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Biosensors for Gait Measurements and Patient Rehabilitation

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 22709

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Prof. Dr. Lennart Scheys

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Guest Editor

Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
Interests: gait; orthopedic surgery; knee; knee joint; kinematics; biomedical imaging; 3D motion analysis; biomechanics

Prof. Dr. Ilse Jonkers

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Guest Editor

Human Movement Biomechanics Research Group, Department of Biomedical Kinesiology, KU Leuven, Leuven, Belgium
Interests: biomechanics; mechanobiology; cartilage; multi-scale modeling; bioreactor; motion capture; osteoarthritis; physics-based modeling; musculoskeletal loading

Special Issue Information

Dear Colleagues,

Musculoskeletal care is gradually but steadily evolving from a primary focus on structural aspects of the musculoskeletal system towards a more comprehensive approach that integrates and targets optimal function of the musculoskeletal system in every step of the care pathway. In other words, we are moving away from “targeting the best possible X-ray” and towards “targeting the best possible functional outcome”. Nevertheless, this evolution is inherently data-hungry, and its success relies on reliable, patient-friendly technology that allows accurately capturing the key biomechanical drivers for, and biomarkers of, an optimally functioning musculoskeletal system during daily-life activities, including gait as an example. Furthermore, the associated data are preferentially continuously captured in a patient’s natural setting, as recent findings suggest that collecting such data at discrete moments in a hospital or laboratory setting may not accurately reflect the patient’s true functional status.

Therefore, this Special Issue welcomes original research articles, experimental studies, and systematic reviews covering all types of biosensors that contribute to the above evolution and target capturing biomechanical data of the musculoskeletal system during gait and/or other daily-life motor tasks with the goal of better informing musculoskeletal care, from diagnosis, through conservative or surgical treatment and the associated rehabilitation, up to post-treatment follow-up.

Prof. Dr. Lennart Scheys
Prof. Dr. Ilse Jonkers
Guest Editors

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Published Papers (7 papers)

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Research

15 pages, 3498 KiB

Open AccessArticle

Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation

by Svenja Sagasser, Adrian Sauer, Christoph Thorwächter, Jana G. Weber, Allan Maas, Matthias Woiczinski, Thomas M. Grupp and Ariana Ortigas-Vásquez

Sensors 2024, 24(11), 3324; https://doi.org/10.3390/s24113324 - 23 May 2024

Cited by 1 | Viewed by 925

Abstract

Recently, inertial measurement units have been gaining popularity as a potential alternative to optical motion capture systems in the analysis of joint kinematics. In a previous study, the accuracy of knee joint angles calculated from inertial data and an extended Kalman filter and smoother algorithm was tested using ground truth data originating from a joint simulator guided by fluoroscopy-based signals. Although high levels of accuracy were achieved, the experimental setup leveraged multiple iterations of the same movement pattern and an absence of soft tissue artefacts. Here, the algorithm is tested against an optical marker-based system in a more challenging setting, with single iterations of a loaded squat cycle simulated on seven cadaveric specimens on a force-controlled knee rig. Prior to the optimisation of local coordinate systems using the REference FRame Alignment MEthod (REFRAME) to account for the effect of differences in local reference frame orientation, root-mean-square errors between the kinematic signals of the inertial and optical systems were as high as 3.8° ± 3.5° for flexion/extension, 20.4° ± 10.0° for abduction/adduction and 8.6° ± 5.7° for external/internal rotation. After REFRAME implementation, however, average root-mean-square errors decreased to 0.9° ± 0.4° and to 1.5° ± 0.7° for abduction/adduction and for external/internal rotation, respectively, with a slight increase to 4.2° ± 3.6° for flexion/extension. While these results demonstrate promising potential in the approach’s ability to estimate knee joint angles during a single loaded squat cycle, they highlight the limiting effects that a reduced number of iterations and the lack of a reliable consistent reference pose inflicts on the sensor fusion algorithm’s performance. They similarly stress the importance of adapting underlying assumptions and correctly tuning filter parameters to ensure satisfactory performance. More importantly, our findings emphasise the notable impact that properly aligning reference-frame orientations before comparing joint kinematics can have on results and the conclusions derived from them. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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Figure 1

Figure 1
Cadaveric knee on the knee rig, side-view with optical markers (left) and from behind with IMU sensors (right). Full article ">Figure 2
Raw knee joint angles, plotted over the entire squat movement, expressed as a percentage. The solid purple lines represent the angles estimated using inertial data. The dashed green lines represent the angles measured by the optical marker-based system. Each row represents one subject, while the columns represent flexion/extension, ab/adduction and ext/internal rotation (from left to right). Full article ">Figure 3
REFRAMEIMU→GOM knee joint angles, plotted over the entire squat movement, expressed as a percentage. The solid purple lines represent the angles estimated using inertial data. The dashed green lines represent the angles measured by the optical marker-based system. Each row represents one subject, while the columns represent flexion/extension, ab/adduction and ext/internal rotation (from left to right). Full article ">Figure 4
REFRAMERMS knee joint angles, plotted over the entire squat movement, expressed as a percentage. The solid purple lines represent the angles estimated using inertial data. The dashed green lines represent the angles measured by the optical marker-based system. Each row represents one subject, while the columns represent flexion/extension, ab/adduction and ext/internal rotation (from left to right). Full article ">Figure 5
Root-mean-square error (RMSE) comparison: average root-mean-square errors ± standard deviation, before optimisation (raw), after REFRAMEIMU→GOM and REFRAMERMS: (a) flexion/extension, (b) abduction/adduction and (c) external/internal rotation. Statistical significance of differences (p-values) based on a paired t-test between raw and REFRAMEIMU→GOM and raw and REFRAMERMS. Full article ">

12 pages, 1589 KiB

Open AccessArticle

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson’s Disease through a Commercial Smartwatch

by Edoardo Bianchini, Silvia Galli, Marika Alborghetti, Lanfranco De Carolis, Alessandro Zampogna, Clint Hansen, Nicolas Vuillerme, Antonio Suppa and Francesco E. Pontieri

Sensors 2023, 23(21), 8971; https://doi.org/10.3390/s23218971 - 4 Nov 2023

Cited by 4 | Viewed by 1663

Abstract

Daily steps could be a valuable indicator of real-world ambulation in Parkinson’s disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland–Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2–5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann–Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84–0.90; SAM range: 7.8–9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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14 pages, 1713 KiB

Open AccessArticle

Sensitivity of Model-Based Predictions of Post-TKA Kinematic Behavior to Residual Errors in Ultrasound-Based Knee Collateral Ligament Strain Assessment

by Félix Dandois, Orçun Taylan, Jacobus H. Müller and Lennart Scheys

Sensors 2023, 23(19), 8268; https://doi.org/10.3390/s23198268 - 6 Oct 2023

Viewed by 1249

Abstract

Ultrasound-based ligament strain estimation shows promise in non-invasively assessing knee joint collateral ligament behavior and improving ligament balancing procedures. However, the impact of ultrasound-based strain estimation residual errors on in-silico arthroplasty predictions remains unexplored. We investigated the sensitivity of post-arthroplasty kinematic predictions to ultrasound-based strain estimation errors compared to clinical inaccuracies in implant positioning.Two cadaveric legs were submitted to active squatting, and specimen-specific rigid computer models were formulated. Mechanical properties of the ligament model were optimized to reproduce experimentally obtained tibiofemoral kinematics and loads with minimal error. Resulting remaining errors were comparable to the current state-of-the-art. Ultrasound-derived strain residual errors were then introduced by perturbing lateral collateral ligament (LCL) and medial collateral ligament (MCL) stiffness. Afterwards, the implant position was perturbed to match with the current clinical inaccuracies reported in the literature. Finally, the impact on simulated post-arthroplasty tibiofemoral kinematics was compared for both perturbation scenarios. Ultrasound-based errors minimally affected kinematic outcomes (mean differences < 0.73° in rotations, 0.1 mm in translations). Greatest differences occurred in external tibial rotations (−0.61° to 0.73° for MCL, −0.28° to 0.27° for LCL). Comparatively, changes in implant position had larger effects, with mean differences up to 1.95° in external tibial rotation and 0.7 mm in mediolateral translation. In conclusion, our study demonstrated that the ultrasound-based assessment of collateral ligament strains has the potential to enhance current computer-based pre-operative knee arthroplasty planning. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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Figure 1

Figure 1
Virtual knee-joint simulator. The femur is fixed to the hip assembly free to rotate in the sagittal plane and to translate along the coronal axis). The tibia is linked to the ankle assembly with a cylindric joint (allowing rotation in the coronal plane). The ankle assembly is free to rotate in all planes and translate along the sagittal axis. Tibiofemoral and patellofemoral joints are created using a contact function constraining the joints based on the bone and implant geometries. Additionally, the model is also constrained by active loading, i.e., quadriceps load between patella and hip assembly (3 bundles) and hamstrings load between tibia and hip assembly (1 bundle medial and 1 bundle lateral) as well as passive forces from soft tissues, i.e., patellar ligament between the tibia and the patella (3 bundles) and both collateral ligaments: MCL (2 bundles) and LCL (1 bundle). Full article ">Figure 2
Tibiofemoral kinematics of both specimens (Specimen 1 on top and Specimen 2 on bottom) experimentally obtained (blue) and simulated with the developed model (red). The displayed kinematics are, from left to right, adduction, external tibial rotation, anterior–posterior (AP) translation, medio-lateral (ML) translation and inferior–superior (IS) translation. Full article ">Figure 3
Comparisons of simulated adduction and external tibial rotation obtained after modification of ligaments stiffness (magenta and green) and results obtained after modification of implant internal rotation (blue and red) across the flexion range (dashed orange) with optimal kinematics (black). Parts (A,B) display comparisons of Specimen 1 for MCL and LCL, respectively. Part (C) displays comparisons of Specimen 2. Full article ">Figure 3 Cont.
Comparisons of simulated adduction and external tibial rotation obtained after modification of ligaments stiffness (magenta and green) and results obtained after modification of implant internal rotation (blue and red) across the flexion range (dashed orange) with optimal kinematics (black). Parts (A,B) display comparisons of Specimen 1 for MCL and LCL, respectively. Part (C) displays comparisons of Specimen 2. Full article ">

15 pages, 1712 KiB

Open AccessArticle

The Validity of Hawkin Dynamics Wireless Dual Force Plates for Measuring Countermovement Jump and Drop Jump Variables

by Andrew J. Badby, Peter D. Mundy, Paul Comfort, Jason P. Lake and John J. McMahon

Sensors 2023, 23(10), 4820; https://doi.org/10.3390/s23104820 - 17 May 2023

Cited by 21 | Viewed by 7621

Abstract

Force plate testing is becoming more commonplace in sport due to the advent of commercially available, portable, and affordable force plate systems (i.e., hardware and software). Following the validation of the Hawkin Dynamics Inc. (HD) proprietary software in recent literature, the aim of this study was to determine the concurrent validity of the HD wireless dual force plate hardware for assessing vertical jumps. During a single testing session, the HD force plates were placed directly atop two adjacent Advanced Mechanical Technology Inc. in-ground force plates (the “gold standard”) to simultaneously collect vertical ground reaction forces produced by 20 participants (27 ± 6 years, 85 ± 14 kg, 176.5 ± 9.23 cm) during the countermovement jump (CMJ) and drop jump (DJ) tests (1000 Hz). Agreement between force plate systems was determined via ordinary least products regression using bootstrapped 95% confidence intervals. No bias was present between the two force plate systems for any of the CMJ and DJ variables, except DJ peak braking force (proportional bias) and DJ peak braking power (fixed and proportional bias). The HD system may be considered a valid alternative to the industry gold standard for assessing vertical jumps because fixed or proportional bias was identified for none of the CMJ variables (n = 17) and only 2 out of 18 DJ variables. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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12 pages, 1900 KiB

Open AccessArticle

Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis

by Giacomo Di Raimondo, Miel Willems, Bryce Adrian Killen, Sara Havashinezhadian, Katia Turcot, Benedicte Vanwanseele and Ilse Jonkers

Sensors 2023, 23(9), 4484; https://doi.org/10.3390/s23094484 - 4 May 2023

Cited by 7 | Viewed by 3619

Abstract

Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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16 pages, 1606 KiB

Open AccessArticle

Video-based Goniometer Applications for Measuring Knee Joint Angles during Walking in Neurological Patients: A Validity, Reliability and Usability Study

by Monica Parati, Matteo Gallotta, Beatrice De Maria, Annalisa Pirola, Matteo Morini, Luca Longoni, Emilia Ambrosini, Giorgio Ferriero and Simona Ferrante

Sensors 2023, 23(4), 2232; https://doi.org/10.3390/s23042232 - 16 Feb 2023

Cited by 2 | Viewed by 3691

Abstract

Easy-to-use evaluation of Range Of Motion (ROM) during walking is necessary to make decisions during neurological rehabilitation programs and during follow-up visits in clinical and remote settings. This study discussed goniometer applications (DrGoniometer and Angles - Video Goniometer) that measure knee joint ROM during walking through smartphone cameras. The primary aim of the study is to test the inter-rater and intra-rater reliability of the collected measurements as well as their concurrent validity with an electro-goniometer. The secondary aim is to evaluate the usability of the two mobile applications. A total of 22 patients with Parkinson’s disease (18 males, age 72 (8) years), 22 post-stroke patients (17 males, age 61 (13) years), and as many healthy volunteers (8 males, age 45 (5) years) underwent knee joint ROM evaluations during walking. Clinicians and inexperienced examiners used the two mobile applications to calculate the ROM, and then rated their perceived usability through the System Usability Scale (SUS). Intraclass correlation coefficients (ICC) and correlation coefficients (corr) were calculated. Both applications showed good reliability (ICC > 0.69) and validity (corr > 0.61), and acceptable usability (SUS > 68). Smartphone-based video goniometers could be used to assess the knee ROM during walking in neurological patients, because of their acceptable degree of reliability, validity and usability. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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Figure 1
Screenshot images of the current version of Angles (a) and the DrGoniometer apps (b), and a schematic representation of the experimental study design composed of a testing patient’s session (T0) and two assessment sessions (T1, T2). The first assessment session (T1) was completed by two experienced examiners (E1, E2) and two inexperienced examiners (I1, I2). The assessments were repeated at T2 by one experienced examiner (E1) and one inexperienced examiner (I1) (c). Full article ">Figure 2
Bland-Altman plot of the knee range of motion of the three participant populations (SK: post-stroke patients, PD: patients with Parkinson’s disease, HC: healthy controls) collected by the experienced examiner (E1) using the Angles and the DrGoniometer applications. The blue line indicates the mean differences between the mobile app and electro-goniometer measures, and the red lines are the 95% lower and upper limits of agreement. Full article ">Figure 3
Bland-Altman plot of the knee range of motion of the three participant populations (SK: post-stroke patients, PD: patients with Parkinson’s disease, HC: healthy controls) collected by the inexperienced examiner (I1) using the Angles and DrGoniometer applications. The blue line indicates the mean differences between the mobile app and electro-goniometer measures, and the red lines are the 95% lower and upper limits of agreement. Full article ">Figure 4
Usability findings of the Angles app (a) and DrGoniometer app (b) evaluated with the system usability scale (SUS) by experienced and inexperienced raters, respectively, in yellow and blue. The mean score collected by experienced and inexperienced raters, respectively, in yellow and blue, was reported for the ten SUS items (1: totally disagree; 5: totally agree). The green stars indicated the best possible usability results. Full article ">

34 pages, 2005 KiB

Open AccessArticle

Detecting Gait Events from Accelerations Using Reservoir Computing

by Laurent Chiasson-Poirier, Hananeh Younesian, Katia Turcot and Julien Sylvestre

Sensors 2022, 22(19), 7180; https://doi.org/10.3390/s22197180 - 21 Sep 2022

Cited by 7 | Viewed by 2555

Abstract

Segmenting the gait cycle into multiple phases using gait event detection (GED) is a well-researched subject with many accurate algorithms. However, the algorithms that are able to perform accurate and robust GED for real-life environments and physical diseases tend to be too complex for their implementation on simple hardware systems limited in computing power and memory, such as those used in wearable devices. This study focuses on a numerical implementation of a reservoir computing (RC) algorithm called the echo state network (ESN) that is based on simple computational steps that are easy to implement on portable hardware systems for real-time detection. RC is a neural network method that is widely used for signal processing applications and uses a fast-training method based on a ridge regression adapted to the large quantity and variety of IMU data needed to use RC in various real-life environment GED. In this study, an ESN was used to perform offline GED with gait data from IMU and ground force sensors retrieved from three databases for a total of 28 healthy adults and 15 walking conditions. Our main finding is that despite its low complexity, ESN is robust for GED, with performance comparable to other state-of-the-art algorithms. Our results show the ESN is robust enough to obtain good detection results in all conditions if the algorithm is trained with variable data that match those conditions. The distribution of the mean absolute errors (MAE) between the detection times from the ESN and the force sensors were between 40 and 120 ms for 6 defined gait events (95th percentile). We compared our ESN with four different state-of-the-art algorithms from the literature. The ESN obtained a MAE not more than 10 ms above three other reference algorithms for normal walking indoor and outdoor conditions and yielded the 2nd lowest MAE and the 2nd highest true positive rate and specificity when applied to outdoor walking and running conditions. Our work opens the door to using the ESN as a GED for applications in wearable sensors for long-term patient monitoring. Full article

(This article belongs to the Special Issue Biosensors for Gait Measurements and Patient Rehabilitation)

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