1
|
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.
Collapse
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
| | | |
Collapse
|
2
|
Petrigna L, Musumeci G. 3D Analysis of Human Movement, Sport, and Health Promotion. J Funct Morphol Kinesiol 2023; 8:157. [PMID: 37987493 PMCID: PMC10660536 DOI: 10.3390/jfmk8040157] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/03/2023] [Indexed: 11/22/2023] Open
Abstract
This Special Issue, "3D Analysis of Human Movement, Sport, and Health Promotion", aimed to collect studies that assessed motor functions and alterations [...].
Collapse
Affiliation(s)
- Luca Petrigna
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia 97, 95123 Catania, Italy;
| | | |
Collapse
|
3
|
López-Blanco R, Sorrentino Rodriguez A, Cubo E, Gabilondo Í, Ezpeleta D, Labrador-Espinosa MÁ, Sánchez-Ferro Á, Tejero C, Matarazzo M. Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology. Neurologia 2023; 38:591-598. [PMID: 35842132 DOI: 10.1016/j.nrleng.2020.10.011] [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: 09/30/2020] [Accepted: 10/10/2020] [Indexed: 10/17/2022] Open
Abstract
INTRODUCTION New technologies are increasingly widespread in biomedicine. Using the consensus definition of new technologies established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN. MATERIAL AND METHODS We defined the concept of new technology in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of technology, the field of neurology, and the geographical provenance of the studies. RESULTS We identified 299 communications related with new technologies from a total of 8139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where new technologies were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to new technologies (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%). CONCLUSIONS The number of communications addressing new technologies follows an upward trend. The number of technologies used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.
Collapse
Affiliation(s)
- R López-Blanco
- Servicio Integrado de Neurología, Hospital Universitario Rey Juan Carlos (Móstoles), Hospital General de Villalba, Hospital Universitario Infanta Elena (Valdemoro), Madrid, Spain
| | | | - E Cubo
- Hospital Universitario Burgos, Burgos, Spain
| | - Í Gabilondo
- Hospital Universitario de Cruces, Barakaldo, Spain
| | - D Ezpeleta
- Hospital Universitario Quirónsalud Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - M Á Labrador-Espinosa
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | - Á Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, Spain
| | - C Tejero
- Hospital Clinico Universitario Lozano Blesa, Zaragoza, Spain
| | - M Matarazzo
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, Spain; Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada.
| |
Collapse
|
4
|
Dasgupta A, Sharma R, Mishra C, Nagaraja VH. Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data. Bioengineering (Basel) 2023; 10:bioengineering10050510. [PMID: 37237580 DOI: 10.3390/bioengineering10050510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/16/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.
Collapse
Affiliation(s)
- Abhishek Dasgupta
- Doctoral Training Centre, University of Oxford, 1-4 Keble Road, Oxford OX1 3NP, UK
| | - Rahul Sharma
- Laboratory for Computation and Visualization in Mathematics and Mechanics, Institute of Mathematics, Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
| | - Challenger Mishra
- Department of Computer Science & Technology, University of Cambridge, 15 J.J. Thomson Ave., Cambridge CB3 0FD, UK
| | - Vikranth Harthikote Nagaraja
- Natural Interaction Laboratory, Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Oxford OX3 7DQ, UK
| |
Collapse
|
5
|
Kim GJ, Parnandi A, Eva S, Schambra H. The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review. Disabil Rehabil 2022; 44:6119-6138. [PMID: 34328803 PMCID: PMC9912423 DOI: 10.1080/09638288.2021.1957027] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/25/2021] [Accepted: 07/13/2021] [Indexed: 01/27/2023]
Abstract
PURPOSE To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment. MATERIALS AND METHODS The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis. RESULTS We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE. CONCLUSIONS Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.IMPLICATIONS FOR REHABILITATIONSensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.
Collapse
Affiliation(s)
- Grace J. Kim
- Department of Occupational Therapy, Steinhardt School of Culture, Education and Human Development, New York University, New York, NY, USA
| | - Avinash Parnandi
- Department of Neurology, NYU Langone Grossman School of Medicine, New York, NY, USA
| | - Sharon Eva
- Department of Occupational Therapy, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Heidi Schambra
- Department of Neurology, NYU Langone Grossman School of Medicine, New York, NY, USA
| |
Collapse
|
6
|
Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering (Basel) 2022; 9:bioengineering9070273. [PMID: 35877324 PMCID: PMC9311612 DOI: 10.3390/bioengineering9070273] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 01/19/2023] Open
Abstract
Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.
Collapse
|
7
|
Tang W, van Ooijen PMA, Sival DA, Maurits NM. 2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings. SENSORS (BASEL, SWITZERLAND) 2022; 22:4245. [PMID: 35684866 PMCID: PMC9185346 DOI: 10.3390/s22114245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer's assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders.
Collapse
Affiliation(s)
- Wei Tang
- Department of Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands;
| | - Peter M. A. van Ooijen
- Data Science Center in Health, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands;
| | - Deborah A. Sival
- Department of Pediatric Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands;
| | - Natasha M. Maurits
- Department of Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands;
| |
Collapse
|
8
|
Stenum J, Cherry-Allen KM, Pyles CO, Reetzke RD, Vignos MF, Roemmich RT. Applications of Pose Estimation in Human Health and Performance across the Lifespan. SENSORS (BASEL, SWITZERLAND) 2021; 21:7315. [PMID: 34770620 PMCID: PMC8588262 DOI: 10.3390/s21217315] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/29/2021] [Accepted: 10/31/2021] [Indexed: 01/15/2023]
Abstract
The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.
Collapse
Affiliation(s)
- Jan Stenum
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA;
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Kendra M. Cherry-Allen
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Connor O. Pyles
- Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723, USA; (C.O.P.); (M.F.V.)
| | - Rachel D. Reetzke
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD 21211, USA;
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Michael F. Vignos
- Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723, USA; (C.O.P.); (M.F.V.)
| | - Ryan T. Roemmich
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA;
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| |
Collapse
|
9
|
Zago M, Kleiner AFR, Federolf PA. Editorial: Machine Learning Approaches to Human Movement Analysis. Front Bioeng Biotechnol 2021; 8:638793. [PMID: 33553133 PMCID: PMC7862562 DOI: 10.3389/fbioe.2020.638793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 12/23/2020] [Indexed: 12/16/2022] Open
Affiliation(s)
- Matteo Zago
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | | | | |
Collapse
|
10
|
López-Blanco R, Sorrentino Rodriguez A, Cubo E, Gabilondo Í, Ezpeleta D, Labrador-Espinosa MA, Sánchez-Ferro Á, Tejero C, Matarazzo M. Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology. Neurologia 2020; 38:S0213-4853(20)30429-1. [PMID: 33358062 DOI: 10.1016/j.nrl.2020.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/10/2020] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION New technologies (NT) are increasingly widespread in biomedicine. Using the consensus definition of NT established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN. MATERIAL AND METHODS We defined the concept of NT in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of NT, the field of neurology, and the geographical provenance of the studies. RESULTS We identified 299 communications related with NT from a total of 8,139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where NT were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to NT (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%). CONCLUSIONS The number of communications addressing NT follows an upward trend. The number of NT used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.
Collapse
Affiliation(s)
- R López-Blanco
- Servicio Integrado de Neurología. Hospital Universitario Rey Juan Carlos (Móstoles), Hospital General de Villalba, Hospital Universitario Infanta Elena (Valdemoro), Madrid, España
| | | | - E Cubo
- Hospital Universitario de Burgos, Burgos, España
| | - Í Gabilondo
- Hospital Universitario de Cruces, Barakaldo, España
| | - D Ezpeleta
- Hospital Universitario Quirónsalud Madrid, Pozuelo de Alarcón, Madrid, España
| | - M A Labrador-Espinosa
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Sevilla, España
| | - Á Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, España
| | - C Tejero
- Hospital Clínico Universitario Lozano Blesa, Zaragoza, España
| | - M Matarazzo
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, España; Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canadá.
| |
Collapse
|