract

This study proposes a new hybrid multi-modal sensory feedback system for prosthetic hands that can provide not only haptic and proprioceptive feedback but also facilitate object recognition without the aid of vision. Modality-matched haptic perception was provided using a mechanotactile feedback system that can proportionally apply the gripping force through the use of a force controller. A vibrotactile feedback system was also employed to distinguish four discrete grip positions of the prosthetic hand. The system performance was evaluated with a total of 32 participants in three different experiments (i) haptic feedback, (ii) proprioceptive feedback and (iii) object recognition with hybrid haptic-proprioceptive feedback. The results from the haptic feedback experiment showed that the participants’ ability to accurately perceive applied force depended on the amount of force applied. As the feedback force was increased, the participants tended to underestimate the force levels, with a decrease in the percentage of force estimation. Of the three arm locations (forearm volar, forearm ventral and bicep), and two muscle states (relaxed and tensed) tested, the highest accuracy was obtained for the bicep location in the relaxed state. The results from the proprioceptive feedback experiment showed that participants could very accurately identify four different grip positions of the hand prosthesis (i.e., open hand, wide grip, narrow grip, and closed hand) without a single case of misidentification. In experiment 3, participants could identify objects with different shapes and stiffness with an overall high success rate of 90.5% across all combinations of location and muscle state. The feedback location and muscle state did not have a significant effect on object recognition accuracy. Overall, our study results indicate that the hybrid feedback system may be a very effective way to enrich a prosthetic hand user’s experience of the stiffness and shape of commonly manipulated objects. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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14 pages, 973 KiB

Open AccessArticle

The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment

by José-Francisco Pedrero-Sánchez, Helios De-Rosario-Martínez, Enrique Medina-Ripoll, David Garrido-Jaén, Pilar Serra-Añó, Sara Mollà-Casanova and Juan López-Pascual

Sensors 2023, 23(14), 6567; https://doi.org/10.3390/s23146567 - 20 Jul 2023

Cited by 1 | Viewed by 1162

Abstract

Falls in older people are a major health concern as the leading cause of disability and the second most common cause of accidental death. We developed a rapid fall risk assessment based on a combination of physical performance measurements made with an inertial sensor embedded in a smartphone. This study aimed to evaluate and validate the reliability and accuracy of an easy-to-use smartphone fall risk assessment by comparing it with the Physiological Profile Assessment (PPA) results. Sixty-five participants older than 55 performed a variation of the Timed Up and Go test using smartphone sensors. Balance and gait parameters were calculated, and their reliability was assessed by the (ICC) and compared with the PPAs. Since the PPA allows classification into six levels of fall risk, the data obtained from the smartphone assessment were categorised into six equivalent levels using different parametric and nonparametric classifier models with neural networks. The F1 score and geometric mean of each model were also calculated. All selected parameters showed ICCs around 0.9. The best classifier, in terms of accuracy, was the nonparametric mixed input data model with a 100% success rate in the classification category. In conclusion, fall risk can be reliably assessed using a simple, fast smartphone protocol that allows accurate fall risk classification among older people and can be a useful screening tool in clinical settings. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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13 pages, 932 KiB

Open AccessArticle

A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study

by Anthonia J. Langerak, Gerrit Ruben Hendrik Regterschot, Marc Evers, Bert-Jan F. van Beijnum, Carel G. M. Meskers, Ruud W. Selles, Gerard M. Ribbers and Johannes B. J. Bussmann

Sensors 2023, 23(13), 5868; https://doi.org/10.3390/s23135868 - 25 Jun 2023

Viewed by 1619

Abstract

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients. Methods: A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7–112) and (3) usability: the system usability scale (range: 0–100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio. Results: Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77–111)) and usability (median (IQR): 77.5 (75–78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η²G = 0.07, p = 0.04) and ratio (η²G = 0.11, p = 0.22). Conclusion: Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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17 pages, 3161 KiB

Open AccessArticle

Uneven Terrain Recognition Using Neuromorphic Haptic Feedback

by Sahana Prasanna, Jessica D’Abbraccio, Mariangela Filosa, Davide Ferraro, Ilaria Cesini, Giacomo Spigler, Andrea Aliperta, Filippo Dell’Agnello, Angelo Davalli, Emanuele Gruppioni, Simona Crea, Nicola Vitiello, Alberto Mazzoni and Calogero Maria Oddo

Sensors 2023, 23(9), 4521; https://doi.org/10.3390/s23094521 - 6 May 2023

Viewed by 1819

Abstract

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot–ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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20 pages, 5370 KiB

Open AccessArticle

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

by Muhammad Hannan Ahmed, Jiazheng Chai, Shingo Shimoda and Mitsuhiro Hayashibe

Sensors 2023, 23(9), 4188; https://doi.org/10.3390/s23094188 - 22 Apr 2023

Cited by 1 | Viewed by 1496

Abstract

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder–elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion–extension and pronation–supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson’s correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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26 pages, 5893 KiB

Open AccessArticle

Estimation of One-Repetition Maximum, Type, and Repetition of Resistance Band Exercise Using RGB Camera and Inertial Measurement Unit Sensors

by Byunggon Hwang, Gyuseok Shim, Woong Choi and Jaehyo Kim

Sensors 2023, 23(2), 1003; https://doi.org/10.3390/s23021003 - 15 Jan 2023

Cited by 1 | Viewed by 2081

Abstract

Resistance bands are widely used nowadays to enhance muscle strength due to their high portability, but the relationship between resistance band workouts and conventional dumbbell weight training is still unclear. Thus, this study suggests a convolutional neural network model that identifies the type of band workout and counts the number of repetitions and a regression model that deduces the band force that corresponds to the one-repetition maximum. Thirty subjects performed five different exercises using resistance bands and dumbbells. Joint movements during each exercise were collected using a camera and an inertial measurement unit. By using different types of input data, several models were created and compared. As a result, the accuracy of the convolutional neural network model using inertial measurement units and joint position is 98.83%. The mean absolute error of the repetition counting algorithm ranges from 0.88 (seated row) to 3.21 (overhead triceps extension). Lastly, the values of adjusted r-square for the 5 exercises are 0.8415 (chest press), 0.9202 (shoulder press), 0.8429 (seated row), 0.8778 (biceps curl), and 0.9232 (overhead triceps extension). In conclusion, the model using 10-channel inertial measurement unit data and joint position data has the best accuracy. However, the model needs to improve the inaccuracies resulting from non-linear movements and one-time performance. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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Review

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38 pages, 1624 KiB

Open AccessReview

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics—An Overview of Current Applications, Challenges, and Future Opportunities

by Carl Mikael Lind, Farhad Abtahi and Mikael Forsman

Sensors 2023, 23(9), 4259; https://doi.org/10.3390/s23094259 - 25 Apr 2023

Cited by 13 | Viewed by 5280

Abstract

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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Other

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16 pages, 848 KiB

Open AccessSystematic Review

Use, Validity and Reliability of Inertial Movement Units in Volleyball: Systematic Review of the Scientific Literature

by Diego Hernán Villarejo-García, Adrián Moreno-Villanueva, Alejandro Soler-López, Pedro Reche-Soto and José Pino-Ortega

Sensors 2023, 23(8), 3960; https://doi.org/10.3390/s23083960 - 13 Apr 2023

Cited by 3 | Viewed by 1958

Abstract

The use of inertial devices in sport has become increasingly common. The aim of this study was to examine the validity and reliability of multiple devices for measuring jump height in volleyball. The search was carried out in four databases (PubMed, Scopus, Web of Sciences and SPORTDiscus) using keywords and Boolean operators. Twenty-one studies were selected that met the established selection criteria. The studies focused on determining the validity and reliability of IMUs (52.38%), on controlling and quantifying external load (28.57%) and on describing differences between playing positions (19.05%). Indoor volleyball was the modality in which IMUs have been used the most. The most evaluated population was elite, adult and senior athletes. The IMUs were used both in training and in competition, evaluating mainly the amount of jump, the height of the jumps and some biomechanical aspects. Criteria and good validity values for jump counting are established. The reliability of the devices and the evidence is contradictory. IMUs are devices used in volleyball to count and measure vertical displacements and/or compare these measurements with the playing position, training or to determine the external load of the athletes. It has good validity measures, although inter-measurement reliability needs to be improved. Further studies are suggested to position IMUs as measuring instruments to analyze jumping and sport performance of players and teams. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: Part II)

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