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Davis JJ, Meardon SA, Brown AW, Raglin JS, Harezlak J, Gruber AH. Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2892. [PMID: 38732998 PMCID: PMC11086149 DOI: 10.3390/s24092892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.
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Affiliation(s)
- John J. Davis
- Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA;
| | - Stacey A. Meardon
- Department of Physical Therapy, East Carolina University, Greenville, NC 27858, USA;
| | - Andrew W. Brown
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - John S. Raglin
- Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA;
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA;
| | - Allison H. Gruber
- Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA;
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David JP, Schick D, Rapp L, Schick J, Glaser M. SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System. SENSORS (BASEL, SWITZERLAND) 2024; 24:2405. [PMID: 38676022 PMCID: PMC11053589 DOI: 10.3390/s24082405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/29/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.
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Affiliation(s)
| | | | | | | | - Markus Glaser
- Zentrum für Zuverlässige Mechatronische Systeme (ZMS), Aalen University, 73430 Aalen, Germany
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3
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Fain A, McCarthy A, Nindl BC, Fuller JT, Wills JA, Doyle TLA. IMUs Can Estimate Hip and Knee Range of Motion during Walking Tasks but Are Not Sensitive to Changes in Load or Grade. SENSORS (BASEL, SWITZERLAND) 2024; 24:1675. [PMID: 38475210 PMCID: PMC10934173 DOI: 10.3390/s24051675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/26/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024]
Abstract
The ability to estimate lower-extremity mechanics in real-world scenarios may untether biomechanics research from a laboratory environment. This is particularly important for military populations where outdoor ruck marches over variable terrain and the addition of external load are cited as leading causes of musculoskeletal injury As such, this study aimed to examine (1) the validity of a minimal IMU sensor system for quantifying lower-extremity kinematics during treadmill walking and running compared with optical motion capture (OMC) and (2) the sensitivity of this IMU system to kinematic changes induced by load, grade, or a combination of the two. The IMU system was able to estimate hip and knee range of motion (ROM) with moderate accuracy during walking but not running. However, SPM analyses revealed IMU and OMC kinematic waveforms were significantly different at most gait phases. The IMU system was capable of detecting kinematic differences in knee kinematic waveforms that occur with added load but was not sensitive to changes in grade that influence lower-extremity kinematics when measured with OMC. While IMUs may be able to identify hip and knee ROM during gait, they are not suitable for replicating lab-level kinematic waveforms.
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Affiliation(s)
- AuraLea Fain
- Biomechanics, Physical Performance and Exercise Research Group, Department of Health, Medicine and Human Sciences, Macquarie University’s Biomechanics, Sydney, NSW 2113, Australia; (A.F.); (A.M.); (J.T.F.); (J.A.W.)
| | - Ayden McCarthy
- Biomechanics, Physical Performance and Exercise Research Group, Department of Health, Medicine and Human Sciences, Macquarie University’s Biomechanics, Sydney, NSW 2113, Australia; (A.F.); (A.M.); (J.T.F.); (J.A.W.)
| | - Bradley C. Nindl
- Neuromuscular Research Laboratory/Warrior Performance Center, Department of Sports Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA;
| | - Joel T. Fuller
- Biomechanics, Physical Performance and Exercise Research Group, Department of Health, Medicine and Human Sciences, Macquarie University’s Biomechanics, Sydney, NSW 2113, Australia; (A.F.); (A.M.); (J.T.F.); (J.A.W.)
| | - Jodie A. Wills
- Biomechanics, Physical Performance and Exercise Research Group, Department of Health, Medicine and Human Sciences, Macquarie University’s Biomechanics, Sydney, NSW 2113, Australia; (A.F.); (A.M.); (J.T.F.); (J.A.W.)
| | - Tim L. A. Doyle
- Biomechanics, Physical Performance and Exercise Research Group, Department of Health, Medicine and Human Sciences, Macquarie University’s Biomechanics, Sydney, NSW 2113, Australia; (A.F.); (A.M.); (J.T.F.); (J.A.W.)
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4
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Fuller JT, Thewlis D, Wills JA, Buckley JD, Arnold JB, Doyle E, Doyle TLA, Bellenger CR. Optimizing Wearable Device and Testing Parameters to Monitor Running-Stride Long-Range Correlations for Fatigue Management in Field Settings. Int J Sports Physiol Perform 2024; 19:207-211. [PMID: 37995677 DOI: 10.1123/ijspp.2023-0186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/24/2023] [Accepted: 10/17/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. METHODS Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100-2000 Hz) and signal length (100-300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. RESULTS Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: -.02 to -.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: -.05 to -.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively). CONCLUSIONS Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.
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Affiliation(s)
- Joel T Fuller
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Biomechanics, Physical Performance, and Exercise Research Group, Macquarie University, Sydney, NSW, Australia
| | - Dominic Thewlis
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Jodie A Wills
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Biomechanics, Physical Performance, and Exercise Research Group, Macquarie University, Sydney, NSW, Australia
| | - Jonathan D Buckley
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), UniSA Allied Health and Human Performance Unit, University of South Australia, Adelaide, SA, Australia
| | - John B Arnold
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), UniSA Allied Health and Human Performance Unit, University of South Australia, Adelaide, SA, Australia
| | - Eoin Doyle
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Biomechanics, Physical Performance, and Exercise Research Group, Macquarie University, Sydney, NSW, Australia
| | - Tim L A Doyle
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
- Biomechanics, Physical Performance, and Exercise Research Group, Macquarie University, Sydney, NSW, Australia
| | - Clint R Bellenger
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), UniSA Allied Health and Human Performance Unit, University of South Australia, Adelaide, SA, Australia
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Thompson R, Rico Bini R, Paton C, Hébert-Losier K. Validation of LEOMO inertial measurement unit sensors with marker-based three-dimensional motion capture during maximum sprinting in track cyclists. J Sports Sci 2024; 42:179-188. [PMID: 38440835 DOI: 10.1080/02640414.2024.2324604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 02/22/2024] [Indexed: 03/06/2024]
Abstract
LEOMO™ is a commercial inertial measurement unit system that provides cycling-specific motion performance indicators (MPIs) and offers a mobile solution for monitoring cyclists. We aimed to validate the LEOMO sensors during sprint cycling using gold-standard marker-based three-dimensional (3D) motion technology (Qualisys, AB). Our secondary aim was to explore the relationship between peak power during sprints and MPIs. Seventeen elite track cyclists performed 3 × 15s seated start maximum efforts on a cycle ergometer. Based on intraclass correlation coefficient (ICC3,1), the MPIs derived from 3D and LEOMO showed moderate agreement (0.50 < 0.75) for the right foot angular range (FAR); left foot angular range first quadrant (FARQ1); right leg angular range (LAR); and mean angle of the pelvis in the sagittal plane. Agreement was poor (ICC < 0.50) between MPIs derived from 3D and LEOMO for the left FAR, right FARQ1, left LAR, and mean range of motion of the pelvis in the frontal and transverse planes. Only one LEOMO-derived (pelvic rotation) and two 3D-derived (right FARQ1 and FAR) MPIs showed large positive significant correlations with peak power. Caution is advised regarding use of the LEOMO for short maximal cycling efforts and derived MPIs to inform peak sprint cycling power production.
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Affiliation(s)
- Roné Thompson
- Division of Health, Engineering, Computing and Science, Te Huataki Waiora School of Health, University of Waikato, Adams Centre for High Performance, Tauranga, New Zealand
- Department of Performance Health, High Performance Sport New Zealand, Grassroots Trust Velodrome, Cambridge, New Zealand
| | | | - Carl Paton
- School of Health and Sport Science, Te Pukenga at Eastern Institute of Technology, Napier, New Zealand
| | - Kim Hébert-Losier
- Division of Health, Engineering, Computing and Science, Te Huataki Waiora School of Health, University of Waikato, Adams Centre for High Performance, Tauranga, New Zealand
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6
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Rasmussen J, Skejø S, Waagepetersen RP. Predicting Tissue Loads in Running from Inertial Measurement Units. SENSORS (BASEL, SWITZERLAND) 2023; 23:9836. [PMID: 38139682 PMCID: PMC10747732 DOI: 10.3390/s23249836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. METHODS We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. RESULTS Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. CONCLUSIONS Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.
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Affiliation(s)
- John Rasmussen
- Department of Materials and Production, Aalborg University, Fibigerstraede 16, 9220 Aalborg East, Denmark
| | - Sebastian Skejø
- Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark;
- Research Unit for General Practice, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark
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7
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Lin YC, Price K, Carmichael DS, Maniar N, Hickey JT, Timmins RG, Heiderscheit BC, Blemker SS, Opar DA. Validity of Inertial Measurement Units to Measure Lower-Limb Kinematics and Pelvic Orientation at Submaximal and Maximal Effort Running Speeds. SENSORS (BASEL, SWITZERLAND) 2023; 23:9599. [PMID: 38067972 PMCID: PMC10708829 DOI: 10.3390/s23239599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/27/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023]
Abstract
Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved non-motorized treadmill using discrete (Bland-Altman analysis) and continuous (root mean square error [RMSE], normalised RMSE, Pearson correlation, and statistical parametric mapping analysis [SPM]) metrics. The hip, knee, and ankle flexions and the pelvic orientation (tilt, obliquity, and rotation) were captured concurrently from both IMU and optical motion capture systems, as 20 participants ran steadily at 70%, 80%, 90%, and 100% of their maximal effort sprinting speed (5.36 ± 0.55, 6.02 ± 0.60, 6.66 ± 0.71, and 7.09 ± 0.73 m/s, respectively). Bland-Altman analysis indicated a systematic bias within ±1° for the peak pelvic tilt, rotation, and lower-limb kinematics and -3.3° to -4.1° for the pelvic obliquity. The SPM analysis demonstrated a good agreement in the hip and knee flexion angles for most phases of the stride cycle, albeit with significant differences noted around the ipsilateral toe-off. The RMSE ranged from 4.3° (pelvic obliquity at 70% speed) to 7.8° (hip flexion at 100% speed). Correlation coefficients ranged from 0.44 (pelvic tilt at 90%) to 0.99 (hip and knee flexions at all speeds). Running speed minimally but significantly affected the RMSE for the hip and ankle flexions. The present IMU system is effective for measuring lower-limb kinematics during sprinting, but the pelvic orientation estimation was less accurate.
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Affiliation(s)
- Yi-Chung Lin
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
| | - Kara Price
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
| | - Declan S. Carmichael
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
| | - Nirav Maniar
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
| | - Jack T. Hickey
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
- Department of Sport Science and Nutrition, Maynooth University, W23 F2H6 Co. Kildare, Ireland
| | - Ryan G. Timmins
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
| | - Bryan C. Heiderscheit
- Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Silvia S. Blemker
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA;
- Springbok Analytics, Charlottesville, VA 22902, USA
| | - David A. Opar
- School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC 3065, Australia; (K.P.); (D.S.C.); (N.M.); (J.T.H.); (R.G.T.); (D.A.O.)
- Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Fitzroy, VIC 3065, Australia
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Gaiesky SKT, Fridman L, Michie T, Blazey P, Tran N, Schneeberg A, Napier C. The one-week and three-month reliability of acceleration outcomes from an insole-embedded inertial measurement unit during treadmill running. Sports Biomech 2023:1-15. [PMID: 37941419 DOI: 10.1080/14763141.2023.2275258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023]
Abstract
Inertial measurement units (IMUs) represent an exciting opportunity for researchers to broaden our understanding of running-related injuries, and for clinicians to expand their application of running gait analysis. The primary aim of our study was to investigate the 1-week (short-term) and 3-month (long-term) reliability of peak resultant, vertical, and anteroposterior accelerations derived from insole-embedded IMUs. The secondary aim was to assess the reliability of peak acceleration variability and left-right limb symmetry in all directions over the short and long term. A sample of healthy adult rearfoot runners (n = 23; age 41.7 ± 11.2 years) ran at a variety of speeds (2.5 m/s, 3.0 m/s, and 3.5 m/s) on a treadmill in standardised footwear with insole-embedded IMUs in each shoe. Peak accelerations exhibited good to excellent short-term reliability and moderate to excellent long-term reliability in all directions. Peak acceleration variability showed poor to good short- and long-term reliability, whereas the symmetry of peak accelerations demonstrated moderate to excellent and moderate to good short- and long-term reliability, respectively. Our results demonstrate how insole-embedded IMUs represent a viable option for clinicians to measure peak accelerations within the clinic.
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Affiliation(s)
- Sean K T Gaiesky
- Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Metro Vancouver, BC, Canada
| | | | - Tom Michie
- Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Metro Vancouver, BC, Canada
- Centre for Aging SMART, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
| | - Paul Blazey
- Centre for Aging SMART, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
| | | | | | - Christopher Napier
- Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Metro Vancouver, BC, Canada
- Centre for Aging SMART, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
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9
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Florenciano Restoy JL, Solé-Casals J, Borràs-Boix X. Effect of Foot Orthoses on Angular Velocity of Feet. SENSORS (BASEL, SWITZERLAND) 2023; 23:8917. [PMID: 37960617 PMCID: PMC10650853 DOI: 10.3390/s23218917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
There is some uncertainty regarding how foot orthoses (FO) affect the biomechanics of the lower extremities during running in non-injured individuals. This study aims to describe the behavior of the angular velocity of the foot in the stride cycle measured with a low-sampling-rate IMU device commonly used by podiatrists. Specific objectives were to determine if there are differences in angular velocity between the right and left foot and to determine the effect of foot orthoses (FO) on the 3D angular velocity of the foot during running. The sample was composed of 40 male adults (age: 43.0 ± 13.8 years, weight: 72.0 ± 5.5 kg, and height: 175.5 ± 7.0 cm), who were healthy and without any locomotor system alterations at the time of the test. All subjects use FO on a regular basis. The results show that there are significant differences in the transverse plane between feet, with greater differences in the right foot. Significant differences between FO and non-FO conditions were observed in the frontal and transverse planes on the left foot and in the sagittal and transverse planes on the right foot. FO decreases the velocity of the foot in dorsi-plantar flexion and abduction and increases the velocity in inversion. The kinematic changes in foot velocity occur between 30% and 60% of the complete cycle, and the FO reduces the velocity in abduction and dorsi-plantar flexion and increases the velocity in inversion-eversion, which facilitates the transition to the oscillating leg and with it the displacement of the center of mass. Quantifying possible asymmetries and assessing the effect of foot orthoses may aid in improving running mechanics and preventing injuries in individuals.
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Affiliation(s)
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic—Central University of Catalonia, 08500 Vic, Spain
| | - Xantal Borràs-Boix
- Sport Exercise and Human Movement, University of Vic—Central University of Catalonia, 08500 Vic, Spain
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10
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Ruder MC, Masood Z, Kobsar D. Reliability of waveforms and gait metrics from multiple outdoor wearable inertial sensors collections in adults with knee osteoarthritis. J Biomech 2023; 160:111818. [PMID: 37793202 DOI: 10.1016/j.jbiomech.2023.111818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023]
Abstract
Wearable sensors may allow research to move outside of controlled laboratory settings to be able to collect real-world data in clinical populations, such as older adults with osteoarthritis. However, the reliability of these sensors must be established across multiple out-of-lab data collections. Nine older adults with symptomatic knee arthritis wore wearable inertial sensors on their proximal tibias during an outdoor 6-minute walk test outside of a controlled laboratory setting as part of a pilot study. Reliability of the underlying waveforms, discrete peak outcomes, and spatiotemporal outcomes were assessed over four separate data collections, each approximately 1 week apart. Reliability at a different number of included strides was also assessed at 10, 20, 50, and 100 strides. The underlying waveforms and discrete peak outcome measures had good-to-excellent reliability for all axes, with lower reliability in frontal plane angular velocity axis. Spatiotemporal outcomes demonstrated excellent reliability. The inclusion of additional strides had little to no effect on reliability in most axes, but the confidence intervals generally became smaller across all axes. However, there was improvement in axes with lower (i.e., good) reliability. These data were collected in an out-of-lab setting, and the results are generally consistent with previous in-lab data collections, likely due to its semi-controlled nature. Additional out-of-laboratory research is required to investigate if these trends continue during truly free-living collections. This study provides support for increasing research conducted in out-of-lab data collections, as demonstrated by the good-to-excellent reliability of all axes.
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Affiliation(s)
- Matthew C Ruder
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada.
| | - Zaryan Masood
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada
| | - Dylan Kobsar
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada
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11
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Mason R, Godfrey A, Barry G, Stuart S. Wearables for running gait analysis: A study protocol. PLoS One 2023; 18:e0291289. [PMID: 37695752 PMCID: PMC10495009 DOI: 10.1371/journal.pone.0291289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
Quantitative running gait analysis is an important tool that provides beneficial outcomes to injury risk/recovery or performance assessment. Wearable devices have allowed running gait to be evaluated in any environment (i.e., laboratory or real-world settings), yet there are a plethora of different grades of devices (i.e., research-grade, commercial, or novel multi-modal) available with little information to make informed decisions on selection. This paper outlines a protocol that will examine different grades of wearables for running gait analysis in healthy individuals. Specifically, this pilot study will: 1) examine analytical validity and reliability of wearables (research-grade, commercial, high-end multimodal) within a controlled laboratory setting; 2) examine analytical validation of different grades of wearables in a real-world setting, and 3) explore clinical validation and usability of wearables for running gait analysis (e.g., injury history (previously injured, never injured), performance level (novice, elite) and relationship to meaningful outcomes). The different grades of wearable include: (1) A research-grade device, the Ax6 consists of a configurable tri-axial accelerometer and tri-axial gyroscope with variable sampling capabilities; (2) attainable (low-grade) commercial with proprietary software, the DorsaVi ViMove2 consisting of two, non-configurable IMUs modules, with a fixed sampling rate and (3) novel multimodal high-end system, the DANU Sports System that is a pair of textile socks, that contain silicone based capacitive pressure sensors, and configurable IMU modules with variable sampling rates. Clinical trial registration: Trial registration: NCT05277181.
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Affiliation(s)
- Rachel Mason
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle, United Kingdom
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, United Kingdom
| | - Gillian Barry
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle, United Kingdom
| | - Samuel Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle, United Kingdom
- Northumbria Healthcare NHS foundation trust, North Shields, United Kingdom
- Department of Neurology, Oregon Health and Science University, Portland, Oregon, United States of America
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12
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Boolani A, Gruber AH, Torad AA, Stamatis A. Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:6624. [PMID: 37514917 PMCID: PMC10384769 DOI: 10.3390/s23146624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/27/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Depressive mood states in healthy populations are prevalent but often under-reported. Biases exist in self-reporting of depression in otherwise healthy individuals. Gait and balance control can serve as objective markers for identifying those individuals, particularly in real-world settings. We utilized inertial measurement units (IMU) to measure gait and balance control. An exploratory, cross-sectional design was used to compare individuals who reported feeling depressed at the moment (n = 49) with those who did not (n = 84). The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was employed to ensure internal validity. We recruited 133 participants aged between 18-36 years from the university community. Various instruments were used to evaluate participants' present depressive symptoms, sleep, gait, and balance. Gait and balance variables were used to detect depression, and participants were categorized into three groups: not depressed, mild depression, and moderate-high depression. Participant characteristics were analyzed using ANOVA and Kruskal-Wallis tests, and no significant differences were found in age, height, weight, BMI, and prior night's sleep between the three groups. Classification models were utilized for depression detection. The most accurate model incorporated both gait and balance variables, yielding an accuracy rate of 84.91% for identifying individuals with moderate-high depression compared to non-depressed individuals.
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Affiliation(s)
- Ali Boolani
- Honors Department, Clarkson University, Potsdam, NY 13699, USA
| | - Allison H Gruber
- Department of Kinesiology, Indiana University, Bloomington, IN 47405, USA
| | - Ahmed Ali Torad
- Faculty of Physical Therapy, Kafrelsheik University, Kafr El Sheik 33516, Egypt
| | - Andreas Stamatis
- Department of Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA
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13
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Zeng Z, Liu Y, Wang L. Validity of IMU measurements on running kinematics in non-rearfoot strike runners across different speeds. J Sports Sci 2023; 41:1083-1092. [PMID: 37733423 DOI: 10.1080/02640414.2023.2259211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/17/2023] [Indexed: 09/22/2023]
Abstract
This study aims to determine the validity of the lower extremity joint kinematics measured by inertial measurement units (IMUs) in non-rearfoot strike pattern (NRFS) runners across different speeds. Fifteen NRFS runners completed three 2-min running tests on a treadmill in random order at 8, 10 and 12 km/h, whilst data were synchronously collected using the IMU system and an optical motion capture system. Before the offset was corrected, the validity of the knee angle waveform was higher than that of the hip and ankle; after the offset was corrected, the validity increased in all three joints. The correlation between the touchdown angles in the sagittal plane measured by the two systems was relatively high after the offset was corrected. The running speed influenced the offset-corrected measurements, with higher error values at higher speeds. The IMU system was able to provide measurements of running kinematics in the sagittal plane of NRFS runners at different running speeds but was unable to reliably measure motion in the frontal and horizontal planes. Future research should analyse the 3D gait of NRFS runners under a larger range of speed conditions to provide evidentiary support for the use of IMUs in running analysis outside the laboratory.
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Affiliation(s)
- Ziwei Zeng
- Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China
| | - Yue Liu
- Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China
| | - Lin Wang
- Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China
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14
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Kiernan D, Dunn Siino K, Hawkins DA. Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115022. [PMID: 37299749 DOI: 10.3390/s23115022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and -24.3 ms; LOAs -96.8 to +131.6 and -137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs -143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases -30.4 and +29.0 ms; LOAs -149.2 to +88.5 and -83.3 to +141.3 ms) and the Auvinet method for TC (bias -2.8 ms; LOAs -152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).
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Affiliation(s)
- Dovin Kiernan
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Kristine Dunn Siino
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616, USA
| | - David A Hawkins
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616, USA
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616, USA
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15
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Scheltinga BL, Kok JN, Buurke JH, Reenalda J. Estimating 3D ground reaction forces in running using three inertial measurement units. Front Sports Act Living 2023; 5:1176466. [PMID: 37255726 PMCID: PMC10225635 DOI: 10.3389/fspor.2023.1176466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/06/2023] [Indexed: 06/01/2023] Open
Abstract
To understand the mechanisms causing running injuries, it is crucial to get insights into biomechanical loading in the runners' environment. Ground reaction forces (GRFs) describe the external forces on the body during running, however, measuring these forces is usually only possible in a gait laboratory. Previous studies show that it is possible to use inertial measurement units (IMUs) to estimate vertical forces, however, forces in anterior-posterior direction play an important role in the push-off. Furthermore, to perform an inverse dynamics approach, for modelling tissue specific loads, 3D GRFs are needed as input. Therefore, the goal of this work was to estimate 3D GRFs using three inertial measurement units. Twelve rear foot strike runners did nine trials at three different velocities (10, 12 and 14 km/h) and three stride frequencies (preferred and preferred ± 10%) on an instrumented treadmill. Then, data from IMUs placed on the pelvis and lower legs were used as input for artificial neural networks (ANNs) to estimate 3D GRFs. Additionally, estimated vertical GRF from a physical model was used as input to create a hybrid machine learning model. Using different splits in validation and training data, different ANNs were fitted and assembled into an ensemble model. Leave-one-subject-out cross-validation was used to validate the models. Performance of the machine learning, hybrid machine learning and a physical model were compared. The estimated vs. measured GRF for the hybrid model had a RMSE normalized over the full range of values of 10.8, 7.8 and 6.8% and a Pearson correlation coefficient of 0.58, 0.91, 0.97 for the mediolateral direction, posterior-anterior and vertical direction respectively. Performance for the three compared models was similar. The ensemble models showed higher model accuracy compared to the ensemble-members. This study is the first to estimate 3D GRF during continuous running from IMUs and shows that it is possible to estimate GRF in posterior-anterior and vertical direction, making it possible to estimate these forces in the outdoor setting. This step towards quantification of biomechanical load in the runners' environment is helpful to gain a better understanding of the development of running injuries.
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Affiliation(s)
- Bouke L. Scheltinga
- Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
- Roessingh Research and Development, Enschede, Netherlands
| | - Joost N. Kok
- Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
| | - Jaap H. Buurke
- Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
- Roessingh Research and Development, Enschede, Netherlands
| | - Jasper Reenalda
- Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
- Roessingh Research and Development, Enschede, Netherlands
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16
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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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17
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Guerra-Armas J, Oliva-Hazañas A, Hazañas-Ruiz S, Torrontegui-Duarte M, Cervero-Simonet M, Morales-Asencio JM, Pineda-Galan C, Flores-Cortes M, Luque-Suarez A. The presence of a previous lower limb injury does not affect step asymmetry in elite basketball players: A prospective, longitudinal observational study. INT J PERF ANAL SPOR 2023. [DOI: 10.1080/24748668.2023.2194604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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18
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Miranda-Oliveira P, Branco M, Fernandes O. Accuracy and Interpretation of the Acceleration from an Inertial Measurement Unit When Applied to the Sprint Performance of Track and Field Athletes. SENSORS (BASEL, SWITZERLAND) 2023; 23:1761. [PMID: 36850357 PMCID: PMC9968079 DOI: 10.3390/s23041761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/17/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
In this study, we aimed to assess sprinting using a developed instrument encompassing an inertial measurement unit (IMU) in order to analyze athlete performance during the sprint, as well as to determine the number of steps, ground contact time, flight time, and step time using a high-speed camera as a reference. Furthermore, we correlated the acceleration components (XYZ) and acceleration ratio with the performance achieved in each split time obtained using photocells. Six athletes (four males and two females) ran 40 m with the IMU placed on their fifth lumbar vertebra. The accuracy was measured through the mean error (standard deviation), correlation (r), and comparison tests. The device could identify 88% to 98% of the number of steps. The GCT, flight time, and step time had mean error rates of 0.000 (0.012) s, 0.010 (0.011) s, and 0.009 (0.009) s when compared with the high-speed camera, respectively. The step time showed a correlation rate of r = 0.793 (p = 0.001) with no statistical differences, being the only parameter with high accuracy. Additionally, we showed probable symmetries, and through linear regression models identified that higher velocities result in the maximum anteroposterior acceleration, mainly over 0-40 m. Our device based on a Wi-Fi connection can determine the step time with accuracy and can show asymmetries, making it essential for coaches and medical teams. A new feature of this study was that the IMUs allowed us to understand that anteroposterior acceleration is associated with the best performance during the 40 m sprint test.
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Affiliation(s)
- Paulo Miranda-Oliveira
- Interdisciplinary Research Centre Egas Moniz (CiiEM), Egas Moniz School of Health & Science, 2829-511 Almada, Portugal
- School of Technology and Management (ESTG), Polytechnic of Leiria, 2411-901 Leiria, Portugal
- Portuguese Athletics Federation (FPA), 2799-538 Oeiras, Portugal
| | - Marco Branco
- Escola Superior de Desporto de Rio Maior, Instituto Politécnico de Santarém, 2040-413 Rio Maior, Portugal
- Centro Interdisciplinar de Estudo da Performance Humana (CIPER), Faculdade Motricidade Humana da Universidade de Lisboa, 1495-751 Oeiras, Portugal
| | - Orlando Fernandes
- Sport and Health Department, School of Health and Human Development, Universidad de Évora, 7000-671 Évora, Portugal
- Comprehensive Health Research Center (CHRC), University of Évora, 7000-671 Évora, Portugal
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19
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Slemenšek J, Fister I, Geršak J, Bratina B, van Midden VM, Pirtošek Z, Šafarič R. Human Gait Activity Recognition Machine Learning Methods. SENSORS (BASEL, SWITZERLAND) 2023; 23:745. [PMID: 36679546 PMCID: PMC9865094 DOI: 10.3390/s23020745] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/23/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject's quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.
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Affiliation(s)
- Jan Slemenšek
- Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
| | - Iztok Fister
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
| | - Jelka Geršak
- Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
| | - Božidar Bratina
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
| | | | - Zvezdan Pirtošek
- Department of Neurology, University Clinical Centre, 1000 Ljubljana, Slovenia
| | - Riko Šafarič
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
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20
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Young F, Mason R, Morris R, Stuart S, Godfrey A. Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020696. [PMID: 36679494 PMCID: PMC9866353 DOI: 10.3390/s23020696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google’s pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01−0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
- Correspondence:
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21
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Khant M, Gouwanda D, Gopalai AA, Lim KH, Foong CC. Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:556. [PMID: 36617154 PMCID: PMC9823674 DOI: 10.3390/s23010556] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/26/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.
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Affiliation(s)
- Min Khant
- School of Engineering, Monash University Malaysia, Subang Jaya 47500, Malaysia
| | - Darwin Gouwanda
- School of Engineering, Monash University Malaysia, Subang Jaya 47500, Malaysia
| | - Alpha A. Gopalai
- School of Engineering, Monash University Malaysia, Subang Jaya 47500, Malaysia
| | - King Hann Lim
- Department of Electrical & Computer Engineering, Curtin University Malaysia, Miri 98009, Malaysia
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22
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Zandbergen MA, Buurke JH, Veltink PH, Reenalda J. Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running. Front Sports Act Living 2023; 5:1085513. [PMID: 37139307 PMCID: PMC10150107 DOI: 10.3389/fspor.2023.1085513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/23/2023] [Indexed: 05/05/2023] Open
Abstract
Measuring impact-related quantities in running is of interest to improve the running technique. Many quantities are typically measured in a controlled laboratory setting, even though most runners run in uncontrolled outdoor environments. While monitoring running mechanics in an uncontrolled environment, a decrease in speed or stride frequency can mask fatigue-related changes in running mechanics. Hence, this study aimed to quantify and correct the subject-specific effects of running speed and stride frequency on changes in impact-related running mechanics during a fatiguing outdoor run. Seven runners ran a competitive marathon while peak tibial acceleration and knee angles were measured with inertial measurement units. Running speed was measured through sports watches. Median values over segments of 25 strides throughout the marathon were computed and used to create subject-specific multiple linear regression models. These models predicted peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee flexion based on running speed and stride frequency. Data were corrected for individual speed and stride frequency effects during the marathon. The speed and stride frequency corrected and uncorrected data were divided into ten stages to investigate the effect of marathon stage on mechanical quantities. This study showed that running speed and stride frequency explained, on average, 20%-30% of the variance in peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee angles while running in an uncontrolled setting. Regression coefficients for speed and stride frequency varied strongly between subjects. Speed and stride frequency corrected peak tibial acceleration, and maximum stance phase knee flexion increased throughout the marathon. At the same time, uncorrected maximum stance phase knee angles showed no significant differences between marathon stages due to a decrease in running speed. Hence, subject-specific effects of changes in speed and stride frequency influence the interpretation of running mechanics and are relevant when monitoring, or comparing the gait pattern between runs in uncontrolled environments.
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Affiliation(s)
- Marit A. Zandbergen
- Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands
- Department of Rehabilitation Technology, Roessingh Research and Development, Enschede, Netherlands
- Correspondence: Marit A. Zandbergen
| | - Jaap H. Buurke
- Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands
- Department of Rehabilitation Technology, Roessingh Research and Development, Enschede, Netherlands
| | - Peter H. Veltink
- Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands
| | - Jasper Reenalda
- Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands
- Department of Rehabilitation Technology, Roessingh Research and Development, Enschede, Netherlands
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23
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Fohrmann D, Hamacher D, Sanchez-Alvarado A, Potthast W, Mai P, Willwacher S, Hollander K. Reliability of Running Stability during Treadmill and Overground Running. SENSORS (BASEL, SWITZERLAND) 2022; 23:347. [PMID: 36616946 PMCID: PMC9823852 DOI: 10.3390/s23010347] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Running stability is the ability to withstand naturally occurring minor perturbations during running. It is susceptible to external and internal running conditions such as footwear or fatigue. However, both its reliable measurability and the extent to which laboratory measurements reflect outdoor running remain unclear. This study aimed to evaluate the intra- and inter-day reliability of the running stability as well as the comparability of different laboratory and outdoor conditions. Competitive runners completed runs on a motorized treadmill in a research laboratory and overground both indoors and outdoors. Running stability was determined as the maximum short-term divergence exponent from the raw gyroscope signals of wearable sensors mounted to four different body locations (sternum, sacrum, tibia, and foot). Sacrum sensor measurements demonstrated the highest reliabilities (good to excellent; ICC = 0.85 to 0.91), while those of the tibia measurements showed the lowest (moderate to good; ICC = 0.55 to 0.89). Treadmill measurements depicted systematically lower values than both overground conditions for all sensor locations (relative bias = -9.8% to -2.9%). The two overground conditions, however, showed high agreement (relative bias = -0.3% to 0.5%; relative limits of agreement = 9.2% to 15.4%). Our results imply moderate to excellent reliability for both overground and treadmill running, which is the foundation of further research on running stability.
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Affiliation(s)
- Dominik Fohrmann
- Institute of Interdisciplinary Exercise Science and Sports Medicine, Faculty of Medicine, MSH Medical School Hamburg, 20457 Hamburg, Germany
- Institute of Biomechanics and Orthopedics, German Sport University Cologne, 50933 Cologne, Germany
| | - Daniel Hamacher
- Institute of Sports Science, Friedrich Schiller University Jena, 07749 Jena, Germany
| | - Alberto Sanchez-Alvarado
- Department of Sports and Exercise Medicine, Institute of Human Movement Science, University of Hamburg, 20148 Hamburg, Germany
| | - Wolfgang Potthast
- Institute of Biomechanics and Orthopedics, German Sport University Cologne, 50933 Cologne, Germany
| | - Patrick Mai
- Institute of Biomechanics and Orthopedics, German Sport University Cologne, 50933 Cologne, Germany
- Department of Mechanical and Process Engineering, Offenburg University of Applied Sciences, 77652 Offenburg, Germany
| | - Steffen Willwacher
- Department of Mechanical and Process Engineering, Offenburg University of Applied Sciences, 77652 Offenburg, Germany
| | - Karsten Hollander
- Institute of Interdisciplinary Exercise Science and Sports Medicine, Faculty of Medicine, MSH Medical School Hamburg, 20457 Hamburg, Germany
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Franco T, Sestrem L, Henriques PR, Alves P, Varanda Pereira MJ, Brandão D, Leitão P, Silva A. Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197605. [PMID: 36236708 PMCID: PMC9572597 DOI: 10.3390/s22197605] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 09/30/2022] [Accepted: 10/01/2022] [Indexed: 06/12/2023]
Abstract
The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient's home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient's movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O(1) were tested to improve the signal's noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.
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Affiliation(s)
- Tiago Franco
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-253 Bragança, Portugal
| | - Leonardo Sestrem
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-253 Bragança, Portugal
| | | | - Paulo Alves
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-253 Bragança, Portugal
| | - Maria João Varanda Pereira
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-253 Bragança, Portugal
| | - Diego Brandão
- Federal Center of Techonology of Rio de Janeiro (CEFET/RJ), Rio de Janeiro 20271-204, Brazil
| | - Paulo Leitão
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-253 Bragança, Portugal
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Young F, Mason R, Wall C, Morris R, Stuart S, Godfrey A. Examination of a foot mounted IMU-based methodology for a running gait assessment. Front Sports Act Living 2022; 4:956889. [PMID: 36147582 PMCID: PMC9485551 DOI: 10.3389/fspor.2022.956889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Gait assessment is essential to understand injury prevention mechanisms during running, where high-impact forces can lead to a range of injuries in the lower extremities. Information regarding the running style to increase efficiency and/or selection of the correct running equipment, such as shoe type, can minimize the risk of injury, e.g., matching a runner's gait to a particular set of cushioning technologies found in modern shoes (neutral/support cushioning). Awareness of training or selection of the correct equipment requires an understanding of a runner's biomechanics, such as determining foot orientation when it strikes the ground. Previous work involved a low-cost approach with a foot-mounted inertial measurement unit (IMU) and an associated zero-crossing-based methodology to objectively understand a runner's biomechanics (in any setting) to learn about shoe selection. Here, an investigation of the previously presented ZC-based methodology is presented only to determine general validity for running gait assessment in a range of running abilities from novice (8 km/h) to experienced (16 km/h+). In comparison to Vicon 3D motion tracking data, the presented approach can extract pronation, foot strike location, and ground contact time with good [ICC(2,1) > 0.750] to excellent [ICC(2,1) > 0.900] agreement between 8-12 km/h runs. However, at higher speeds (14 km/h+), the ZC-based approach begins to deteriorate in performance, suggesting that other features and approaches may be more suitable for faster running and sprinting tasks.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Rachel Mason
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Conor Wall
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Rosie Morris
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Samuel Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
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Liang FY, Gao F, Cao J, Law SW, Liao WH. Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices. SENSORS 2022; 22:s22134814. [PMID: 35808309 PMCID: PMC9269045 DOI: 10.3390/s22134814] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/13/2022] [Accepted: 06/23/2022] [Indexed: 02/06/2023]
Abstract
The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human–machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer’s motion for lower limb assistive devices.
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Affiliation(s)
- Feng-Yan Liang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China;
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
| | - Fei Gao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China;
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Junyi Cao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
| | | | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China;
- Correspondence:
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27
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Proposal of an Alpine Skiing Kinematic Analysis with the Aid of Miniaturized Monitoring Sensors, a Pilot Study. SENSORS 2022; 22:s22114286. [PMID: 35684907 PMCID: PMC9185405 DOI: 10.3390/s22114286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 02/04/2023]
Abstract
The recent growth and spread of smart sensor technologies make these connected devices suitable for diagnostic and monitoring in different fields. In particular, these sensors are useful in diagnostics for control of diseases or during rehabilitation. They are also extensively used in the monitoring field, both by non-expert and expert users, to monitor health status and progress during a sports activity. For athletes, these devices could be used to control and enhance their performance. This development has led to the realization of miniaturized sensors that are wearable during different sporting activities without interfering with the movements of the athlete. The use of these sensors, during training or racing, opens new frontiers for the understanding of motions and causes of injuries. This pilot study introduced a motion analysis system to monitor Alpine ski activities during training sessions. Through five inertial measurement units (IMUs), placed on five points of the athletes, it is possible to compute the angle of each joint and evaluate the ski run. Comparing the IMU data, firstly, with a video and then proposing them to an expert coach, it is possible to observe from the data the same mistakes visible in the camera. The aim of this work is to find a tool to support ski coaches during training sessions. Since the evaluation of athletes is now mainly developed with the support of video, we evaluate the use of IMUs to support the evaluation of the coach with more precise data.
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Preatoni E, Bergamini E, Fantozzi S, Giraud LI, Orejel Bustos AS, Vannozzi G, Camomilla V. The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:3225. [PMID: 35590914 PMCID: PMC9105988 DOI: 10.3390/s22093225] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 02/06/2023]
Abstract
Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.
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Affiliation(s)
- Ezio Preatoni
- Department for Health, University of Bath, Bath BA2 7AY, UK; (E.P.); (L.I.G.)
- Centre for Health and Injury and Illness Prevention in Sport, University of Bath, Bath BA2 7AY, UK
| | - Elena Bergamini
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
| | - Silvia Fantozzi
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;
- Health Sciences and Technologies—Interdepartmental Centre for Industrial Research, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
| | - Lucie I. Giraud
- Department for Health, University of Bath, Bath BA2 7AY, UK; (E.P.); (L.I.G.)
| | - Amaranta S. Orejel Bustos
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
| | - Giuseppe Vannozzi
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
| | - Valentina Camomilla
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
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