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Biomedical Sensing for Human Motion Monitoring
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Biomedical Sensing for Human Motion Monitoring

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

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 59093

Special Issue Editors

Dr. Jason Friedman

E-Mail Website
Guest Editor
Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
Interests: human motor control; motor learning; rehabilitation
Dr. Sigal Portnoy

E-Mail Website
Guest Editor
Department of Occupational Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
Interests: biomedical engineering; rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in sensor technology mean that wearable sensors are available that can provide information similar to that which once required an expensive lab setup. In addition to reducing costs, taking movement recordings outside of the lab has many other advantages. It is possible to record physiological and biomechanical signals in more realistic situations, record changes in behavior observed throughout the day, better quantify the natural variability in behavior at different time scales and in response to different events, and to monitor behavior to identify special events such as falls in real time. These insights have the potential to improve healthcare outcomes by improving diagnosis, allowing the tracking of progress (e.g., exercise) and rehabilitation, providing large data sets for use in research studies, and providing real-time feedback to improve behavior and for safety purposes. In this Special Issue, we invite papers on topics related to new techniques, analyses, and feedback of recording and monitoring of human movements in different environments using a variety of biomedical sensors. 

Dr. Jason Friedman
Dr. Sigal Portnoy
Guest Editors

Manuscript Submission Information

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Keywords

  • Wearables
  • Human movement
  • Motor control
  • Motor learning
  • Feedback
  • IMU
  • Variability
  • Behavior

Published Papers (20 papers)

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13 pages, 2431 KiB  
Article
Enabling the ActiGraph GT9X Link’s Idle Sleep Mode and Inertial Measurement Unit Settings Directly Impacts Data Acquisition
by Hannah J. Coyle-Asbil, Janik Habegger, Michele Oliver and Lori Ann Vallis
Sensors 2023, 23(12), 5558; https://doi.org/10.3390/s23125558 - 14 Jun 2023
Cited by 1 | Viewed by 980
Abstract
The ActiGraph GT9X has been implemented in clinical trials to track physical activity and sleep. Given recent incidental findings from our laboratory, the overall aim of this study was to notify academic and clinical researchers of the idle sleep mode (ISM) and inertial [...] Read more.
The ActiGraph GT9X has been implemented in clinical trials to track physical activity and sleep. Given recent incidental findings from our laboratory, the overall aim of this study was to notify academic and clinical researchers of the idle sleep mode (ISM) and inertial measurement unit (IMU)’s interaction, as well as their subsequent effect on data acquisition. Investigations were undertaken using a hexapod robot to test the X, Y and Z sensing axes of the accelerometers. Seven GT9X were tested at frequencies ranging from 0.5 to 2 Hz. Testing was performed for three sets of setting parameters: Setting Parameter 1 (ISMONIMUON), Setting Parameter 2 (ISMOFFIMUON), Setting Parameter 3 (ISMONIMUOFF). The minimum, maximum and range of outputs were compared between the settings and frequencies. Findings indicated that Setting Parameters 1 and 2 were not significantly different, but both were significantly different from Setting Parameter 3. Upon inspection, it was discovered that the ISM was only active during Setting Parameter 3 testing, despite it being enabled in Setting Parameter 1. Researchers should be aware of this when conducting future research using the GT9X. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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11 pages, 3598 KiB  
Article
Quantification of Movement Error from Spiral Drawing Test
by Hyunjin Yoon and Minkyu Ahn
Sensors 2023, 23(6), 3043; https://doi.org/10.3390/s23063043 - 11 Mar 2023
Viewed by 1944
Abstract
Parkinson’s disease is a neurodegenerative disease that often comes with symptoms such as muscle stiffness, slowness of movement, and tremors at rest. Since this disease negatively influences the quality of life in patients, an early and accurate diagnosis is important for slowing the [...] Read more.
Parkinson’s disease is a neurodegenerative disease that often comes with symptoms such as muscle stiffness, slowness of movement, and tremors at rest. Since this disease negatively influences the quality of life in patients, an early and accurate diagnosis is important for slowing the progression of the disease and providing effective treatment to patients. One of the quick and easy methods for diagnosing is the spiral drawing test and the differences between the target spiral picture and the drawing by patients can be used as an indicator of movement error. Simply, the average distance between paired samples of the target spiral and the drawing can be easily calculated and used as the level of movement error. However, finding the correct pair of samples between the target spiral and the drawing is relatively difficult, and the accurate algorithm to quantify the movement error has not been thoroughly studied. In this study, we propose algorithms applicable to the spiral drawing test, that ultimately can be used to measure the level of movement error in Parkinson’s disease patients. They are equivalent inter-point distance (ED), shortest distance (SD), varying inter-point distance (VD), and equivalent angle (EA). To evaluate the performance and sensitivity of the methods, we collected data from simulation and experiments with healthy subjects and evaluated the four methods. As a result, in normal (good drawing) and severe symptom (poor drawing) conditions, the calculated errors were 3.67/5.48 from ED, 0.11/1.21 from SD, 0.38/1.46 from VD and 0.01/0.02 from EA, meaning that ED, SD, and VD measure movement error with high noise while EA is sensitive to even small symptom levels. Similarly, in the experiment data, only EA shows the linear increase of error distance to changing symptom levels from 1 to 3. In summary, we found that EA is the most effective algorithm in finding the correct pair of samples between the spiral and the drawing, and consequently yields low errors and high sensitivity to symptom levels. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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13 pages, 4346 KiB  
Article
Validity and Reliability of Inertial Measurement Unit (IMU)-Derived 3D Joint Kinematics in Persons Wearing Transtibial Prosthesis
by Jutima Rattanakoch, Manunchaya Samala, Weerawat Limroongreungrat, Gary Guerra, Kittichai Tharawadeepimuk, Ampika Nanbancha, Wisavaporn Niamsang, Pichitpol Kerdsomnuek and Sarit Suwanmana
Sensors 2023, 23(3), 1738; https://doi.org/10.3390/s23031738 - 3 Feb 2023
Cited by 5 | Viewed by 2606
Abstract
Background: A validity and reliability assessment of inertial measurement unit (IMU)-derived joint angular kinematics during walking is a necessary step for motion analysis in the lower extremity prosthesis user population. This study aimed to assess the accuracy and reliability of an inertial measurement [...] Read more.
Background: A validity and reliability assessment of inertial measurement unit (IMU)-derived joint angular kinematics during walking is a necessary step for motion analysis in the lower extremity prosthesis user population. This study aimed to assess the accuracy and reliability of an inertial measurement unit (IMU) system compared to an optical motion capture (OMC) system in transtibial prosthesis (TTP) users. Methods: Thirty TTP users were recruited and underwent simultaneous motion capture from IMU and OMC systems during walking. Reliability and validity were assessed using intra- and inter-subject variability with standard deviation (S.D.), average S.D., and intraclass correlation coefficient (ICC). Results: The intra-subject S.D. for all rotations of the lower limb joints were less than 1° for both systems. The IMU system had a lower mean S.D. (o), as seen in inter-subject variability. The ICC revealed good to excellent agreement between the two systems for all sagittal kinematic parameters. Conclusion: All joint angular kinematic comparisons supported the IMU system’s results as comparable to OMC. The IMU was capable of precise sagittal plane motion data and demonstrated validity and reliability to OMC. These findings evidence that when compared to OMC, an IMU system may serve well in evaluating the gait of lower limb prosthesis users. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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10 pages, 4618 KiB  
Article
Cushioned Footwear Effect on Pain and Gait Characteristics of Individuals with Knee Osteoarthritis: A Double-Blinded 3 Month Intervention Study
by Isabella Schwartz, Yonah Ofran, Svetlana Bernovsky, Leonid Kandel, Gurion Rivkin, Naama Karniel, Martin Seyres and Sigal Portnoy
Sensors 2023, 23(3), 1375; https://doi.org/10.3390/s23031375 - 26 Jan 2023
Viewed by 1864
Abstract
One of the recommendations for individuals with knee osteoarthritis (OA) is the use of specific footwear, such as sturdy or cushioned shoes. However, the long-term use effects of using cushioned shoes on the pain and spatiotemporal gait parameters in individuals with knee OA [...] Read more.
One of the recommendations for individuals with knee osteoarthritis (OA) is the use of specific footwear, such as sturdy or cushioned shoes. However, the long-term use effects of using cushioned shoes on the pain and spatiotemporal gait parameters in individuals with knee OA are yet to be reported. We therefore aimed to compare the efficacy of cushioned sport footwear versus sham shoes on motor functions, pain and gait characteristics of individuals with knee OA who used the shoes for 3 months. In a double-blinded study, we provided 26 individuals with knee OA with cushioned sport shoes and 12 individuals with knee OA with similar sport shoes without cushioning for 3 months. The gait analysis, the timed up and go (TUG) test and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) were conducted and the pain levels were measured at the baseline, 1 month, and 3 months after the baseline. We found that the cushioned shoes reduce the amount of pain (based on WOMAC) in the affected knee and increase functionality in the research group, but not in the control group. Gait velocity and cadence were increased in both groups. Gait spatiotemporal parameters and their symmetry were unaffected during the intervention. We conclude that the use of cushioned shoes should be recommended to individuals with knee OA for alleviating pain. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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12 pages, 6905 KiB  
Article
Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study
by Sungbae Jo, Sunmi Song, Junesun Kim and Changho Song
Sensors 2022, 22(24), 9819; https://doi.org/10.3390/s22249819 - 14 Dec 2022
Cited by 2 | Viewed by 1953
Abstract
(1) Background: The present study investigated the agreement between the Azure Kinect and marker-based motion analysis during functional movements. (2) Methods: Twelve healthy adults participated in this study and performed a total of six different tasks including front view squat, side view squat, [...] Read more.
(1) Background: The present study investigated the agreement between the Azure Kinect and marker-based motion analysis during functional movements. (2) Methods: Twelve healthy adults participated in this study and performed a total of six different tasks including front view squat, side view squat, forward reach, lateral reach, front view lunge, and side view lunge. Movement data were collected using an Azure Kinect and 12 infrared cameras while the participants performed the movements. The comparability between marker-based motion analysis and Azure Kinect was visualized using Bland–Altman plots and scatter plots. (3) Results: During the front view of squat motions, hip and knee joint angles showed moderate and high level of concurrent validity, respectively. The side view of squat motions showed moderate to good in the visible hip joint angles, whereas hidden hip joint angle showed poor concurrent validity. The knee joint angles showed variation between excellent and moderate concurrent validity depending on the visibility. The forward reach motions showed moderate concurrent validity for both shoulder angles, whereas the lateral reach motions showed excellent concurrent validity. During the front view of lunge motions, both the hip and knee joint angles showed moderate concurrent validity. The side view of lunge motions showed variations in concurrent validity, while the right hip joint angle showed good concurrent validity; the left hip joint showed poor concurrent validity. (4) Conclusions: The overall agreement between the Azure Kinect and marker-based motion analysis system was moderate to good when the body segments were visible to the Azure Kinect, yet the accuracy of tracking hidden body parts is still a concern. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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15 pages, 1165 KiB  
Article
Upper Limb Kinematics of Handwriting among Children with and without Developmental Coordination Disorder
by Amani Abu-Ata, Dido Green, Ran Sopher, Sigal Portnoy and Navah Z. Ratzon
Sensors 2022, 22(23), 9224; https://doi.org/10.3390/s22239224 - 27 Nov 2022
Cited by 1 | Viewed by 1695
Abstract
Background: Children with developmental coordination disorder (DCD) often experience difficulties with handwriting legibility and speed. This study investigates the relationship between handwriting and upper limb kinematics to characterize movement patterns of children with DCD and typically developing (TD) children. Methods: 30 children with [...] Read more.
Background: Children with developmental coordination disorder (DCD) often experience difficulties with handwriting legibility and speed. This study investigates the relationship between handwriting and upper limb kinematics to characterize movement patterns of children with DCD and typically developing (TD) children. Methods: 30 children with and without DCD matched for age, gender, and parent education were compared across handwriting abilities using a standardized handwriting assessment of both copied and dictated tasks (A-A Handwriting). The 3D motion capture system (Qualysis) was used to analyze upper limb kinematics and characterize movement patterns during handwriting and contrasted with written output. Results: Children with DCD wrote fewer legible letters in both copying and dictation. Children with DCD also showed poor automatization of key writing concepts. Atypical wrist postures were associated with reduced legibility for children with DCD (F (1,27) 4.71, p = 0.04, p-η2 = 0.15); whereas for TD children, better legibility was associated with greater variations in movement speed, particularly of the wrist (rho = −0.578, p < 0.05). Conclusion: Results reflect different movement parameters influencing handwriting in children with DCD. An improved understanding of the movement characteristics during handwriting of these children may assist intervention design. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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16 pages, 3241 KiB  
Article
Trunk Posture from Randomly Oriented Accelerometers
by Aidan R. W. Friederich, Musa L. Audu and Ronald J. Triolo
Sensors 2022, 22(19), 7690; https://doi.org/10.3390/s22197690 - 10 Oct 2022
Viewed by 1643
Abstract
Feedback control of functional neuromuscular stimulation has the potential to improve daily function for individuals with spinal cord injuries (SCIs) by enhancing seated stability. Our fully implanted networked neuroprosthesis (NNP) can provide real-time feedback signals for controlling the trunk through accelerometers embedded in [...] Read more.
Feedback control of functional neuromuscular stimulation has the potential to improve daily function for individuals with spinal cord injuries (SCIs) by enhancing seated stability. Our fully implanted networked neuroprosthesis (NNP) can provide real-time feedback signals for controlling the trunk through accelerometers embedded in modules distributed throughout the trunk. Typically, inertial sensors are aligned with the relevant body segment. However, NNP implanted modules are placed according to surgical constraints and their precise locations and orientations are generally unknown. We have developed a method for calibrating multiple randomly oriented accelerometers and fusing their signals into a measure of trunk orientation. Six accelerometers were externally attached in random orientations to the trunks of six individuals with SCI. Calibration with an optical motion capture system resulted in RMSE below 5° and correlation coefficients above 0.97. Calibration with a handheld goniometer resulted in RMSE of 7° and correlation coefficients above 0.93. Our method can obtain trunk orientation from a network of sensors without a priori knowledge of their relationships to the body anatomical axes. The results of this study will be invaluable in the design of feedback control systems for stabilizing the trunk of individuals with SCI in combination with the NNP implanted technology. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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33 pages, 10493 KiB  
Article
Novel Planar Strain Sensor Design for Capturing 3-Dimensional Fingertip Forces from Patients Affected by Hand Paralysis
by Jacob Carducci, Kevin Olds, John W. Krakauer, Jing Xu and Jeremy D. Brown
Sensors 2022, 22(19), 7441; https://doi.org/10.3390/s22197441 - 30 Sep 2022
Cited by 1 | Viewed by 1797
Abstract
Assessment and therapy for individuals who have hand paresis requires force sensing approaches that can measure a wide range of finger forces in multiple dimensions. Here we present a novel strain-gauge force sensor with 3 degrees of freedom (DOF) designed for use in [...] Read more.
Assessment and therapy for individuals who have hand paresis requires force sensing approaches that can measure a wide range of finger forces in multiple dimensions. Here we present a novel strain-gauge force sensor with 3 degrees of freedom (DOF) designed for use in a hand assessment and rehabilitation device. The sensor features a fiberglass printed circuit board substrate to which eight strain gauges are bonded. All circuity for the sensor is routed directly through the board, which is secured to a larger rehabilitative device via an aluminum frame. After design, the sensing package was characterized for weight, capacity, and resolution requirements. Furthermore, a test sensor was calibrated in a three-axis configuration and validated in the larger spherical workspace to understand how accurate and precise the sensor is, while the sensor has slight shortcomings with validation error, it does satisfy the precision, calibration accuracy, and fine sensing requirements in orthogonal loading, and all structural specifications are met. The sensor is therefore a great candidate for sensing technology in rehabilitation devices that assess dexterity in patients with impaired hand function. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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16 pages, 4818 KiB  
Article
Accounting for Viscoelasticity When Interpreting Nano-Composite High-Deflection Strain Gauges
by Spencer A. Baker, McKay D. McFadden, Emma E. Bowden, Anton E. Bowden, Ulrike H. Mitchell and David T. Fullwood
Sensors 2022, 22(14), 5239; https://doi.org/10.3390/s22145239 - 13 Jul 2022
Cited by 1 | Viewed by 1380
Abstract
High-deflection strain gauges show potential as economical and user-friendly sensors for capturing large deformations. The interpretation of these sensors is much more complex than that of conventional strain gauges due to the viscoelastic nature of strain gauges. This research endeavor developed and tested [...] Read more.
High-deflection strain gauges show potential as economical and user-friendly sensors for capturing large deformations. The interpretation of these sensors is much more complex than that of conventional strain gauges due to the viscoelastic nature of strain gauges. This research endeavor developed and tested a model for interpreting sensor outputs that includes the time-dependent nature of strain gauges. A model that captures the effect of quasi-static strains was determined by using a conventional approach of fitting an equation to observed data. The dynamic relationship between the strain and the resistance was incorporated by superimposing dynamic components onto the quasi-static model to account for spikes in resistances that accompany each change in sensor strain and subsequent exponential decays. It was shown that the model can be calibrated for a given sensor by taking two data points at known strains. The resulting sensor-specific model was able to interpret strain-gauge electrical signals during a cyclical load to predict strain with an average mean absolute error (MAE) of 1.4% strain, and to determine the strain rate with an average MAE of 0.036 mm/s. The resulting model and tuning procedure may be used in a wide range of applications, such as biomechanical monitoring and analysis. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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16 pages, 2381 KiB  
Article
Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
by David S. Wood, Kurt Jensen, Allison Crane, Hyunwook Lee, Hayden Dennis, Joshua Gladwell, Anne Shurtz, David T. Fullwood, Matthew K. Seeley, Ulrike H. Mitchell, William F. Christensen and Anton E. Bowden
Sensors 2022, 22(7), 2499; https://doi.org/10.3390/s22072499 - 24 Mar 2022
Cited by 6 | Viewed by 2447
Abstract
In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device [...] Read more.
In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device is presented. Testing was performed on eighteen subjects, and knee angles were predicted using a machine learning regressor. Subject-specific and device-specific models are analyzed and presented. Subject-specific models average root mean square errors of 7.6 and 1.8 degrees for flexion/extension and internal/external rotation, respectively. Device-specific models average root mean square errors of 12.6 and 3.5 degrees for flexion/extension and internal/external rotation, respectively. The device presented in this work proved to be a repeatable, reusable, low-cost device that can adequately model the knee’s flexion/extension and internal/external rotation angles for rehabilitation purposes. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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17 pages, 1386 KiB  
Article
Neurofeedback Therapy for Sensory Over-Responsiveness—A Feasibility Study
by Ruba Hamed, Limor Mizrachi, Yelena Granovsky, Gil Issachar, Shlomit Yuval-Greenberg and Tami Bar-Shalita
Sensors 2022, 22(5), 1845; https://doi.org/10.3390/s22051845 - 25 Feb 2022
Cited by 2 | Viewed by 3370
Abstract
Background: Difficulty in modulating multisensory input, specifically the sensory over-responsive (SOR) type, is linked to pain hypersensitivity and anxiety, impacting daily function and quality of life in children and adults. Reduced cortical activity recorded under resting state has been reported, suggestive of neuromodulation [...] Read more.
Background: Difficulty in modulating multisensory input, specifically the sensory over-responsive (SOR) type, is linked to pain hypersensitivity and anxiety, impacting daily function and quality of life in children and adults. Reduced cortical activity recorded under resting state has been reported, suggestive of neuromodulation as a potential therapeutic modality. This feasibility study aimed to explore neurofeedback intervention in SOR. Methods: Healthy women with SOR (n = 10) underwent an experimental feasibility study comprising four measurement time points (T1—baseline; T2—preintervention; T3—postintervention; T4—follow-up). Outcome measures included resting-state EEG recording, in addition to behavioral assessments of life satisfaction, attaining functional goals, pain sensitivity, and anxiety. Intervention targeted the upregulation of alpha oscillatory power over ten sessions. Results: No changes were detected in all measures between T1 and T2. Exploring the changes in brain activity between T2 and T4 revealed power enhancement in delta, theta, beta, and gamma oscillatory bands, detected in the frontal region (p = 0.03–<0.001; Cohen’s d = 0.637–1.126) but not in alpha oscillations. Furthermore, a large effect was found in enhancing life satisfaction and goal attainment (Cohen’s d = 1.18; 1.04, respectively), and reduced pain sensitivity and anxiety trait (Cohen’s d = 0.70). Conclusion: This is the first study demonstrating the feasibility of neurofeedback intervention in SOR. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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20 pages, 4495 KiB  
Article
Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling
by Ming Jeat Foo, Jen-Shuan Chang and Wei Tech Ang
Sensors 2022, 22(4), 1661; https://doi.org/10.3390/s22041661 - 20 Feb 2022
Cited by 2 | Viewed by 2864
Abstract
Gait evaluation is important in gait rehabilitation and assistance to monitor patient’s balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially [...] Read more.
Gait evaluation is important in gait rehabilitation and assistance to monitor patient’s balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user’s lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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15 pages, 3235 KiB  
Article
A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities
by Bryan Rivera, Consuelo Cano, Israel Luis and Dante A. Elias
Sensors 2022, 22(3), 763; https://doi.org/10.3390/s22030763 - 20 Jan 2022
Cited by 5 | Viewed by 3322
Abstract
Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect [...] Read more.
Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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10 pages, 1303 KiB  
Article
Gait Analysis Using Accelerometry Data from a Single Smartphone: Agreement and Consistency between a Smartphone Application and Gold-Standard Gait Analysis System
by Roy T. Shahar and Maayan Agmon
Sensors 2021, 21(22), 7497; https://doi.org/10.3390/s21227497 - 11 Nov 2021
Cited by 12 | Viewed by 4112
Abstract
Spatio-temporal parameters of human gait, currently measured using different methods, provide valuable information on health. Inertial Measurement Units (IMUs) are one such method of gait analysis, with smartphone IMUs serving as a good substitute for current gold-standard techniques. Here we investigate the concurrent [...] Read more.
Spatio-temporal parameters of human gait, currently measured using different methods, provide valuable information on health. Inertial Measurement Units (IMUs) are one such method of gait analysis, with smartphone IMUs serving as a good substitute for current gold-standard techniques. Here we investigate the concurrent validity of a smartphone placed in a front-facing pocket to perform gait analysis. Sixty community-dwelling healthy adults equipped with a smartphone and an application for gait analysis completed a 2-min walk on a marked path. Concurrent validity was assessed against an APDM mobility lab (APDM Inc.; Portland, OR, USA). Bland–Altman plots and intraclass correlation coefficients (agreement and consistency) for gait speed, cadence, and step length indicate good to excellent agreement (ICC2,1 > 0.8). For right leg stance and swing % of gait cycle and double support % of gait cycle, results were moderate (0.52 < ICC2,1 < 0.62). For left leg stance and swing % of gait cycle left results show poor agreement (ICC2,1 < 0.5). Consistency of results was good to excellent for all tested parameters (ICC3,1 > 0.8). Thus we have a valid and reliable instrument for measuring healthy adults’ spatio-temporal gait parameters in a controlled walking environment. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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14 pages, 3117 KiB  
Article
Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study
by Cecilia Provenzale, Nicola Di Stefano, Alessia Noccaro and Fabrizio Taffoni
Sensors 2021, 21(17), 5817; https://doi.org/10.3390/s21175817 - 29 Aug 2021
Cited by 5 | Viewed by 4316
Abstract
Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. [...] Read more.
Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick–hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=−0.973, 95% CI −0.981,−0.962,  p<0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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15 pages, 3005 KiB  
Article
Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique
by Zachary Choffin, Nathan Jeong, Michael Callihan, Savannah Olmstead, Edward Sazonov, Sarah Thakral, Camilee Getchell and Vito Lombardi
Sensors 2021, 21(11), 3790; https://doi.org/10.3390/s21113790 - 30 May 2021
Cited by 8 | Viewed by 4125
Abstract
Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing [...] Read more.
Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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20 pages, 33162 KiB  
Article
Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles
by Hui Zhou, Dandan Yang, Zhengyi Li, Dao Zhou, Junfeng Gao and Jinan Guan
Sensors 2021, 21(9), 2933; https://doi.org/10.3390/s21092933 - 22 Apr 2021
Cited by 1 | Viewed by 2207
Abstract
Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability [...] Read more.
Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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15 pages, 14418 KiB  
Article
Accelerometry-Enhanced Magnetic Sensor for Intra-Oral Continuous Jaw Motion Tracking
by Mantas Jucevičius, Rimantas Ožiūnas, Mindaugas Mažeika, Vaidotas Marozas and Darius Jegelevičius
Sensors 2021, 21(4), 1409; https://doi.org/10.3390/s21041409 - 18 Feb 2021
Cited by 9 | Viewed by 4443
Abstract
Currently available jaw motion tracking methods require large accessories mounted on a patient and are utilized in controlled environments, for short-time examinations only. In some cases, especially in the evaluation of bruxism, a non-restrictive, 24-h jaw tracking method is needed. Bruxism oriented, electromyography [...] Read more.
Currently available jaw motion tracking methods require large accessories mounted on a patient and are utilized in controlled environments, for short-time examinations only. In some cases, especially in the evaluation of bruxism, a non-restrictive, 24-h jaw tracking method is needed. Bruxism oriented, electromyography (EMG)-based devices and sensor-enhanced occlusal splints are able to continuously detect masticatory activity but are uninformative in regards to movement trajectories and kinematics. This study explores a possibility to use a permanent magnet and a 3-axial magnetometer to track the mandible’s spatial position in relation to the maxilla. An algorithm for determining the sensor’s coordinates from magnetic field values was developed, and it was verified via analytical and finite element modeling and by using a 3D positioning system. Coordinates of the cubic test trajectory (a = 10 mm) were determined with root-mean-square error (RMSE) of 0.328±0.005 mm. Possibility for teeth impact detection by accelerometry was verified. Test on a 6 degrees-of-freedom (DOF), hexapod-based jaw motion simulator moving at natural speed confirmed the system’s ability to simultaneously detect jaw position and the impacts of teeth. Small size of MEMS sensors is suitable for a wearable intra-oral system that could allow visualization of continuous jaw movement in 3D models and could enable new research on parafunctional jaw activities. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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26 pages, 7511 KiB  
Article
Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches
by Chaiyawan Auepanwiriyakul, Sigourney Waibel, Joanna Songa, Paul Bentley and A. Aldo Faisal
Sensors 2020, 20(24), 7313; https://doi.org/10.3390/s20247313 - 19 Dec 2020
Cited by 21 | Viewed by 6235
Abstract
Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a convenient and low-cost method to monitor the natural behaviour of hospital patients. However, their accuracy at quantifying limb motion, and clinical acceptability, have not yet been demonstrated. To this end we conducted [...] Read more.
Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a convenient and low-cost method to monitor the natural behaviour of hospital patients. However, their accuracy at quantifying limb motion, and clinical acceptability, have not yet been demonstrated. To this end we conducted a two-stage study: First, we compared the inertial accuracy of wrist-worn IMUs, both research-grade (Xsens MTw Awinda, and Axivity AX3) and consumer-grade (Apple Watch Series 3 and 5), and optical motion tracking (OptiTrack). Given the moderate to strong performance of the consumer-grade sensors, we then evaluated this sensor and surveyed the experiences and attitudes of hospital patients (N = 44) and staff (N = 15) following a clinical test in which patients wore smartwatches for 1.5–24 h in the second study. Results indicate that for acceleration, Xsens is more accurate than the Apple Series 5 and 3 smartwatches and Axivity AX3 (RMSE 1.66 ± 0.12 m·s−2; R2 0.78 ± 0.02; RMSE 2.29 ± 0.09 m·s−2; R2 0.56 ± 0.01; RMSE 2.14 ± 0.09 m·s−2; R2 0.49 ± 0.02; RMSE 4.12 ± 0.18 m·s−2; R2 0.34 ± 0.01 respectively). For angular velocity, Series 5 and 3 smartwatches achieved similar performances against Xsens with RMSE 0.22 ± 0.02 rad·s−1; R2 0.99 ± 0.00; and RMSE 0.18 ± 0.01 rad·s−1; R2 1.00± SE 0.00, respectively. Surveys indicated that in-patients and healthcare professionals strongly agreed that wearable motion sensors are easy to use, comfortable, unobtrusive, suitable for long-term use, and do not cause anxiety or limit daily activities. Our results suggest that consumer smartwatches achieved moderate to strong levels of accuracy compared to laboratory gold-standard and are acceptable for pervasive monitoring of motion/behaviour within hospital settings. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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Review

Jump to: Research

18 pages, 889 KiB  
Review
Human Movement Quality Assessment Using Sensor Technologies in Recreational and Professional Sports: A Scoping Review
by Verena Venek, Stefan Kranzinger, Hermann Schwameder and Thomas Stöggl
Sensors 2022, 22(13), 4786; https://doi.org/10.3390/s22134786 - 24 Jun 2022
Cited by 6 | Viewed by 2326
Abstract
The use of sensor technology in sports facilitates the data-driven evaluation of human movement not only in terms of quantity but also in terms of quality. This scoping review presents an overview of sensor technologies and human movement quality assessments in ecologically-similar environments. [...] Read more.
The use of sensor technology in sports facilitates the data-driven evaluation of human movement not only in terms of quantity but also in terms of quality. This scoping review presents an overview of sensor technologies and human movement quality assessments in ecologically-similar environments. We searched four online databases to identify 16 eligible articles with either recreational and/or professional athletes. A total of 50% of the studies used inertial sensor technology, 31% vision-based sensor technology. Most of the studies (69%) assessed human movement quality using either the comparison to an expert’s performance, to an exercise definition or to the athletes’ individual baseline performance. A total of 31% of the studies used expert-based labeling of the movements to label data. None of the included studies used a control group-based study design to investigate impact on training progress, injury prevention or behavior change. Although studies have used sensor technology for movement quality assessment, the transfer from the lab to the field in recreational and professional sports is still emerging. Hence, research would benefit from impact studies of technology-assisted training interventions including control groups as well as investigating features of human movement quality in addition to kinematic parameters. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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