1
|
Kim W, Vela EA, Kohles SS, Huayamave V, Gonzalez O. Validation of a Biomechanical Injury and Disease Assessment Platform Applying an Inertial-Based Biosensor and Axis Vector Computation. ELECTRONICS 2023; 12:3694. [PMID: 37974898 PMCID: PMC10653259 DOI: 10.3390/electronics12173694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg's flexion and extension knee movements and applied to a living subject's upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury.
Collapse
Affiliation(s)
- Wangdo Kim
- Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia—UTEC, Lima 15063, Peru
- Research Center in Bioengineering, Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia—UTEC, Lima 15063, Peru
| | - Emir A. Vela
- Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia—UTEC, Lima 15063, Peru
- Research Center in Bioengineering, Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia—UTEC, Lima 15063, Peru
| | - Sean S. Kohles
- Kohles Bioengineering, Cape Meares, OR 97141, USA
- Division of Biomaterials & Biomechanics, School of Dentistry, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Emergency Medicine, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Human Physiology and Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR 97403, USA
| | - Victor Huayamave
- Department of Mechanical Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
| | - Oscar Gonzalez
- Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia—UTEC, Lima 15063, Peru
| |
Collapse
|
2
|
Kulvicius T, Zhang D, Nielsen-Saines K, Bölte S, Kraft M, Einspieler C, Poustka L, Wörgötter F, Marschik PB. Infant movement classification through pressure distribution analysis. COMMUNICATIONS MEDICINE 2023; 3:112. [PMID: 37587165 PMCID: PMC10432534 DOI: 10.1038/s43856-023-00342-5] [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: 11/02/2022] [Accepted: 08/01/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we propose an innovative non-intrusive approach using a pressure sensing device to classify infant general movements. Here we differentiate typical general movement patterns of the "fidgety period" (fidgety movements) vs. the "pre-fidgety period" (writhing movements). METHODS Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4 to 16 weeks of post-term age. 1776 pressure data snippets, each 5 s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present or absent. Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks. RESULTS Here we show that the convolution neural network achieved the highest average classification accuracy (81.4%). By comparing the pros and cons of other methods aiming at automated general movement assessment to the pressure sensing approach, we infer that the proposed approach has a high potential for clinical applications. CONCLUSIONS We conclude that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.
Collapse
Affiliation(s)
- Tomas Kulvicius
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.
- Department for Computational Neuroscience, Third Institute of Physics-Biophysics, Georg-August-University of Göttingen, Göttingen, Germany.
| | - Dajie Zhang
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
- iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Karin Nielsen-Saines
- Division of Pediatric Infectious Diseases, David Geffen UCLA School of Medicine, Los Angeles, CA, USA
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Marc Kraft
- Department of Medical Engineering, Technical University Berlin, Berlin, Germany
| | - Christa Einspieler
- iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Luise Poustka
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
| | - Florentin Wörgötter
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
- Department of Medical Engineering, Technical University Berlin, Berlin, Germany
| | - Peter B Marschik
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz-ScienceCampus Primate Cognition, Göttingen, Germany
- iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
3
|
Vanmechelen I, Bekteshi S, Haberfehlner H, Feys H, Desloovere K, Aerts JM, Monbaliu E. Reliability and Discriminative Validity of Wearable Sensors for the Quantification of Upper Limb Movement Disorders in Individuals with Dyskinetic Cerebral Palsy. SENSORS (BASEL, SWITZERLAND) 2023; 23:1574. [PMID: 36772614 PMCID: PMC9921560 DOI: 10.3390/s23031574] [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: 12/07/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Background-Movement patterns in dyskinetic cerebral palsy (DCP) are characterized by abnormal postures and involuntary movements. Current evaluation tools in DCP are subjective and time-consuming. Sensors could yield objective information on pathological patterns in DCP, but their reliability has not yet been evaluated. The objectives of this study were to evaluate (i) reliability and (ii) discriminative ability of sensor parameters. Methods-Inertial measurement units were placed on the arm, forearm, and hand of individuals with and without DCP while performing reach-forward, reach-and-grasp-vertical, and reach-sideways tasks. Intra-class correlation coefficients (ICC) were calculated for reliability, and Mann-Whitney U-tests for between-group differences. Results-Twenty-two extremities of individuals with DCP (mean age 16.7 y) and twenty individuals without DCP (mean age 17.2 y) were evaluated. ICC values for all sensor parameters except jerk and sample entropy ranged from 0.50 to 0.98 during reach forwards/sideways and from 0.40 to 0.95 during reach-and-grasp vertical. Jerk and maximal acceleration/angular velocity were significantly higher for the DCP group in comparison with peers. Conclusions-This study was the first to assess the reliability of sensor parameters in individuals with DCP, reporting high between- and within-session reliability for the majority of the sensor parameters. These findings suggest that pathological movements of individuals with DCP can be reliably captured using a selection of sensor parameters.
Collapse
Affiliation(s)
- Inti Vanmechelen
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, 8200 Bruges, Belgium
| | - Saranda Bekteshi
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, 8200 Bruges, Belgium
| | - Helga Haberfehlner
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, 8200 Bruges, Belgium
- Department of Rehabilitation Medicine, Amsterdam Movement Sciences, Amsterdam UMC, 1081 HZ Amsterdam, The Netherlands
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, 3000 Leuven, Belgium
| | - Kaat Desloovere
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, 3212 Pellenberg, Belgium
| | - Jean-Marie Aerts
- Department of Biosystems, Measure, Model & Manage Bioresponses (M3-BIORES), Division of Animal and Human Health Engineering, KU Leuven, 3000 Leuven, Belgium
| | - Elegast Monbaliu
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, 8200 Bruges, Belgium
| |
Collapse
|
4
|
Vanmechelen I, Haberfehlner H, De Vleeschhauwer J, Van Wonterghem E, Feys H, Desloovere K, Aerts JM, Monbaliu E. Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review. Front Robot AI 2023; 9:1068413. [PMID: 36714804 PMCID: PMC9879015 DOI: 10.3389/frobt.2022.1068413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 01/10/2023] Open
Abstract
Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.
Collapse
Affiliation(s)
- Inti Vanmechelen
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,*Correspondence: Inti Vanmechelen,
| | - Helga Haberfehlner
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,Amsterdam Movement Sciences, Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam, Netherlands
| | - Joni De Vleeschhauwer
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Ellen Van Wonterghem
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Kaat Desloovere
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Pellenberg, Belgium
| | - Jean-Marie Aerts
- Division of Animal and Human Health Engineering, KU Leuven, Department of Biosystems, Measure, Model and Manage Bioresponses (M3-BIORES), Leuven, Belgium
| | - Elegast Monbaliu
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
| |
Collapse
|
5
|
Buisseret F, Dierick F, Van der Perre L. Wearable Sensors Applied in Movement Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8239. [PMID: 36365937 PMCID: PMC9658576 DOI: 10.3390/s22218239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
Recent advances in the miniaturization of electronics have resulted in sensors whose sizes and weights are such that they can be attached to living systems without interfering with their natural movements and behaviors [...].
Collapse
Affiliation(s)
- Fabien Buisseret
- Centre de Recherche, d’Étude et de Formation Continue de la Haute Ecole Louvain en Hainaut (CeREF Technique), Chaussée de Binche 159, 7000 Mons, Belgium
- Service de Physique Nucléaire et Subnucléaire, Research Institute for Complex Systems, UMONS Université de Mons, Place du Parc 20, 7000 Mons, Belgium
| | - Frédéric Dierick
- Centre de Recherche, d’Étude et de Formation Continue de la Haute Ecole Louvain en Hainaut (CeREF Technique), Chaussée de Binche 159, 7000 Mons, Belgium
- Centre National de Rééducation Fonctionnelle et de Réadaptation–Rehazenter, Laboratoire d’Analyse du Mouvement et de la Posture (LAMP), Rue André Vésale 1, 2674 Luxembourg, Luxembourg
- Faculté des Sciences de la Motricité, UCLouvain, Place Pierre de Coubertin 1-2, 1348 Ottignies-Louvain-la-Neuve, Belgium
| | | |
Collapse
|