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Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 23018

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Special Issue Editors

Dr. Laura Gastaldi

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Guest Editor

Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
Interests: biomechanical engineering; paralympic sports; human–robot collaboration; rehabilitation; motion analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Elisa Digo

E-Mail Website
Guest Editor

Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: biomechanical engineering; motion analysis; robotics; human-robot collaboration

Special Issue Information

Dear Colleagues,

Motion analysis has received a forward momentum from widespread of wearable inertial measurement units (IMUs). Unobtrusiveness, ease of use, and low cost are just a few of the several advantages of this innovative technology. The constant progress in terms of miniaturization, performance, and integration in other systems makes IMUs suitable for new applications. Moreover, the combination of inertial sensors with artificial intelligence and machine learning techniques is opening the frontiers for new challenges. In addition to the movement analysis traditionally performed in a clinical context, new applications are emerging. IMUs are now exploited, for example, in the sports field to improve performance and prevent injuries; in collaborative robotics to optimize the cooperation between the operator and the robot, both in terms of safety and performance; in e-health and wellness applications; and in tele-rehabilitation and activity recognition.

The present Special Issue is designed to comprehensively collect new advances and applications of wearable inertial sensors in different fields. Contributions that focus on intelligence methods, such as machine and deep learning, to post-process, analyze and interpret the acquired data are also welcome.

Dr. Laura Gastaldi
Dr. Elisa Digo
Guest Editors

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Keywords

characterization of systems, techniques and methods for motion and gait analysis
clinical reports using wearable sensors
wearable sensors, methods and/or techniques for medical decision making
wearable sensors, methods and/or techniques for activities modeling
health monitoring
fall risk
rehabilitation
biomechanics of sport
machine learning techniques
gesture recognition
industry
human–robot collaboration

Published Papers (14 papers)

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9 pages, 583 KiB

Open AccessCommunication

The Potential Role of Wearable Inertial Sensors in Laboring Women with Walking Epidural Analgesia

by Mikhail Dziadzko, Adrien Péneaud, Lionel Bouvet, Thomas Robert, Laetitia Fradet and David Desseauve

Sensors 2024, 24(6), 1904; https://doi.org/10.3390/s24061904 - 16 Mar 2024

Viewed by 493

Abstract

There is a growing interest in wearable inertial sensors to monitor and analyze the movements of pregnant women. The noninvasive and discrete nature of these sensors, integrated into devices accumulating large datasets, offers a unique opportunity to study the dynamic changes in movement patterns during the rapid physical transformations induced by pregnancy. However, the final cut of the third trimester of pregnancy, particularly the first stage of labor up to delivery, remains underexplored. The growing popularity of “walking epidural”, a neuraxial analgesia method allowing motor function preservation, ambulation, and free movement throughout labor and during delivery, opens new opportunities to study the biomechanics of labor using inertial sensors. Critical research gaps exist in parturient fall prediction and detection during walking epidural and understanding pain dynamics during labor, particularly in the presence of pelvic girdle pain. The analysis of fetal descent, upright positions, and their relationship with dynamic pelvic movements facilitated by walking during labor is another area where inertial sensors can play an interesting role. Moreover, as contemporary obstetrics advocate for less restricted or non-restricted movements during labor, the role of inertial sensors in objectively measuring the quantity and quality of women’s movements becomes increasingly important. This includes studying the impact of epidural analgesia on maternal mobility, walking patterns, and associated obstetrical outcomes. In this paper, the potential use of wearable inertial sensors for gait analysis in the first stage of labor is discussed. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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27 pages, 16767 KiB

Open AccessArticle

Impact-Aware Foot Motion Reconstruction and Ramp/Stair Detection Using One Foot-Mounted Inertial Measurement Unit

by Yisen Wang, Katherine H. Fehr and Peter G. Adamczyk

Sensors 2024, 24(5), 1480; https://doi.org/10.3390/s24051480 - 24 Feb 2024

Viewed by 534

Abstract

Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are

0.03 \pm 0.08 c m

on level ground,

0.95 \pm 0.37 c m

on ramps, and

1.27 \pm 1.22 c m

on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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18 pages, 3728 KiB

Open AccessCommunication

A Circular, Wireless Surface-Electromyography Array

by Kenneth Deprez, Eliah De Baecke, Mauranne Tijskens, Ruben Schoeters, Maarten Velghe and Arno Thielens

Sensors 2024, 24(4), 1119; https://doi.org/10.3390/s24041119 - 08 Feb 2024

Viewed by 1275

Abstract

Commercial, high-tech upper limb prostheses offer a lot of functionality and are equipped with high-grade control mechanisms. However, they are relatively expensive and are not accessible to the majority of amputees. Therefore, more affordable, accessible, open-source, and 3D-printable alternatives are being developed. A commonly proposed approach to control these prostheses is to use bio-potentials generated by skeletal muscles, which can be measured using surface electromyography (sEMG). However, this control mechanism either lacks accuracy when a single sEMG sensor is used or involves the use of wires to connect to an array of multiple nodes, which hinders patients’ movements. In order to mitigate these issues, we have developed a circular, wireless s-EMG array that is able to collect sEMG potentials on an array of electrodes that can be spread (not) uniformly around the circumference of a patient’s arm. The modular sEMG system is combined with a Bluetooth Low Energy System on Chip, motion sensors, and a battery. We have benchmarked this system with a commercial, wired, state-of-the-art alternative and found an r = 0.98 (p < 0.01) Spearman correlation between the root-mean-squared (RMS) amplitude of sEMG measurements measured by both devices for the same set of 20 reference gestures, demonstrating that the system is accurate in measuring sEMG. Additionally, we have demonstrated that the RMS amplitudes of sEMG measurements between the different nodes within the array are uncorrelated, indicating that they contain independent information that can be used for higher accuracy in gesture recognition. We show this by training a random forest classifier that can distinguish between 6 gestures with an accuracy of 97%. This work is important for a large and growing group of amputees whose quality of life could be improved using this technology. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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12 pages, 1810 KiB

Open AccessArticle

Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee–Ankle–Foot Orthosis

by Sumin Yang, Bummo Koo, Seunghee Lee, Dae-Jin Jang, Hyunjun Shin, Hyuk-Jae Choi and Youngho Kim

Sensors 2024, 24(3), 964; https://doi.org/10.3390/s24030964 - 01 Feb 2024

Viewed by 614

Abstract

Gait event detection is essential for controlling an orthosis and assessing the patient’s gait. In this study, patients wearing an electromechanical (EM) knee–ankle–foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of −2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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15 pages, 4357 KiB

Open AccessArticle

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks

by Elkyn Alexander Belalcazar-Bolaños, Diego Torricelli and José L. Pons

Sensors 2023, 23(24), 9683; https://doi.org/10.3390/s23249683 - 07 Dec 2023

Viewed by 1059

Abstract

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in

98 %

of the cases, which represents a significant improvement over current threshold-based detection algorithms. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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12 pages, 640 KiB

Open AccessArticle

SMART System in the Assessment of Exercise Tolerance in Adults

by Katarzyna Nierwińska, Andrzej Myśliwiec, Anna Konarska-Rawluk, Anna Lipowicz, Andrzej Małecki and Andrzej Knapik

Sensors 2023, 23(24), 9624; https://doi.org/10.3390/s23249624 - 05 Dec 2023

Viewed by 724

Abstract

Health-oriented physical activity should meet two key criteria: safety and an optimal level of exercise. The system of monitoring and rationalization of training (SMART) was designed to meet them. SMART integrates a custom-configured inertial measurement unit (IMU) and a sensor with real-time heart rate measurement (HR) using a proprietary computer application. SMART was used to evaluate the safety and exercise load with 115 study participants: 51 women (44.35%) and 64 men (55.65%) aged 19 to 65 years. The exercise test was the 6MWT test. In 35% of the participants, the mean HR exceeded the recognized safe limit of HR 75% max. Ongoing monitoring of HR allows for optimal exercise and its safety. Step count data were collected from the SMART system. The average step length was calculated by dividing the distance by the number of steps. The aim of the present study was to assess the risk of excessive cardiovascular stress during the 6MWT test using the SMART system. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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12 pages, 3413 KiB

Open AccessArticle

Can Wearable Sensors Provide Accurate and Reliable 3D Tibiofemoral Angle Estimates during Dynamic Actions?

by Mirel Ajdaroski and Amanda Esquivel

Sensors 2023, 23(14), 6627; https://doi.org/10.3390/s23146627 - 24 Jul 2023

Viewed by 977

Abstract

The ability to accurately measure tibiofemoral angles during various dynamic activities is of clinical interest. The purpose of this study was to determine if inertial measurement units (IMUs) can provide accurate and reliable angle estimates during dynamic actions. A tuned quaternion conversion (TQC) method tuned to dynamics actions was used to calculate Euler angles based on IMU data, and these calculated angles were compared to a motion capture system (our “gold” standard) and a commercially available sensor fusion algorithm. Nine healthy athletes were instrumented with APDM Opal IMUs and asked to perform nine dynamic actions; five participants were used in training the parameters of the TQC method, with the remaining four being used to test validity. Accuracy was based on the root mean square error (RMSE) and reliability was based on the Bland–Altman limits of agreement (LoA). Improvement across all three orthogonal angles was observed as the TQC method was able to more accurately (lower RMSE) and more reliably (smaller LoA) estimate an angle than the commercially available algorithm. No significant difference was observed between the TQC method and the motion capture system in any of the three angles (p < 0.05). It may be feasible to use this method to track tibiofemoral angles with higher accuracy and reliability than the commercially available sensor fusion algorithm. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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17 pages, 1903 KiB

Open AccessArticle

Evaluation of Upper Body and Lower Limbs Kinematics through an IMU-Based Medical System: A Comparative Study with the Optoelectronic System

by Serena Cerfoglio, Paolo Capodaglio, Paolo Rossi, Ilaria Conforti, Valentina D’Angeli, Elia Milani, Manuela Galli and Veronica Cimolin

Sensors 2023, 23(13), 6156; https://doi.org/10.3390/s23136156 - 05 Jul 2023

Cited by 4 | Viewed by 1899

Abstract

In recent years, the use of inertial-based systems has been applied to remote rehabilitation, opening new perspectives for outpatient assessment. In this study, we assessed the accuracy and the concurrent validity of the angular measurements provided by an inertial-based device for rehabilitation with respect to the state-of-the-art system for motion tracking. Data were simultaneously collected with the two systems across a set of exercises for trunk and lower limbs, performed by 21 healthy participants. Additionally, the sensitivity of the inertial measurement unit (IMU)-based system to its malpositioning was assessed. Root mean square error (RMSE) was used to explore the differences in the outputs of the two systems in terms of range of motion (ROM), and their agreement was assessed via Pearson’s correlation coefficient (PCC) and Lin’s concordance correlation coefficient (CCC). The results showed that the IMU-based system was able to assess upper-body and lower-limb kinematics with a mean error in general lower than 5° and that its measurements were moderately biased by its mispositioning. Although the system does not seem to be suitable for analysis requiring a high level of detail, the findings of this study support the application of the device in rehabilitation programs in unsupervised settings, providing reliable data to remotely monitor the progress of the rehabilitation pathway and change in patient’s motor function. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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22 pages, 2648 KiB

Open AccessArticle

A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living

by Samer A. Mohamed and Uriel Martinez-Hernandez

Sensors 2023, 23(13), 5854; https://doi.org/10.3390/s23135854 - 24 Jun 2023

Viewed by 1132

Abstract

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional–long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85

μ

s. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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20 pages, 5760 KiB

Open AccessArticle

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson’s Disease

by Stefano Filippo Castiglia, Dante Trabassi, Carmela Conte, Alberto Ranavolo, Gianluca Coppola, Gabriele Sebastianelli, Chiara Abagnale, Francesca Barone, Federico Bighiani, Roberto De Icco, Cristina Tassorelli and Mariano Serrao

Sensors 2023, 23(10), 4983; https://doi.org/10.3390/s23104983 - 22 May 2023

Cited by 4 | Viewed by 1471

Abstract

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson’s disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1–6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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23 pages, 4991 KiB

Open AccessArticle

Balance Assessment Using a Smartwatch Inertial Measurement Unit with Principal Component Analysis for Anatomical Calibration

by Benjamin M. Presley, Jeffrey C. Sklar, Scott J. Hazelwood, Britta Berg-Johansen and Stephen M. Klisch

Sensors 2023, 23(10), 4585; https://doi.org/10.3390/s23104585 - 09 May 2023

Viewed by 1494

Abstract

Balance assessment, or posturography, tracks and prevents health complications for a variety of groups with balance impairment, including the elderly population and patients with traumatic brain injury. Wearables can revolutionize state-of-the-art posturography methods, which have recently shifted focus to clinical validation of strictly positioned inertial measurement units (IMUs) as replacements for force-plate systems. Yet, modern anatomical calibration (i.e., sensor-to-segment alignment) methods have not been utilized in inertial-based posturography studies. Functional calibration methods can replace the need for strict placement of inertial measurement units, which may be tedious or confusing for certain users. In this study, balance-related metrics from a smartwatch IMU were tested against a strictly placed IMU after using a functional calibration method. The smartwatch and strictly placed IMUs were strongly correlated in clinically relevant posturography scores (r = 0.861–0.970, p < 0.001). Additionally, the smartwatch was able to detect significant variance (p < 0.001) between pose-type scores from the mediolateral (ML) acceleration data and anterior-posterior (AP) rotation data. With this calibration method, a large problem with inertial-based posturography has been addressed, and wearable, “at-home” balance-assessment technology is within possibility. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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23 pages, 26418 KiB

Open AccessArticle

Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach

by Chenhui Huang, Kenichiro Fukushi, Zhenwei Wang, Fumiyuki Nihey, Hiroshi Kajitani and Kentaro Nakahara

Sensors 2022, 22(1), 351; https://doi.org/10.3390/s22010351 - 04 Jan 2022

Cited by 8 | Viewed by 2468

Abstract

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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22 pages, 2408 KiB

Open AccessSystematic Review

Current Knowledge about ActiGraph GT9X Link Activity Monitor Accuracy and Validity in Measuring Steps and Energy Expenditure: A Systematic Review

by Quentin Suau, Edoardo Bianchini, Alexandre Bellier, Matthias Chardon, Tracy Milane, Clint Hansen and Nicolas Vuillerme

Sensors 2024, 24(3), 825; https://doi.org/10.3390/s24030825 - 26 Jan 2024

Viewed by 979

Abstract

Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a “gold standard” and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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31 pages, 989 KiB

Open AccessSystematic Review

Instrumented Timed Up and Go Test (iTUG)—More Than Assessing Time to Predict Falls: A Systematic Review

by Paulina Ortega-Bastidas, Britam Gómez, Pablo Aqueveque, Soledad Luarte-Martínez and Roberto Cano-de-la-Cuerda

Sensors 2023, 23(7), 3426; https://doi.org/10.3390/s23073426 - 24 Mar 2023

Cited by 8 | Viewed by 6554

Abstract

The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test’s predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson’s disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings. Full article

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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