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Movement Biomechanics Applications of Wearable Inertial Sensors

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 9332

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Prof. Dr. Antonio P. Veloso

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CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Cruz-Quebrada-Dafundo, 1499-002 Cruz-Quebrada, Portugal
Interests: biomechanics; sports biomechanics; human movement biomechanics; musculoskeletal modeling; neuromechanics; clinical gait analysis; biomechanics modeling and simulation; movement disorders; rehabilitation biomechanics; occupational biomechanics
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Prof. Dr. Ricardo Matias

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Faculty of Sciences, University of Lisbon, 1749-016 Lisboa, Portugal
Interests: biomechanics; pattern recognition; musculoskeletal disorders; movement analysis; sports injuries; sports science; gait analysis; machine learning; data analysis; sport biomechanics

Prof. Dr. Filipa João

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CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Cruz-Quebrada-Dafundo, 1499-002 Oeiras, Portugal
Interests: biomechanics; musculoskeletal modeling; gait analysis; cerebral palsy
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Special Issue Information

Dear Colleagues,

Wearable inertial sensors, including accelerometers, gyroscopes, magnetometers, and GPS sensors, have grown considerably over the past decade. In sports science and biomechanics, wearable inertial sensors enable the analysis of moving objects and human motion. On one hand, GPS sensors measure the acceleration and angular velocity of an object in sports. On the other hand, wearable inertial sensors can capture human motion patterns outside of laboratory and clinical settings, and the resulting data can be used for fall risk assessment, fall detection, and injury prevention and performance improvement for athletes. Recent advances in sensors and wearable technology have opened up new opportunities to examine athlete performance and provide real-time feedback.

This special issue aims to collect the latest developments and research findings in the broad field of wearable inertial sensors. The topics of this special issue include, but are not limited to:

Wearable sensors for early detection of injury
Wearable sensors for sports biomechanics
Wearable sensors for fall detection
Inertial sensors for movement and motion assessment
Application of GPS sensors in sports
Signal processing techniques for inertial sensors
Localization with inertial sensors
Use of Smartphones inertial sensors to detect onset and progretion of neurologiacl disease

Prof. Dr. Antonio P. Veloso
Prof. Dr. Ricardo Matias
Prof. Dr. Filipa João
Guest Editors

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Published Papers (5 papers)

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Research

11 pages, 1257 KiB

Open AccessArticle

Sensor-Based Quantitative Assessment of Children’s Fine Motor Competence: An Instrumented Version of the Placing Bricks Test

by Maria Cristina Bisi and Rita Stagni

Sensors 2024, 24(7), 2192; https://0-doi-org.brum.beds.ac.uk/10.3390/s24072192 - 29 Mar 2024

Viewed by 546

Abstract

The assessment of fine motor competence plays a pivotal role in neuropsychological examinations for the identification of developmental deficits. Several tests have been proposed for the characterization of fine motor competence, with evaluation metrics primarily based on qualitative observation, limiting quantitative assessment to measures such as test durations. The Placing Bricks (PB) test evaluates fine motor competence across the lifespan, relying on the measurement of time to completion. The present study aims at instrumenting the PB test using wearable inertial sensors to complement PB standard assessment with reliable and objective process-oriented measures of performance. Fifty-four primary school children (27 6-year-olds and 27 7-year-olds) performed the PB according to standard protocol with their dominant and non-dominant hands, while wearing two tri-axial inertial sensors, one per wrist. An ad hoc algorithm based on the analysis of forearm angular velocity data was developed to automatically identify task events, and to quantify phases and their variability. The algorithm performance was tested against video recordings in data from five children. Cycle and Placing durations showed a strong agreement between IMU- and Video-derived measurements, with a mean difference <0.1 s, 95% confidence intervals <50% median phase duration, and very high positive correlation (ρ > 0.9). Analyzing the whole population, significant differences were found for age, as follows: six-year-olds exhibited longer cycle durations and higher variability, indicating a stage of development and potential differences in hand dominance; seven-year-olds demonstrated quicker and less variable performance, aligning with the expected maturation and the refined motor control associated with dominant hand training during the first year of school. The proposed sensor-based approach allowed the quantitative assessment of fine motor competence in children, providing a portable and rapid tool for monitoring developmental progress. Full article

(This article belongs to the Special Issue Movement Biomechanics Applications of Wearable Inertial Sensors)

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13 pages, 1170 KiB

Open AccessArticle

Parameterization of Biomechanical Variables through Inertial Measurement Units (IMUs) in Occasional Healthy Runners

by Álvaro Pareja-Cano, José María Arjona, Brian Caulfield and Antonio Cuesta-Vargas

Sensors 2024, 24(7), 2191; https://0-doi-org.brum.beds.ac.uk/10.3390/s24072191 - 29 Mar 2024

Viewed by 608

Abstract

Running is one of the most popular sports practiced today and biomechanical variables are fundamental to understanding it. The main objectives of this study are to describe kinetic, kinematic, and spatiotemporal variables measured using four inertial measurement units (IMUs) in runners during treadmill running, investigate the relationships between these variables, and describe differences associated with different data sampling and averaging strategies. A total of 22 healthy recreational runners (M age = 28 ± 5.57 yrs) participated in treadmill measurements, running at their preferred speed (M = 10.1 ± 1.9 km/h) with a set-up of four IMUs placed on tibias and the lumbar area. Raw data was processed and analysed over selections spanning 30 s, 30 steps and 1 step. Very strong positive associations were obtained between the same family variables in all selections. The temporal variables were inversely associated with the step rate variable in the selection of 30 s and 30 steps of data. There were moderate associations between kinetic (forces) and kinematic (displacement) variables. There were no significant differences between the biomechanics variables in any selection. Our results suggest that a 4-IMU set-up, as presented in this study, is a viable approach for parameterization of the biomechanical variables in running, and also that there are no significant differences in the biomechanical variables studied independently, if we select data from 30 s, 30 steps or 1 step for processing and analysis. These results can assist in the methodological aspects of protocol design in future running research. Full article

(This article belongs to the Special Issue Movement Biomechanics Applications of Wearable Inertial Sensors)

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16 pages, 3166 KiB

Open AccessArticle

Using Raw Accelerometer Data to Predict High-Impact Mechanical Loading

by Lucas Veras, Florêncio Diniz-Sousa, Giorjines Boppre, Vítor Devezas, Hugo Santos-Sousa, John Preto, João Paulo Vilas-Boas, Leandro Machado, José Oliveira and Hélder Fonseca

Sensors 2023, 23(4), 2246; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042246 - 16 Feb 2023

Cited by 2 | Viewed by 1575

Abstract

The purpose of this study was to develop peak ground reaction force (pGRF) and peak loading rate (pLR) prediction equations for high-impact activities in adult subjects with a broad range of body masses, from normal weight to severe obesity. A total of 78 participants (27 males; 82.4 ± 20.6 kg) completed a series of trials involving jumps of different types and heights on force plates while wearing accelerometers at the ankle, lower back, and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland–Altman plots. Body mass was a predictor in all models, along with peak acceleration in the pGRF models and peak acceleration rate in the pLR models. The equations to predict pGRF had a coefficient of determination (R²) of at least 0.83, and a mean absolute percentage error (MAPE) below 14.5%, while the R² for the pLR prediction equations was at least 0.87 and the highest MAPE was 24.7%. Jumping pGRF can be accurately predicted through accelerometry data, enabling the continuous assessment of mechanical loading in clinical settings. The pLR prediction equations yielded a lower accuracy when compared to the pGRF equations. Full article

(This article belongs to the Special Issue Movement Biomechanics Applications of Wearable Inertial Sensors)

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14 pages, 9399 KiB

Open AccessArticle

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

by Alessandro Leone, Gabriele Rescio, Andrea Caroppo, Pietro Siciliano and Andrea Manni

Sensors 2023, 23(2), 1039; https://0-doi-org.brum.beds.ac.uk/10.3390/s23021039 - 16 Jan 2023

Cited by 10 | Viewed by 3944

Abstract

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers. Full article

(This article belongs to the Special Issue Movement Biomechanics Applications of Wearable Inertial Sensors)

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10 pages, 988 KiB

Open AccessArticle

Gait Analysis in Children with Cerebral Palsy: Are Plantar Pressure Insoles a Reliable Tool?

by Maria Raquel Raposo, Diogo Ricardo, Júlia Teles, António Prieto Veloso and Filipa João

Sensors 2022, 22(14), 5234; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145234 - 13 Jul 2022

Cited by 2 | Viewed by 1955

Abstract

Cerebral palsy (CP) is a common cause of motor disability, and pedobarography is a useful, non-invasive, portable, and accessible tool; is easy to use in a clinical setting; and can provide plenty of information about foot–soil interaction and gait deviations. The reliability of this method in children with CP is lacking. The aim of this study is to investigate test–retest reliability and minimal detectable change (MDC) of plantar pressure insole variables in children with CP. Eight children performed two trials 8 ± 2.5 days apart, using foot insoles to collect plantar pressure data. Whole and segmented foot measurements were analyzed using intraclass correlation coefficients (ICC). The variability of the data was measured by calculating the standard error of measurement (SEM) and the MDC/ICC values demonstrated high test–retest reliability for most variables, ranging from good to excellent (ICC ≥ 0.60). The SEM and the MDC values were considered low for the different variables. The variability observed between sessions may be attributed to the heterogeneous sub-diagnosis of CP. Full article

(This article belongs to the Special Issue Movement Biomechanics Applications of Wearable Inertial Sensors)

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