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16 pages, 2031 KiB

Open AccessArticle

Assessing Motor Variability during Squat: The Reliability of Inertial Devices in Resistance Training

by Fernando García-Aguilar, Miguel López-Fernández, David Barbado, Francisco J. Moreno and Rafael Sabido

Sensors 2024, 24(6), 1951; https://doi.org/10.3390/s24061951 - 19 Mar 2024

Cited by 1 | Viewed by 1776

Abstract

Movement control can be an indicator of how challenging a task is for the athlete, and can provide useful information to improve training efficiency and prevent injuries. This study was carried out to determine whether inertial measurement units (IMU) can provide reliable information [...] Read more.

Movement control can be an indicator of how challenging a task is for the athlete, and can provide useful information to improve training efficiency and prevent injuries. This study was carried out to determine whether inertial measurement units (IMU) can provide reliable information on motion variability during strength exercises, focusing on the squat. Sixty-six healthy, strength-trained young adults completed a two-day protocol, where the variability in the squat movement was analyzed at two different loads (30% and 70% of one repetition maximum) using inertial measurement units and a force platform. The time series from IMUs and force platforms were analyzed using linear (standard deviation) and non-linear (detrended fluctuation analysis, sample entropy and fuzzy entropy) measures. Reliability was analyzed for both IMU and force platform using the intraclass correlation coefficient and the standard error of measurement. Standard deviation, detrended fluctuation analysis, sample entropy, and fuzzy entropy from the IMUs time series showed moderate to good reliability values (ICC: 0.50–0.85) and an acceptable error. The study concludes that IMUs are reliable tools for analyzing movement variability in strength exercises, providing accessible options for performance monitoring and training optimization. These findings have implications for the design of more effective strength training programs, emphasizing the importance of movement control in enhancing athletic performance and reducing injury risks. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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28 pages, 2021 KiB

Open AccessArticle

Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition

by Harish Haresamudram, Irfan Essa and Thomas Plötz

Sensors 2024, 24(4), 1238; https://doi.org/10.3390/s24041238 - 15 Feb 2024

Cited by 5 | Viewed by 1848

Abstract

Human activity recognition (HAR) in wearable and ubiquitous computing typically involves translating sensor readings into feature representations, either derived through dedicated pre-processing procedures or integrated into end-to-end learning approaches. Independent of their origin, for the vast majority of contemporary HAR methods and applications, [...] Read more.

Human activity recognition (HAR) in wearable and ubiquitous computing typically involves translating sensor readings into feature representations, either derived through dedicated pre-processing procedures or integrated into end-to-end learning approaches. Independent of their origin, for the vast majority of contemporary HAR methods and applications, those feature representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches had been explored—primarily motivated by the desire to minimize computational requirements on HAR, but also with a view on applications beyond mere activity classification, such as, for example, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting data representations with detrimental effects on downstream analysis tasks. Times have changed, and in this paper, we propose a return to discretized representations. We adopt and apply recent advancements in vector quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, where the index comprises the discrete representation, resulting in recognition performance that is at least on par with their contemporary, continuous counterparts—often surpassing them. Therefore, this work presents a proof of concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation of a suite of wearable-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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19 pages, 2107 KiB

Open AccessArticle

Characterization of Walking in Mild Parkinson’s Disease: Reliability, Validity and Discriminant Ability of the Six-Minute Walk Test Instrumented with a Single Inertial Sensor

by Gaia Bailo, Francesca Lea Saibene, Virginia Bandini, Pietro Arcuri, Anna Salvatore, Mario Meloni, Anna Castagna, Jorge Navarro, Tiziana Lencioni, Maurizio Ferrarin and Ilaria Carpinella

Sensors 2024, 24(2), 662; https://doi.org/10.3390/s24020662 - 20 Jan 2024

Cited by 12 | Viewed by 3112

Abstract

Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson’s disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could [...] Read more.

Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson’s disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could provide additional quantitative and objective information about gait quality complementing standard clinical outcome. This study aims to evaluate the test–retest reliability, validity and discriminant ability of gait parameters obtained by a single IMU during the 6MWT in subjects with mild PD. Twenty-two people with mild PD and ten healthy persons performed the 6MWT wearing an IMU placed on the lower trunk. Features belonging to rhythm and pace, variability, regularity, jerkiness, intensity, dynamic instability and symmetry domains were computed. Test–retest reliability was evaluated through the Intraclass Correlation Coefficient (ICC), while concurrent validity was determined by Spearman’s coefficient. Mann–Whitney U test and the Area Under the receiver operating characteristic Curve (AUC) were then applied to assess the discriminant ability of reliable and valid parameters. Results showed an overall high reliability (ICC ≥ 0.75) and multiple significant correlations with clinical scales in all domains. Several features exhibited significant alterations compared to healthy controls. Our findings suggested that the 6MWT instrumented with a single IMU can provide reliable and valid information about gait features in individuals with PD. This offers objective details about gait quality and the possibility of being integrated into clinical evaluations to better define walking rehabilitation strategies in a quick and easy way. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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11 pages, 695 KiB

Open AccessArticle

Personal and Clinical Determinants of Brace-Wearing Time in Adolescents with Idiopathic Scoliosis

by Giulia Fregna, Sara Rossi Raccagni, Alessandra Negrini, Fabio Zaina and Stefano Negrini

Sensors 2024, 24(1), 116; https://doi.org/10.3390/s24010116 - 25 Dec 2023

Cited by 3 | Viewed by 3922

Abstract

Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine and trunk deformity. Bracing is an effective treatment for medium-degree curves. Thermal sensors help monitor patients’ adherence (compliance), a critical issue in bracing treatment. Some studies investigated adherence determinants but rarely through sensors or in [...] Read more.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine and trunk deformity. Bracing is an effective treatment for medium-degree curves. Thermal sensors help monitor patients’ adherence (compliance), a critical issue in bracing treatment. Some studies investigated adherence determinants but rarely through sensors or in highly adherent cohorts. We aimed to verify the influence of personal and clinical variables routinely registered by physicians on adherence to brace treatment in a large cohort of consecutive AIS patients from a highly adherent cohort. We performed a cross-sectional study of patients consecutively recruited in the last three years at a tertiary referral institute and treated with braces for one year. To ensure high adherence, for years, we have provided specific support to brace treatment through a series of cognitive-behavioural interventions for patients and parents. We used iButton thermal sensor systematic data collection to precisely analyse the real brace-wearing time. We included 514 adolescents, age 13.8 ± 1.6, with the worst scoliosis curve of 34.5 ± 10.3° Cobb. We found a 95% (95CI 60–101%) adherence to the brace prescription of 21.9 ± 1.7 h per day. Determinants included gender (91% vs. 84%; females vs. males) and age < 14 years (92% vs. 88%). Brace hours prescription, BMI, and all clinical variables (worst curve Cobb degrees, angle of trunk rotation, and TRACE index for aesthetics) did not influence adherence. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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12 pages, 2452 KiB

Open AccessArticle

Predicting Tissue Loads in Running from Inertial Measurement Units

by John Rasmussen, Sebastian Skejø and Rasmus Plenge Waagepetersen

Sensors 2023, 23(24), 9836; https://doi.org/10.3390/s23249836 - 15 Dec 2023

Cited by 1 | Viewed by 2038

Abstract

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be [...] Read more.

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. Methods: We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. Results: Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. Conclusions: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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23 pages, 6484 KiB

Open AccessArticle

HARE: Unifying the Human Activity Recognition Engineering Workflow

by Orhan Konak, Robin van de Water, Valentin Döring, Tobias Fiedler, Lucas Liebe, Leander Masopust, Kirill Postnov, Franz Sauerwald, Felix Treykorn, Alexander Wischmann, Hristijan Gjoreski, Mitja Luštrek and Bert Arnrich

Sensors 2023, 23(23), 9571; https://doi.org/10.3390/s23239571 - 2 Dec 2023

Cited by 2 | Viewed by 2894

Abstract

Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data [...] Read more.

Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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16 pages, 3274 KiB

Open AccessArticle

Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities

by Valeria Filippou, Michael R. Backhouse, Anthony C. Redmond and David C. Wong

Sensors 2023, 23(22), 9061; https://doi.org/10.3390/s23229061 - 9 Nov 2023

Viewed by 1777

Abstract

This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, [...] Read more.

This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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15 pages, 1945 KiB

Open AccessArticle

Shoulder Range of Motion Measurement Using Inertial Measurement Unit–Concurrent Validity and Reliability

by Jakub Kaszyński, Cezary Baka, Martyna Białecka and Przemysław Lubiatowski

Sensors 2023, 23(17), 7499; https://doi.org/10.3390/s23177499 - 29 Aug 2023

Cited by 9 | Viewed by 2795

Abstract

This study aimed to evaluate the reliability of the RSQ Motion sensor and its validity against the Propriometer and electronic goniometer in measuring the active range of motion (ROM) of the shoulder. The study included 15 volunteers (mean age 24.73 ± 3.31) without [...] Read more.

This study aimed to evaluate the reliability of the RSQ Motion sensor and its validity against the Propriometer and electronic goniometer in measuring the active range of motion (ROM) of the shoulder. The study included 15 volunteers (mean age 24.73 ± 3.31) without any clinical symptoms with no history of trauma, disease, or surgery to the upper limb. Four movements were tested: flexion, abduction, external and internal rotation. Validation was assessed in the full range of active shoulder motion. Reliability was revised in full active ROM, a fixed angle of 90 degrees for flexion and abduction, and 45 degrees for internal and external rotation. Each participant was assessed three times: on the first day by both testers and on the second day only by one of the testers. Goniometer and RSQ Motion sensors showed moderate to excellent correlation for all tested movements (ICC 0.61–0.97, LOA < 23 degrees). Analysis of inter-rater reliability showed good to excellent agreement between both testers (ICC 0.74–0.97, LOA 13–35 degrees). Analysis of intra-rater reliability showed moderate to a good agreement (ICC 0.7–0.88, LOA 22–37 degrees). The shoulder internal and external rotation measurement with RSQ Motion sensors is valid and reliable. There is a high level of inter-rater and intra-rater reliability for the RSQ Motion sensors and Propriometer. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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9 pages, 560 KiB

Open AccessArticle

Does Accelerometry at the Centre of Mass Accurately Predict the Gait Energy Expenditure in Patients with Hemiparesis?

by Léo Barassin, Didier Pradon, Nicolas Roche and Jean Slawinski

Sensors 2023, 23(16), 7177; https://doi.org/10.3390/s23167177 - 15 Aug 2023

Cited by 1 | Viewed by 1283

Abstract

Background: The aim of this study was to compare energy expenditure (EE) predicted by accelerometery (EE_Acc) with indirect calorimetry (EE_META) in individuals with hemiparesis. Methods: Twenty-four participants (12 with stroke and 12 healthy controls) performed a six-minute walk test [...] Read more.

Background: The aim of this study was to compare energy expenditure (EE) predicted by accelerometery (EE_Acc) with indirect calorimetry (EE_META) in individuals with hemiparesis. Methods: Twenty-four participants (12 with stroke and 12 healthy controls) performed a six-minute walk test (6MWT) during which EE_META was measured using a portable indirect calorimetry system and EE_ACC was calculated using Bouten’s equation (1993) with data from a three-axis accelerometer positioned between L3 and L4. Results: The median EE_META was 9.85 [8.18;11.89] W·kg⁻¹ in the stroke group and 5.0 [4.56;5.46] W·kg⁻¹ in the control group. The median EE_ACC was 8.57 [7.86;11.24] W·kg⁻¹ in the control group and 8.2 [7.05;9.56] W·kg⁻¹ in the stroke group. The EE_ACC and EE_META were not significantly correlated in either the control (p = 0.8) or the stroke groups (p = 0.06). The Bland–Altman method showed a mean difference of 1.77 ± 3.65 W·kg⁻¹ between the EE_ACC and EE_META in the stroke group and −2.08 ± 1.59 W·kg⁻¹ in the controls. Conclusions: The accuracy of the predicted EE, based on the accelerometer and the equations proposed by Bouten et al., was low in individuals with hemiparesis and impaired gait. This combination (sensor and Bouten’s equation) is not yet suitable for use as a stand-alone measure in clinical practice for the evaluation of hemiparetic patients. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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16 pages, 5753 KiB

Open AccessArticle

Biomechanical Load of Neck and Lumbar Joints in Open-Surgery Training

by Ce Zhang, Charlotte Christina Roossien, Gijsbertus Jacob Verkerke, Han Houdijk, Juha M. Hijmans and Christian Greve

Sensors 2023, 23(15), 6974; https://doi.org/10.3390/s23156974 - 5 Aug 2023

Cited by 5 | Viewed by 2144

Abstract

The prevalence of musculoskeletal symptoms (MSS) like neck and back pain is high among open-surgery surgeons. Prolonged working in the same posture and unfavourable postures are biomechanical risk factors for developing MSS. Ergonomic devices such as exoskeletons are possible solutions that can reduce [...] Read more.

The prevalence of musculoskeletal symptoms (MSS) like neck and back pain is high among open-surgery surgeons. Prolonged working in the same posture and unfavourable postures are biomechanical risk factors for developing MSS. Ergonomic devices such as exoskeletons are possible solutions that can reduce muscle and joint load. To design effective exoskeletons for surgeons, one needs to quantify which neck and trunk postures are seen and how much support during actual surgery is required. Hence, this study aimed to establish the biomechanical profile of neck and trunk postures and neck and lumbar joint loads during open surgery (training). Eight surgical trainees volunteered to participate in this research. Neck and trunk segment orientations were recorded using an inertial measurement unit (IMU) system during open surgery (training). Neck and lumbar joint kinematics, joint moments and compression forces were computed using OpenSim modelling software and a musculoskeletal model. Histograms were used to illustrate the joint angle and load distribution of the neck and lumbar joints over time. During open surgery, the neck flexion angle was 71.6% of the total duration in the range of 10~40 degrees, and lumbar flexion was 68.9% of the duration in the range of 10~30 degrees. The normalized neck and lumbar flexion moments were 53.8% and 35.5% of the time in the range of 0.04~0.06 Nm/kg and 0.4~0.6 Nm/kg, respectively. Furthermore, the neck and lumbar compression forces were 32.9% and 38.2% of the time in the range of 2.0~2.5 N/kg and 15~20 N/kg, respectively. In contrast to exoskeletons used for heavy lifting tasks, exoskeletons designed for surgeons exhibit lower support torque requirements while additional degrees of freedom (DOF) are needed to accommodate combinations of neck and trunk postures. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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16 pages, 3721 KiB

Open AccessArticle

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks

by Ismael Espinoza Jaramillo, Channabasava Chola, Jin-Gyun Jeong, Ji-Heon Oh, Hwanseok Jung, Jin-Hyuk Lee, Won Hee Lee and Tae-Seong Kim

Sensors 2023, 23(14), 6491; https://doi.org/10.3390/s23146491 - 18 Jul 2023

Cited by 12 | Viewed by 4147

Abstract

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In [...] Read more.

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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12 pages, 4165 KiB

Open AccessArticle

Cervical Range of Motion Assessment through Inertial Technology: A Validity and Reliability Study

by Martina Palmieri, Lucia Donno, Veronica Cimolin and Manuela Galli

Sensors 2023, 23(13), 6013; https://doi.org/10.3390/s23136013 - 28 Jun 2023

Cited by 9 | Viewed by 3288

Abstract

Inertial technology has spread widely for its comfortable use and adaptability to various motor tasks. The main objective of this study was to assess the validity of inertial measurements of the cervical spine range of motion (CROM) when compared to that of the [...] Read more.

Inertial technology has spread widely for its comfortable use and adaptability to various motor tasks. The main objective of this study was to assess the validity of inertial measurements of the cervical spine range of motion (CROM) when compared to that of the optoelectronic system in a group of healthy individuals. A further aim of this study was to determine the optimal placement of the inertial sensor in terms of reliability of the measure, comparing measurements obtained from the same device placed at the second cervical vertebra (C2), the forehead (F) and the external occipital protuberance (EOP). Twenty healthy subjects were recruited and asked to perform flexion–extension, lateral bending, and axial rotation movements of the head. Outcome measurements of interest were CROM and mean angular velocities for each cervical movement. Results showed that inertial measurements have good reliability (0.75 < ICC < 0.9). Excellent reliability (ICC > 0.9) was found in both flexion and right lateral bending angles. All parameters extracted with EOP placement showed ICC > 0.62, while ICC < 0.5 was found in lateral bending mean angular velocities both for F and C2 placements. Therefore, the optimal sensor’s positioning emerged to be EOP. These results suggest that inertial technology could be useful and reliable for the evaluation of the CROM. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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12 pages, 4521 KiB

Open AccessArticle

Shoulder Range of Motion Measurement Using Inertial Measurement Unit—Validation with a Robot Arm

by Martyna Białecka, Kacper Gruszczyński, Paweł Cisowski, Jakub Kaszyński, Cezary Baka and Przemysław Lubiatowski

Sensors 2023, 23(12), 5364; https://doi.org/10.3390/s23125364 - 6 Jun 2023

Cited by 7 | Viewed by 3395

Abstract

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion [...] Read more.

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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16 pages, 1762 KiB

Open AccessArticle

A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data

by Jeroen G. V. Habets, Rachel K. Spooner, Varvara Mathiopoulou, Lucia K. Feldmann, Johannes L. Busch, Jan Roediger, Bahne H. Bahners, Alfons Schnitzler, Esther Florin and Andrea A. Kühn

Sensors 2023, 23(11), 5238; https://doi.org/10.3390/s23115238 - 31 May 2023

Cited by 5 | Viewed by 3671

Abstract

Bradykinesia is a cardinal hallmark of Parkinson’s disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools [...] Read more.

Bradykinesia is a cardinal hallmark of Parkinson’s disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson’s Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson’s disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Review

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13 pages, 1268 KiB

Open AccessReview

Influence of Specific Interventions on Bracing Compliance in Adolescents with Idiopathic Scoliosis—A Systematic Review of Papers Including Sensors’ Monitoring

by Claudio Cordani, Lia Malisano, Francesca Febbo, Giorgia Giranio, Matteo Johann Del Furia, Sabrina Donzelli and Stefano Negrini

Sensors 2023, 23(17), 7660; https://doi.org/10.3390/s23177660 - 4 Sep 2023

Cited by 6 | Viewed by 4549

Abstract

Adolescent idiopathic scoliosis (AIS) is a common disease that, in many cases, can be conservatively treated through bracing. High adherence to brace prescription is fundamental to gaining the maximum benefit from this treatment approach. Wearable sensors are available that objectively monitor the brace-wearing [...] Read more.

Adolescent idiopathic scoliosis (AIS) is a common disease that, in many cases, can be conservatively treated through bracing. High adherence to brace prescription is fundamental to gaining the maximum benefit from this treatment approach. Wearable sensors are available that objectively monitor the brace-wearing time, but their use, combined with other interventions, is poorly investigated. The aims of the current review are as follows: (i) to summarize the real compliance with bracing reported by studies using sensors; (ii) to find out the real brace wearing rate through objective electronic monitoring; (iii) to verify if interventions made to increase adherence to bracing can be effective according to the published literature. We conducted a systematic review of the literature published on Medline, EMBASE, CINAHL, Scopus, CENTRAL, and Web of Science. We identified 466 articles and included examples articles, which had a low to good methodological quality. We found that compliance a greatly varied between 21.8 and 93.9% (weighted average: 58.8%), real brace wearing time varied between 5.7 and 21 h per day (weighted average 13.3), and specific interventions seemed to improve both outcomes, with compliance increasing from 58.5 to 66% and brace wearing increasing from 11.9 to 15.1 h per day. Two comparative studies showed positive effects of stand-alone counseling and information on the sensors’ presence when added to counseling. Sensors proved to be useful tools for objectively and continuously monitoring adherence to therapy in everyday clinical practice. Specific interventions, like the use of sensors, counseling, education, and exercises, could increase compliance. However, further studies using high-quality designs should be conducted in this field. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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