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15 pages, 801 KiB

Open AccessArticle

Associations Between Physiological Determinants and GNSS-Derived Technical Characteristics in Cross-Country Roller Skiing

by Shunya Uda, Naoto Miyamoto, Wako Kajiwara, Hiroshi Nakano, Keisuke Onodera, Ryoji Horimoto, Takato Okada and Masaki Takeda

Sensors 2025, 25(8), 2521; https://doi.org/10.3390/s25082521 - 17 Apr 2025

Viewed by 176

Abstract

This study aimed to examine how physiological determinants are associated with skiing technique and race performance in cross-country roller skiing by integrating motion data obtained via a Global Navigation Satellite System (GNSS) with laboratory-based physiological assessments. Nineteen well-trained male skiers completed a 10 [...] Read more.

This study aimed to examine how physiological determinants are associated with skiing technique and race performance in cross-country roller skiing by integrating motion data obtained via a Global Navigation Satellite System (GNSS) with laboratory-based physiological assessments. Nineteen well-trained male skiers completed a 10 km roller ski race, during which skiing velocity, cycle length, cycle time, and sub-technique usage were measured using GNSS. Whole-body and upper-body endurance and power were evaluated on the treadmill and ski ergometer. Time to exhaustion during the double poling test (r = −0.84, p < 0.01) and VO_2max from the pole walk and run test (r = −0.72, p < 0.01) were the strongest predictors of race performance, and both were significantly associated with skiing velocity (VO_2max: r = 0.79, p < 0.01; TTE-DPT: r = 0.81, p < 0.01) and cycle length (VO_2max: r = 0.58, p < 0.01; TTE-DPT: r = 0.47, p < 0.05) in the most frequently used technique. These findings suggest that the development of both whole-body and upper-body endurance plays a crucial role in improving technical efficiency and race performances. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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16 pages, 2968 KiB

Open AccessArticle

Combining 24-Hour Continuous Monitoring of Time-Locked Heart Rate, Physical Activity and Gait in Older Adults: Preliminary Findings

by Eitan E. Asher, Eran Gazit, Nasim Montazeri, Elisa Mejía-Mejía, Rachel Godfrey, David A. Bennett, Veronique G. VanderHorst, Aron S. Buchman, Andrew S. P. Lim and Jeffrey M. Hausdorff

Sensors 2025, 25(6), 1945; https://doi.org/10.3390/s25061945 - 20 Mar 2025

Viewed by 299

Abstract

Hemodynamic homeostasis is essential for adapting the heart rate (HR) to postural and physiological changes during daily activities. Traditional HR monitoring, such as 24 hour (h) Holter monitoring, provides important information on homeostasis during daily living. However, this approach lacks concurrent activity recording, [...] Read more.

Hemodynamic homeostasis is essential for adapting the heart rate (HR) to postural and physiological changes during daily activities. Traditional HR monitoring, such as 24 hour (h) Holter monitoring, provides important information on homeostasis during daily living. However, this approach lacks concurrent activity recording, limiting insights into hemodynamic adaptation and our ability to interpret changes in HR. To address this, we utilized a novel wearable sensor system (ANNE^@Sibel) to capture time-locked HR and daily activity (i.e., lying, sitting, standing, walking) data in 105 community-dwelling older adults. We developed custom tools to extract 24 h time-locked measurements and introduced a “heart rate response score” (HRRS), based on root Jensen–Shannon divergence, to quantify HR changes relative to activity. As expected, we found a progressive HR increase with more vigorous activities, though individual responses varied widely, highlighting heterogeneous HR adaptations. The HRRS (mean: 0.38 ± 0.14; min: −0.11; max: 0.74) summarized person-specific HR changes and was correlated with several clinical measures, including systolic blood pressure changes during postural transitions (r = 0.325, p = 0.003), orthostatic hypotension status, and calcium channel blocker medication use. These findings demonstrate the potential of unobtrusive sensors in remote phenotyping as a means of providing valuable physiological and behavioral data to enhance the quantitative description of aging phenotypes. This approach could enhance personalized medicine by informing targeted interventions based on hemodynamic adaptations during everyday activities. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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14 pages, 1456 KiB

Open AccessArticle

Characterization of Muscle Activation and Muscle Synergism in the ‘Forward Lunge’ Gait Movement of Badminton Players Using Surface Electromyography Sensors

by Jian Jiang, Haojie Li and Chen Xiu

Sensors 2025, 25(6), 1644; https://doi.org/10.3390/s25061644 - 7 Mar 2025

Viewed by 454

Abstract

The ‘forward lunge’ is a crucial movement in badminton that demands effective muscle activation and coordination. This study compared the muscle activation patterns of professional and amateur male badminton players during this movement. A total of 24 players (12 professionals and 12 amateurs) [...] Read more.

The ‘forward lunge’ is a crucial movement in badminton that demands effective muscle activation and coordination. This study compared the muscle activation patterns of professional and amateur male badminton players during this movement. A total of 24 players (12 professionals and 12 amateurs) participated, with surface electromyography (sEMG) used to measure the activity of 12 muscles on the right side during the lunge. The movement was divided into swing and support phases based on ground reaction force data. The sEMG signals were analyzed using integral EMG (iEMG) and root-mean-square (RMS) amplitude, and muscle synergy patterns were extracted via non-negative matrix factorization (NNMF) and k-means clustering. The results showed significantly higher iEMG and RMS values in muscles such as the gastrocnemius, biceps femoris, gluteus maximus, external oblique, and latissimus dorsi in professional players (p < 0.05), while no significant differences were observed in the tibialis anterior, vastus medialis, vastus lateralis, deltoideus, biceps, and soleus muscles. Muscle synergy analysis revealed three activation patterns in the professional group, compared to two in the amateur group. The additional synergy pattern in the professional players involved greater recruitment of lower limb and core muscles, especially during the support phase. In contrast, the amateur group showed earlier muscle activation but exhibited less efficient coordination. These findings suggest that muscle activation and coordination patterns in the forward lunge are influenced by playing level, highlighting the importance of lower limb and core training for badminton athletes to optimize performance and reduce injury risk. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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20 pages, 1941 KiB

Open AccessArticle

High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data

by Annemarie F. Laudanski, Arne Küderle, Felix Kluge, Bjoern M. Eskofier and Stacey M. Acker

Sensors 2025, 25(4), 1083; https://doi.org/10.3390/s25041083 - 11 Feb 2025

Viewed by 701

Abstract

Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. The objective of this study was to develop a sensor-based framework [...] Read more.

Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. The objective of this study was to develop a sensor-based framework for the detection and measurement of high-flexion postures frequently adopted in occupational settings. IMU-based joint angle estimates for the ankle, knee, and hip were time and scale normalized prior to being input to a multi-dimensional Dynamic Time Warping (mDTW) distance-based Nearest Neighbour algorithm for the identification of twelve postures. Data from 50 participants were divided to develop and evaluate the mDTW model. Overall accuracies of 82.3% and 55.6% were reached when classifying movements from the testing and validation datasets, respectively, which increased to 86% and 74.6% when adjusting for imbalances between classification groups. The highest misclassification rates occurred between flatfoot squatting, heels-up squatting, and stooping, while the model was incapable of identifying sequences of walking based on a single stride template. The developed mDTW model proved robust in identifying high-flexion postures performed by participants both included and precluded from algorithm development, indicating its strong potential for the quantitative measurement of postural adoption in real-world settings. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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17 pages, 300 KiB

Open AccessArticle

Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson’s Disease Mobility Assessments

by Rana M. Khalil, Lisa M. Shulman, Ann L. Gruber-Baldini, Sunita Shakya, Jeffrey M. Hausdorff, Rainer von Coelln and Michael P. Cummings

Sensors 2024, 24(24), 8096; https://doi.org/10.3390/s24248096 - 19 Dec 2024

Cited by 1 | Viewed by 947

Abstract

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson’s disease (PD) on motor control, balance, and cognitive function. We assess the test–retest reliability of these tasks in 262 [...] Read more.

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson’s disease (PD) on motor control, balance, and cognitive function. We assess the test–retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials. We show that the diagnostic accuracy for distinguishing PD from controls decreases by a mean of 1.8% between the first and the second trial, suggesting that task repetition may not be necessary for accurate diagnosis. Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), greater variability is seen in subtask duration and sensor-derived measures, reflected in machine learning performance and statistical differences. Our findings also show that this variability differs not only between controls and PD participants but also among groups with varying levels of PD severity, indicating the need to consider population characteristics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks may fail to reveal nuanced variations in movement. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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18 pages, 7180 KiB

Open AccessArticle

A New Sensorized Approach Based on a DeepLabCut Model and IR Thermography for Characterizing the Thermal Profile in Knees During Exercise

by Davide Crisafulli, Marta Spataro, Cristiano De Marchis, Giacomo Risitano and Dario Milone

Sensors 2024, 24(23), 7862; https://doi.org/10.3390/s24237862 - 9 Dec 2024

Cited by 1 | Viewed by 1122

Abstract

The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon [...] Read more.

The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon injuries. This study presents an innovative approach that combines infrared thermography techniques with a Resnet 152 (DeepLabCut based) to detect and monitor temperature variations across specific knee regions during repeated sit-to-stand exercises. Thermal profiles are then analysed in relation to weight distribution data collected using a Wii Balance Board during the exercise. DeepLabCut was used to automate the selection of the region of interest (ROI) for temperature assessments, improving data accuracy compared to traditional time-consuming semi-automatic methods. This integrative approach enables precise and marker-free measurements, offering clinically relevant data that can aid in the diagnosis of knee pathologies, evaluation of the rehabilitation progress, and assessment of treatment effectiveness. The results emphasize the potential of combining thermography with DeepLabCut-driven data analysis to develop accessible, non-invasive tools for joint health monitoring or preventive diagnostics of pathologies. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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12 pages, 1546 KiB

Open AccessArticle

Concurrent Validity and Relative Reliability of the RunScribe™ System for the Assessment of Spatiotemporal Gait Parameters During Walking

by Andrés Ráfales-Perucha, Elisa Bravo-Viñuales, Alejandro Molina-Molina, Antonio Cartón-Llorente, Silvia Cardiel-Sánchez and Luis E. Roche-Seruendo

Sensors 2024, 24(23), 7825; https://doi.org/10.3390/s24237825 - 7 Dec 2024

Viewed by 1047

Abstract

The evaluation of gait biomechanics using portable inertial measurement units (IMUs) offers real-time feedback and has become a crucial tool for detecting gait disorders. However, many of these devices have not yet been fully validated. The aim of this study was to assess [...] Read more.

The evaluation of gait biomechanics using portable inertial measurement units (IMUs) offers real-time feedback and has become a crucial tool for detecting gait disorders. However, many of these devices have not yet been fully validated. The aim of this study was to assess the concurrent validity and relative reliability of the RunScribe™ system for measuring spatiotemporal gait parameters during walking. A total of 460 participants (age: 36 ± 13 years; height: 173 ± 9 cm; body mass: 70 ± 13 kg) were asked to walk on a treadmill at 5 km·h⁻¹. Spatiotemporal parameters of step frequency (SF), step length (SL), step time (ST), contact time (CT), swing time (SwT), stride time (StT), stride length (StL) and normalized stride length (StL%) were measured through RunScribe™ and OptoGait™ systems. Bland–Altman analysis indicated small systematic biases and random errors for all variables. Pearson correlation analysis showed strong correlations (0.70–0.94) between systems. The intraclass correlation coefficient supports these results, except for contact time (ICC = 0.64) and swing time (ICC = 0.34). The paired t-test showed small differences in SL, StL and StL% (≤0.25) and large in CT and SwT (1.2 and 2.2, respectively), with no differences for the rest of the variables. This study confirms the accuracy of the RunScribe™ system for assessing spatiotemporal parameters during walking, potentially reducing the barriers to continuous gait monitoring and early detection of gait issues. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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12 pages, 1915 KiB

Open AccessArticle

Time Efficiency and Ergonomic Assessment of a Robotic Wheelchair Transfer System

by Shantanu A. Satpute, Kaylee J. Uribe, Oluwatofunmi O. Olaore, Minori Iizuka, Ian C. McCumber Gandara, William J. Schoy IV, Rutuja A. Kulkarni, Rosemarie Cooper, Alicia M. Koontz, Owen Flaugh and Rory A. Cooper

Sensors 2024, 24(23), 7558; https://doi.org/10.3390/s24237558 - 27 Nov 2024

Viewed by 1274

Abstract

Background: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers, which often result in back pain and musculoskeletal disorders due to the physical demands of lifting and repositioning. While mechanical floor lifts, the current standard, reduce back strain, they are [...] Read more.

Background: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers, which often result in back pain and musculoskeletal disorders due to the physical demands of lifting and repositioning. While mechanical floor lifts, the current standard, reduce back strain, they are time-consuming and require handling techniques that subject caregivers to prolonged and repeated non-neutral trunk postures, increasing the risk of long-term back injuries. Aims: The aim was to assess the time efficiency and ergonomics of the powered personal transfer system (PPTS), a robotic transfer device designed for bed-to/from-wheelchair transfers. Methods: We evaluated transfers with the PPTS and mechanical lift with eight able-bodied participants who assisted with transfers between a bed and a wheelchair. Inertial measurement units (IMUs) were placed on participants to track their motion and assess trunk joint angles during transfers. Results: The PPTS significantly reduced the transfer time (144.31 s vs. 525.82 s, p < 0.001) and required significantly less range of motion for trunk flexion (p < 0.001), lateral bending (p = 0.008), and axial rotation (p = 0.001), all of which have been associated with back injuries. Additionally, the PPTS significantly reduced the time caregivers spent in non-neutral trunk postures, potentially lowering injury risks. Conclusions: These findings suggest that the PPTS improves transfer efficiency and caregiver safety, offering a promising alternative to the current standard of care for wheelchair-to/from-bed transfers. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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14 pages, 2424 KiB

Open AccessArticle

Biomechanical Analysis of Injury Risk in Two High-Altitude Landing Positions Using Xsens Inertial Units and EMG Sensors

by Xuewu Yao, Haojie Li and Chen Xiu

Sensors 2024, 24(21), 6822; https://doi.org/10.3390/s24216822 - 24 Oct 2024

Cited by 1 | Viewed by 1452

Abstract

High-altitude landing maneuvers can pose a significant injury risk, particularly when performed with different landing techniques. This study aims to compare the biomechanical parameters and injury risks associated with two landing positions—staggered foot landing and simultaneous bilateral landing—using Xsens inertial units and electromyography [...] Read more.

High-altitude landing maneuvers can pose a significant injury risk, particularly when performed with different landing techniques. This study aims to compare the biomechanical parameters and injury risks associated with two landing positions—staggered foot landing and simultaneous bilateral landing—using Xsens inertial units and electromyography (EMG) sensors. A total of 26 university students (13 males, 13 females) participated in this study. Kinematic data were collected using inertial measurement units (IMUs), muscle activity was recorded with EMG, and ground reaction forces were captured using 3D force plates. The data were processed and analyzed using the AnyBody modeling system to simulate joint forces, moments, and muscle activation. This study found that simultaneous bilateral landing exhibited greater hip flexion-extension, knee flexion-extension, and ankle inversion. Vertical joint forces were also significantly higher in the hip, knee, and ankle during simultaneous bilateral landing. Staggered foot landing showed higher muscle forces in the gluteus maximus, iliopsoas, and quadriceps femoris (p < 0.001). The EMG analysis revealed significant differences in the biceps femoris (p = 0.008) and quadriceps femoris (p < 0.001). These findings suggest that simultaneous bilateral landing increases joint load, while staggered foot landing increases muscle activation, which may lead to different injury risks between the two techniques. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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12 pages, 3877 KiB

Open AccessArticle

The Effect of Tai Chi (Bafa Wubu) Training and Artificial Intelligence-Based Movement-Precision Feedback on the Mental and Physical Outcomes of Elderly

by Yuze Zhang, Haojie Li and Rui Huang

Sensors 2024, 24(19), 6485; https://doi.org/10.3390/s24196485 - 9 Oct 2024

Cited by 1 | Viewed by 4020

Abstract

(1) Background: This study aims to compare the effects of AI-based exercise feedback and standard training on the physical and mental health outcomes of older adults participating in a 4-week tai chi training program. (2) Methods: Participants were divided into three groups: an [...] Read more.

(1) Background: This study aims to compare the effects of AI-based exercise feedback and standard training on the physical and mental health outcomes of older adults participating in a 4-week tai chi training program. (2) Methods: Participants were divided into three groups: an AI feedback group received real-time movement accuracy feedback based on AI and inertial measurement units (IMUs), a conventional feedback group received verbal feedback from supervisors, and a control group received no feedback. All groups trained three times per week for 8 weeks. Outcome measures, including movement accuracy, balance, grip strength, quality of life, and depression, were assessed before and after the training period. (3) Results: Compared to pre-training, all three groups showed significant improvements in movement accuracy, grip strength, quality of life, and depression. Only the AI feedback group showed significant improvements in balance. In terms of movement accuracy and balance, the AI feedback group showed significantly greater improvement compared to the conventional feedback group and the control group. (4) Conclusions: Providing real-time AI-based movement feedback during tai chi training offers greater health benefits for older adults compared to standard training without feedback. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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15 pages, 1892 KiB

Open AccessArticle

Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models

by Shahzad Hussain, Hafeez Ur Rehman Siddiqui, Adil Ali Saleem, Muhammad Amjad Raza, Josep Alemany-Iturriaga, Álvaro Velarde-Sotres, Isabel De la Torre Díez and Sandra Dudley

Sensors 2024, 24(19), 6325; https://doi.org/10.3390/s24196325 - 29 Sep 2024

Cited by 1 | Viewed by 2280

Abstract

Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose [...] Read more.

Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system’s robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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16 pages, 8157 KiB

Open AccessArticle

Evaluation and Validation on Sensitivity of Near-Infrared Diffuse Reflectance in Non-Invasive Human Blood Glucose Measurement

by Qing Ge, Tongshuai Han, Rong Liu, Zengfu Zhang, Di Sun, Jin Liu and Kexin Xu

Sensors 2024, 24(18), 5879; https://doi.org/10.3390/s24185879 - 10 Sep 2024

Cited by 2 | Viewed by 2058

Abstract

In non-invasive blood glucose measurement, the sensitivity of glucose-induced optical signals within human tissue is a crucial reference point. This study evaluates the sensitivity of glucose-induced diffuse reflectance in the 1000–1700 nm range. A key factor in understanding this sensitivity is the rate [...] Read more.

In non-invasive blood glucose measurement, the sensitivity of glucose-induced optical signals within human tissue is a crucial reference point. This study evaluates the sensitivity of glucose-induced diffuse reflectance in the 1000–1700 nm range. A key factor in understanding this sensitivity is the rate at which the scattering coefficient changes due to glucose, as it is significantly higher than in non-living media and predominantly influences the diffuse light signal level when blood glucose levels change. The study measured and calculated the changes in the scattering coefficient at 1314 nm, a wavelength chosen for its minimal interference from glucose absorption and other bodily constituents. Based on the Mie scattering theory and the results at 1314 nm, the changes in the scattering coefficient within the 1000–1700 nm range were estimated. Subsequently, the sensitivity of the glucose signal across this range was determined through Monte Carlo (MC) simulations. The findings from 25 human trials indicate that the measured sensitivities at five other typical wavelengths within this band generally align with the sensitivities calculated using the aforementioned method. This research can guide the identification of blood glucose signals and the selection of wavelengths for non-invasive blood glucose measurements. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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13 pages, 1782 KiB

Open AccessArticle

Overnight Sleep Staging Using Chest-Worn Accelerometry

by Fons Schipper, Angela Grassi, Marco Ross, Andreas Cerny, Peter Anderer, Lieke Hermans, Fokke van Meulen, Mickey Leentjens, Emily Schoustra, Pien Bosschieter, Ruud J. G. van Sloun, Sebastiaan Overeem and Pedro Fonseca

Sensors 2024, 24(17), 5717; https://doi.org/10.3390/s24175717 - 2 Sep 2024

Cited by 1 | Viewed by 1645

Abstract

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform “proxy” sleep staging using [...] Read more.

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform “proxy” sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13–83 years, with BMI 18–47 kg/m². We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen’s kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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9 pages, 1281 KiB

Open AccessArticle

Algorithm Validation for Quantifying ActiGraph™ Physical Activity Metrics in Individuals with Chronic Low Back Pain and Healthy Controls

by Jordan F. Hoydick, Marit E. Johnson, Harold A. Cook, Zakiy F. Alfikri, John M. Jakicic, Sara R. Piva, April J. Chambers and Kevin M. Bell

Sensors 2024, 24(16), 5323; https://doi.org/10.3390/s24165323 - 17 Aug 2024

Viewed by 1532

Abstract

Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph™, a widely used physical activity monitor, collects raw acceleration data, and processes these data through proprietary algorithms to produce physical activity measures. The purpose of [...] Read more.

Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph™, a widely used physical activity monitor, collects raw acceleration data, and processes these data through proprietary algorithms to produce physical activity measures. The purpose of this study was to replicate ActiGraph™ algorithms in MATLAB and test the validity of this method with both healthy controls and participants with cLBP. MATLAB code was developed to replicate ActiGraph™’s activity counts and step counts algorithms, to sum the activity counts into counts per minute (CPM), and categorize each minute into activity intensity cut points. A free-living validation was performed where 24 individuals, 12 cLBP and 12 healthy, wore an ActiGraph™ GT9X on their non-dominant hip for up to seven days. The raw acceleration data were processed in both ActiLife™ (v6), ActiGraph™’s data analysis software platform, and through MATLAB (2022a). Percent errors between methods for all 24 participants, as well as separated by cLBP and healthy, were all less than 2%. ActiGraph™ algorithms were replicated and validated for both populations, based on minimal error differences between ActiLife™ and MATLAB, allowing researchers to analyze data from any accelerometer in a manner comparable to ActiLife™. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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14 pages, 1772 KiB

Open AccessArticle

Predicting the Healing of Lower Extremity Fractures Using Wearable Ground Reaction Force Sensors and Machine Learning

by Kylee North, Grange Simpson, Walt Geiger, Amy Cizik, David Rothberg and Robert Hitchcock

Sensors 2024, 24(16), 5321; https://doi.org/10.3390/s24165321 - 17 Aug 2024

Cited by 1 | Viewed by 1499

Abstract

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole [...] Read more.

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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20 pages, 8592 KiB

Open AccessArticle

On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach

by Avirup Roy, Hrishikesh Dutta, Amit Kumar Bhuyan and Subir Biswas

Sensors 2024, 24(14), 4444; https://doi.org/10.3390/s24144444 - 9 Jul 2024

Cited by 1 | Viewed by 1398

Abstract

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses [...] Read more.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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15 pages, 721 KiB

Open AccessArticle

Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior

by Sarah K. Keadle, Skylar Eglowski, Katie Ylarregui, Scott J. Strath, Julian Martinez, Alex Dekhtyar and Vadim Kagan

Sensors 2024, 24(7), 2359; https://doi.org/10.3390/s24072359 - 8 Apr 2024

Cited by 3 | Viewed by 1546

Abstract

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults [...] Read more.

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18–59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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18 pages, 969 KiB

Open AccessEditor’s ChoiceArticle

Wrist-Based Fall Detection: Towards Generalization across Datasets

by Vanilson Fula and Plinio Moreno

Sensors 2024, 24(5), 1679; https://doi.org/10.3390/s24051679 - 5 Mar 2024

Cited by 14 | Viewed by 3622

Abstract

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence [...] Read more.

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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18 pages, 6240 KiB

Open AccessArticle

Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis

by Youngmin Oh

Sensors 2024, 24(5), 1618; https://doi.org/10.3390/s24051618 - 1 Mar 2024

Cited by 3 | Viewed by 1587

Abstract

Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this study, we investigate data augmentation and model training strategies to [...] Read more.

Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this study, we investigate data augmentation and model training strategies to address this problem. Three transformations are tested with varying data volumes to analyze the changes in the classification performance of individual data. Moreover, the impact of transfer learning relative to a pre-trained one-dimensional convolutional neural network (Conv1D) and training with an advanced InceptionTime model are estimated with data augmentation. In Conv1D, the joint training data of non-disabled (ND) participants and double rotationally augmented data of stroke patients is observed to outperform the baseline in terms of F1-score (60.9% vs. 47.3%). Transfer learning pre-trained with ND data exhibits 60.3% accuracy, whereas joint training with InceptionTime exhibits 67.2% accuracy under the same conditions. Our results indicate that rotational augmentation is more effective for individual data with initially lower performance and subset data with smaller numbers of participants than other techniques, suggesting that joint training on rotationally augmented ND and stroke data enhances classification performance, particularly in cases with sparse data and lower initial performance. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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16 pages, 3360 KiB

Open AccessArticle

Assessment of Foot Strike Angle and Forward Propulsion with Wearable Sensors in People with Stroke

by Carmen J. Ensink, Cheriel Hofstad, Theo Theunissen and Noël L. W. Keijsers

Sensors 2024, 24(2), 710; https://doi.org/10.3390/s24020710 - 22 Jan 2024

Cited by 4 | Viewed by 1926

Abstract

Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an [...] Read more.

Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an inertial measurement unit (IMU) to measure the foot strike angle (FSA), and explores eight different kinematic parameters as potential indicators for forward propulsion. Twelve people with stroke performed walking trials while equipped with five IMUs and markers for optical motion analysis (the gold standard). The validity of the IMU-based FSA was assessed via Bland–Altman analysis, ICC, and the repeatability coefficient. Eight different kinematic parameters were compared to the forward propulsion via Pearson correlation. Analyses were performed on a stride-by-stride level and within-subject level. On a stride-by-stride level, the mean difference between the IMU-based FSA and OMCS-based FSA was 1.4 (95% confidence: −3.0; 5.9) degrees, with ICC = 0.97, and a repeatability coefficient of 5.3 degrees. The mean difference for the within-subject analysis was 1.5 (95% confidence: −1.0; 3.9) degrees, with a mean repeatability coefficient of 3.1 (SD: 2.0) degrees. Pearson’s r value for all the studied parameters with forward propulsion were below 0.75 for the within-subject analysis, while on a stride-by-stride level the foot angle upon terminal contact and maximum foot angular velocity could be indicative for the peak forward propulsion. In conclusion, the FSA can accurately be assessed with an IMU on the foot in people with stroke during regular walking. However, no suitable kinematic indicator for forward propulsion was identified based on foot and shank movement that could be used for feedback in people with stroke. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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11 pages, 848 KiB

Open AccessStudy Protocol

Do PROMs or Sensor-Based Monitoring Detect Improvements in Patients’ Knee Function After Total-Knee Arthroplasty?—A Study Protocol for a Prospective Controlled Study

by Lotanna Mba, Robert Prill, Jonathan Lettner, Nikolai Ramadanov, Robert Krause, Jan Reichmann and Roland Becker

Sensors 2025, 25(1), 118; https://doi.org/10.3390/s25010118 - 27 Dec 2024

Viewed by 816

Abstract

Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to [...] Read more.

Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to collect data on performance-based knee function using inertial measurement units (IMUs) worn by participants both preoperatively and postoperatively. PROMs will be completed by the patients before and after surgery to assess their ability to detect the same changes in performance-based knee function measured by the sensors. Additionally, the study will investigate the correlation between the degree of knee alignment correction and postoperative performance-based knee function in participants after TKA. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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