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Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 13633

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Dr. Caroline Sunderland

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Sport Performance Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, Department of Sport Science, Nottingham Trent University, Nottingham, UK
Interests: team sport performance; GPS for activity profile monitoring
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Special Issue Information

Dear Colleagues,

In recent years, sensors have been used for the objective measurement of movement, human physiology, and physical activity in a wide variety of settings. These have included sports performance, physical activity, and sedentary behavior research. Participants have ranged from elite athletes to clinical populations, and from young children to the elderly. This Special Issue aims to amalgamate research which uses sensors to monitor human activity, behavior, and physiology in order to demonstrate the variety and impact of sensor research.

We welcome research that employs either wearable or environmental sensors from a range of disciplines. Examples include physiology; sport performance; physical activity; movement behavior; sedentary behavior; and physical behavior. We encourage original articles, reviews, perspectives, and letters.

Dr. Caroline Sunderland
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (11 papers)

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Research

11 pages, 2054 KiB

Open AccessArticle

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach

by Liam David Hughes, Martin Bencsik, Maria Bisele and Cleveland Thomas Barnett

Sensors 2023, 23(22), 9241; https://doi.org/10.3390/s23229241 - 17 Nov 2023

Viewed by 873

Abstract

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category’s cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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23 pages, 11711 KiB

Open AccessArticle

UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises

by Rafael Aguilar-Ortega, Rafael Berral-Soler, Isabel Jiménez-Velasco, Francisco J. Romero-Ramírez, Manuel García-Marín, Jorge Zafra-Palma, Rafael Muñoz-Salinas, Rafael Medina-Carnicer and Manuel J. Marín-Jiménez

Sensors 2023, 23(21), 8862; https://doi.org/10.3390/s23218862 - 31 Oct 2023

Viewed by 1243

Abstract

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors’ ability to monitor patients’ recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient’s mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject’s position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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18 pages, 1247 KiB

Open AccessArticle

The Effect of a Smartphone App with an Accelerometer on the Physical Activity Behavior of Hospitalized Patients: A Randomized Controlled Trial

by Hanneke C. van Dijk-Huisman, Rachel Senden, Maud H. H. Smeets, Rik G. J. Marcellis, Fabienne J. H. Magdelijns and Antoine F. Lenssen

Sensors 2023, 23(21), 8704; https://doi.org/10.3390/s23218704 - 25 Oct 2023

Viewed by 842

Abstract

Inactive behavior is common in hospitalized patients. This study investigated the effectiveness of using a smartphone app with an accelerometer (Hospital Fit) in addition to usual care physiotherapy on increasing patients’ physical activity (PA) behavior. A randomized controlled trial was performed at Maastricht University Medical Centre. Patients receiving physiotherapy while hospitalized at the department of Pulmonology or Internal Medicine were randomized to usual care physiotherapy or using Hospital Fit additionally. Daily time spent walking, standing, and upright (standing/walking) (min) and daily number of postural transitions were measured with an accelerometer between the first and last treatment. Multiple linear regression analysis was performed to determine the association between PA behavior and Hospital Fit use, corrected for functional independence (mILAS). Seventy-eight patients were included with a median (IQR) age of 63 (56–68) years. Although no significant effects were found, a trend was seen in favor of Hospital Fit. Effects increased with length of use. Corrected for functional independence, Hospital Fit use resulted in an average increase of 27.4 min (95% CI: −2.4–57.3) standing/walking on day five and 29.2 min (95% CI: −6.4–64.7) on day six compared to usual care. Hospital Fit appears valuable in increasing PA in functionally independent patients. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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12 pages, 1296 KiB

Open AccessArticle

Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health

by Benjamin D. Maylor, Charlotte L. Edwardson, Alexandra M. Clarke-Cornwell, Melanie J. Davies, Nathan P. Dawkins, David W. Dunstan, Kamlesh Khunti, Tom Yates and Alex V. Rowlands

Sensors 2023, 23(17), 7353; https://doi.org/10.3390/s23177353 - 23 Aug 2023

Viewed by 1092

Abstract

Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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15 pages, 1135 KiB

Open AccessArticle

Variations in Concurrent Validity of Two Independent Inertial Measurement Units Compared to Gold Standard for Upper Body Posture during Computerised Device Use

by Roger Lee, Riad Akhundov, Carole James, Suzi Edwards and Suzanne J. Snodgrass

Sensors 2023, 23(15), 6761; https://doi.org/10.3390/s23156761 - 28 Jul 2023

Cited by 1 | Viewed by 1023

Abstract

Inertial measurement units (IMUs) may provide an objective method for measuring posture during computer use, but research is needed to validate IMUs’ accuracy. We examine the concurrent validity of two different IMU systems in measuring three-dimensional (3D) upper body posture relative to a motion capture system (Mocap) as a potential device to assess postures outside a laboratory environment. We used 3D Mocap and two IMU systems (Wi-Fi and Bluetooth) to capture the upper body posture of twenty-six individuals during three physical computer working conditions (monitor correct, monitor raised, and laptop). Coefficient of determination (R²) and root-mean-square error (RMSE) compared IMUs to Mocap. Head/neck segment [HN], upper trunk segment [UTS], and joint angle [HN-UTS] were the primary variables. Wi-Fi IMUs demonstrated high validity for HN and UTS (sagittal plane) and HN-UTS (frontal plane) for all conditions, and for HN rotation movements (both for the monitor correct and monitor raised conditions), others moderate to poor. Bluetooth IMUs for HN, and UTS (sagittal plane) for the monitor correct, laptop, and monitor raised conditions were moderate. Frontal plane movements except UTS (monitor correct and laptop) and all rotation had poor validity. Both IMU systems were affected by gyroscopic drift with sporadic data loss in Bluetooth IMUs. Wi-Fi IMUs had more acceptable accuracy when measuring upper body posture during computer use compared to Mocap, except for trunk rotations. Variation in IMU systems’ performance suggests validation in the task-specific movement(s) is essential. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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9 pages, 2374 KiB

Open AccessArticle

Beyond the Clinic: Maximum Free-Living Stepping as a Potential Measure of Physical Performance

by Craig Speirs, Mark D. Dunlop, Marc Roper and Malcolm Granat

Sensors 2023, 23(14), 6555; https://doi.org/10.3390/s23146555 - 20 Jul 2023

Viewed by 708

Abstract

Measures of physical performance captured within a clinical setting are commonly used as a surrogate for underlying health or disease risk within an individual. By measuring physical behaviour within a free-living setting, we may be able to better quantify physical performance. In our study, we outline an approach to measure maximum free-living step count using a body-worn sensor as an indicator of physical performance. We then use this approach to characterise the maximum step count over a range of window durations within a population of older adults to identify a preferred duration over which to measure the maximum step count. We found that while almost all individuals (97%) undertook at least one instance of continuous stepping longer than two minutes, a sizeable minority of individuals (31%) had no periods of continuous stepping longer than six minutes. We suggest that the maximum step count measured over a six-minute period may be too sensitive to the adults’ lack of opportunity to undertake prolonged periods of stepping, and a two-minute window could provide a more representative measure of physical performance. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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13 pages, 1098 KiB

Open AccessCommunication

Stepping Beyond Counts in Recovery of Total Hip Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

by Camdon Fary, Jason Cholewa, Scott Abshagen, Dave Van Andel, Anna Ren, Mike B. Anderson and Krishna Tripuraneni

Sensors 2023, 23(14), 6538; https://doi.org/10.3390/s23146538 - 20 Jul 2023

Viewed by 958

Abstract

Gait quality parameters have been used to measure recovery from total hip arthroplasty (THA) but are time-intensive and previously could only be performed in a lab. Smartphone sensor data and algorithmic advances presently allow for the passive collection of qualitative gait metrics. The purpose of this prospective study was to observe the recovery of physical function following THA by assessing passively collected pre- and post-operative gait quality metrics. This was a multicenter, prospective cohort study. From six weeks pre-operative through to a minimum 24 weeks post-operative, 612 patients used a digital care management application that collected gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-sample t-test. Recovery was defined as the post-operative week when the respective gait metric was no longer statistically inferior to the pre-operative value. To control for multiple comparison error, significance was set at p < 0.002. Walking speeds and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-post-operative (p < 0.001). Walking speed (1.00 ± 0.14 m/s, p = 0.04), step length (0.58 ± 0.06 m/s, p = 0.02), asymmetry (14.5 ± 19.4%, p = 0.046), and double support percentage (31.6 ± 1.5%, p = 0.0089) recovered at 9, 8, 7, and 10 weeks post-operative, respectively. Walking speed, step length, asymmetry, and double support all recovered beyond pre-operative values at 13, 17, 10, and 18 weeks, respectively (p < 0.002). Functional recovery following THA can be measured via passively collected gait quality metrics using a digital care management platform. The data suggest that metrics of gait quality are most negatively affected two weeks post-operative; recovery to pre-operative levels occurs at approximately 10 weeks following primary THA, and follows a slower trajectory compared to previously reported step count recovery trajectories. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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12 pages, 5696 KiB

Open AccessArticle

Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment

by Nieke Vets, An De Groef, Kaat Verbeelen, Nele Devoogdt, Ann Smeets, Dieter Van Assche, Liesbet De Baets and Jill Emmerzaal

Sensors 2023, 23(13), 6100; https://doi.org/10.3390/s23136100 - 02 Jul 2023

Viewed by 1411

Abstract

(1) Background: Being able to objectively assess upper limb (UL) dysfunction in breast cancer survivors (BCS) is an emerging issue. This study aims to determine the accuracy of a pre-trained lab-based machine learning model (MLM) to distinguish functional from non-functional arm movements in a home situation in BCS. (2) Methods: Participants performed four daily life activities while wearing two wrist accelerometers and being video recorded. To define UL functioning, video data were annotated and accelerometer data were analyzed using a counts threshold method and an MLM. Prediction accuracy, recall, sensitivity, f1-score, ‘total minutes functional activity’ and ‘percentage functionally active’ were considered. (3) Results: Despite a good MLM accuracy (0.77–0.90), recall, and specificity, the f1-score was poor. An overestimation of the ‘total minutes functional activity’ and ‘percentage functionally active’ was found by the MLM. Between the video-annotated data and the functional activity determined by the MLM, the mean differences were 0.14% and 0.10% for the left and right side, respectively. For the video-annotated data versus the counts threshold method, the mean differences were 0.27% and 0.24%, respectively. (4) Conclusions: An MLM is a better alternative than the counts threshold method for distinguishing functional from non-functional arm movements. However, the abovementioned wrist accelerometer-based assessment methods overestimate UL functional activity. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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19 pages, 5212 KiB

Open AccessArticle

Quantifying Hand Strength and Isometric Pinch Individuation Using a Flexible Pressure Sensor Grid

by Brian J. Conway, Léon Taquet, Timothy F. Boerger, Sarah C. Young, Kate B. Krucoff, Brian D. Schmit and Max O. Krucoff

Sensors 2023, 23(13), 5924; https://doi.org/10.3390/s23135924 - 26 Jun 2023

Viewed by 1178

Abstract

Modulating force between the thumb and another digit, or isometric pinch individuation, is critical for daily tasks and can be impaired due to central or peripheral nervous system injury. Because surgical and rehabilitative efforts often focus on regaining this dexterous ability, we need to be able to consistently quantify pinch individuation across time and facilities. Currently, a standardized metric for such an assessment does not exist. Therefore, we tested whether we could use a commercially available flexible pressure sensor grid (Tekscan F-Socket [Tekscan Inc., Norwood, MA, USA]) to repeatedly measure isometric pinch individuation and maximum voluntary contraction (MVC) in twenty right-handed healthy volunteers at two visits. We developed a novel equation informed by the prior literature to calculate isometric individuation scores that quantified percentage of force on the grid generated by the indicated digit. MVC intra-class correlation coefficients (ICCs) for the left and right hands were 0.86 (p < 0.0001) and 0.88 (p < 0.0001), respectively, suggesting MVC measurements were consistent over time. However, individuation score ICCs, were poorer (left index ICC 0.41, p = 0.28; right index ICC −0.02, p = 0.51), indicating that this protocol did not provide a sufficiently repeatable individuation assessment. These data support the need to develop novel platforms specifically for repeatable and objective isometric hand dexterity assessments. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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13 pages, 2723 KiB

Open AccessArticle

Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range

by Henri Vähä-Ypyä, Jakob Bretterhofer, Pauliina Husu, Jana Windhaber, Tommi Vasankari, Sylvia Titze and Harri Sievänen

Sensors 2023, 23(11), 5073; https://doi.org/10.3390/s23115073 - 25 May 2023

Cited by 2 | Viewed by 1071

Abstract

Accelerometer data can be used to estimate incident oxygen consumption (VO₂) during physical activity. Relationships between the accelerometer metrics and VO₂ are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO₂ less than 25 mL/kg/min, accelerometer metrics accounted for 71–86% of the variation in VO₂. For typical running speeds starting from VO₂ of 25 mL/kg/min up to over 60 mL/kg/min, 32–69% of the variation in VO₂ could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO₂ during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO₂. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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13 pages, 1789 KiB

Open AccessArticle

Immersive Virtual Reality Reaction Time Test and Relationship with the Risk of Falling in Parkinson’s Disease

by Pablo Campo-Prieto, José Mª Cancela-Carral and Gustavo Rodríguez-Fuentes

Sensors 2023, 23(9), 4529; https://doi.org/10.3390/s23094529 - 06 May 2023

Cited by 1 | Viewed by 2568

Abstract

Immersive virtual reality (IVR) uses customized and advanced software and hardware to create a digital 3D reality in which all of the user’s senses are stimulated with computer-generated sensations and feedback. This technology is a promising tool that has already proven useful in Parkinson’s disease (PD). The risk of falls is very high in people with PD, and reaction times and processing speed may be markers of postural instability and functionality, cognitive impairment and disease progression. An exploratory study was conducted to explore the feasibility of reaction time tests performed in IVR as predictors of falls. A total of 26 volunteers (79.2% male; 69.73 ± 6.32 years) diagnosed with PD (1.54 ± 0.90 H&Y stage; 26.92 ± 2.64 MMSE) took part in the study. IVR intervention was feasible, with no adverse effects (no Simulator Sickness Questionnaire symptoms). IVR reaction times were related (Spearman’s rho) to functionality (timed up and go test (TUG) (rho = 0.537, p = 0.005); TUG-Cognitive (rho = 0.576, p = 0.020); cognitive impairment mini mental state exam (MMSE) (rho = −0.576, p = 0.002)) and the years of the patients (rho = 0.399, p = 0.043) but not with the first PD symptom or disease stage. IVR test is a complementary assessment tool that may contribute to preventing falls in the proposed sample. Additionally, based on the relationship between TUG and reaction times, a cut-off time is suggested that would be effective at predicting the risk of suffering a fall in PD patients using a simple and quick IVR test. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)

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