1
|
Bonato P, Feipel V, Corniani G, Arin-Bal G, Leardini A. Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience. Gait Posture 2024; 113:191-203. [PMID: 38917666 DOI: 10.1016/j.gaitpost.2024.06.007] [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: 01/24/2024] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA). RESEARCH QUESTION How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled? METHODS The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects. RESULTS FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA. SIGNIFICANCE We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
Collapse
Affiliation(s)
- Paolo Bonato
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Véronique Feipel
- Laboratory of Functional Anatomy, Faculty of Motor Sciences, Laboratory of Anatomy, Biomechanics and Organogenesis, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium
| | - Giulia Corniani
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Gamze Arin-Bal
- Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey; Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Alberto Leardini
- Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| |
Collapse
|
2
|
Wolff C, Steinheimer P, Warmerdam E, Dahmen T, Slusallek P, Schlinkmann C, Chen F, Orth M, Pohlemann T, Ganse B. Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study. J Med Internet Res 2024; 26:e44948. [PMID: 38718385 PMCID: PMC11112465 DOI: 10.2196/44948] [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: 01/24/2023] [Revised: 01/11/2024] [Accepted: 02/17/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. OBJECTIVE For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. METHODS In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: -20%, -15%, -10%, -5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. RESULTS Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. CONCLUSIONS While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.
Collapse
Affiliation(s)
- Christian Wolff
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Patrick Steinheimer
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany
| | - Elke Warmerdam
- Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany
| | - Tim Dahmen
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Philipp Slusallek
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | | | - Fei Chen
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Marcel Orth
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany
| | - Tim Pohlemann
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany
| | - Bergita Ganse
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany
- Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany
| |
Collapse
|
3
|
Popișter F, Dragomir M, Ciudin P, Goia HȘ. Empowering Rehabilitation: Design and Structural Analysis of a Low-Cost 3D-Printed Smart Orthosis. Polymers (Basel) 2024; 16:1303. [PMID: 38794496 PMCID: PMC11125049 DOI: 10.3390/polym16101303] [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: 04/16/2024] [Revised: 04/29/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024] Open
Abstract
Three-dimensional (3D) printing of polymer materials encompasses a wide range of applications and innovations. Polymer-based 3D printing, also known as additive manufacturing, has gained significant attention due to its versatility, cost-effectiveness, and potential to revolutionize various industries. The current paper focuses on obtaining a durable low-cost rehabilitation knee orthosis. Researchers propose that the entire structure should be obtained using modern equipment within the additive manufacturing domain-3D printing. The researchers focus on determining, through a 3D analysis of the entire 3D model assembly, which parts present a high degree of stress when a kinematic simulation is developed. The entire 3D model of the orthosis starts based on the result obtained from a 3D scanning of the knee joint of a patient, providing a precise fixation, and allowing for direct personalization. Based on the results and identification of the critical parts, there will be used different materials and a combination of 3D printing strategies to validate the physical model of the entire orthosis. For the manufacturing process, the researchers use two types of low-cost fused filament fabrication (FFF), which are easy to find on the worldwide market. The motivation for manufacturing the entire assembly using 3D printing techniques is the short time in which complex shapes can be obtained, which is relevant for the present study. The main purpose of the present research is to advance orthotic technology by developing an innovative knee brace made of 3D-printed polymers that are designed to be lightweight, easy-to-use, and provide comfort and functionality to patients during the rehabilitation process.
Collapse
Affiliation(s)
- Florin Popișter
- Department of Design Engineering and Robotics, Faculty of Industrial Engineering, Robotics and Production Management, Technical University of Cluj-Napoca, B-dul Muncii 103-105, 400641 Cluj-Napoca, Romania; (P.C.); (H.Ș.G.)
| | - Mihai Dragomir
- Department of Design Engineering and Robotics, Faculty of Industrial Engineering, Robotics and Production Management, Technical University of Cluj-Napoca, B-dul Muncii 103-105, 400641 Cluj-Napoca, Romania; (P.C.); (H.Ș.G.)
| | | | | |
Collapse
|
4
|
Warmerdam E, Wolff C, Orth M, Pohlemann T, Ganse B. Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures. Front Bioeng Biotechnol 2024; 12:1355254. [PMID: 38497053 PMCID: PMC10940326 DOI: 10.3389/fbioe.2024.1355254] [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: 12/13/2023] [Accepted: 02/15/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction: Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior. In this study, ground reaction force-based gait data of instrumented insoles from longitudinal intermittent laboratory assessments were compared to monitoring in daily life. Methods: Straight walking data of patients were collected during clinical visits and in between those visits the instrumented insoles recorded all stepping activities of the patients during daily life. Results: Out of 16 patients, due to technical and compliance issues, only six delivered sufficient datasets of about 12 weeks. Stance duration was longer (p = 0.004) and gait was more asymmetric during daily life (asymmetry of maximal force p < 0.001, loading slope p = 0.001, unloading slope p < 0.001, stance duration p < 0.001). Discussion: The differences between the laboratory assessments and the daily-life monitoring could be caused by a different and more diverse behavior during daily life. The daily life gait parameters significantly improved over time with union. One of the patients developed an infected non-union and showed worsening of force-related gait parameters, which was earlier detectable in the continuous daily life gait data compared to the lab data. Therefore, continuous gait monitoring in the daily life has potential to detect healing problems early on. Continuous monitoring with instrumented insoles has advantages once technical and compliance problems are solved.
Collapse
Affiliation(s)
- Elke Warmerdam
- Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| | - Christian Wolff
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Marcel Orth
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| | - Tim Pohlemann
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| | - Bergita Ganse
- Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg, Germany
- Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg, Germany
| |
Collapse
|
5
|
Häckel S, Kämpf T, Baur H, von Aesch A, Kressig RW, Stuck AE, Bastian JD. Assessing lower extremity loading during activities of daily living using continuous-scale physical functional performance 10 and wireless sensor insoles: a comparative study between younger and older adults. Eur J Trauma Emerg Surg 2023; 49:2521-2529. [PMID: 37480378 PMCID: PMC10728254 DOI: 10.1007/s00068-023-02331-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 07/11/2023] [Indexed: 07/24/2023]
Abstract
PURPOSE This study aims to investigate the lower extremity loading during activities of daily living (ADLs) using the Continuous Scale of Physical Functional Performance (CS-PFP 10) test and wireless sensor insoles in healthy volunteers. METHODS In this study, 42 participants were recruited, consisting of 21 healthy older adults (mean age 69.6 ± 4.6 years) and 21 younger healthy adults (mean age 23.6 ± 1.8 years). The performance of the subjects during ADLs was assessed using the CS-PFP 10 test, which comprised 10 tasks. The lower extremity loading was measured using wireless sensor insoles (OpenGo, Moticon, Munich, Germany) during the CS-PFP 10 test, which enabled the measurement of ground reaction forces, including the mean and maximum total forces during the stance phase, expressed in units of body weight (BW). RESULTS The total CS-PFP 10 score was significantly lower in older participants compared to the younger group (mean total score of 57.1 ± 9.0 compared to 78.2 ± 5.4, respectively). No significant differences in the mean total forces were found between older and young participants. The highest maximum total forces were observed during the tasks 'endurance walk' (young: 1.97 ± 0.34 BW, old: 1.70 ± 0.43 BW) and 'climbing stairs' (young: 1.65 ± 0.36 BW, old: 1.52 ± 0.28 BW). Only in the endurance walk, older participants showed a significantly higher maximum total force (p < 0.001). CONCLUSION The use of wireless sensor insoles in a laboratory setting can effectively measure the load on the lower extremities during ADLs. These findings could offer valuable insights for developing tailored recommendations for patients with partial weight-bearing restrictions.
Collapse
Affiliation(s)
- Sonja Häckel
- Department of Orthopaedic Surgery and Traumatology, Inselspital, University Hospital Bern, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
| | - Tobias Kämpf
- Department of Orthopaedic Surgery and Traumatology, Inselspital, University Hospital Bern, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Heiner Baur
- Department of Health Professions, Bern University of Applied Sciences, Murtenstrasse 10, Bern, Switzerland
- Physiotherapie SportClinic Zurich, Giesshübelstrasse 15, 8045, Zurich, Switzerland
| | - Arlene von Aesch
- Department of Health Professions, Bern University of Applied Sciences, Murtenstrasse 10, Bern, Switzerland
- Physiotherapie SportClinic Zurich, Giesshübelstrasse 15, 8045, Zurich, Switzerland
| | - Reto Werner Kressig
- University Department of Geriatric Medicine Felix Platter and University of Basel, Basel, Switzerland
| | - Andreas Ernst Stuck
- Department of Geriatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes Dominik Bastian
- Department of Orthopaedic Surgery and Traumatology, Inselspital, University Hospital Bern, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| |
Collapse
|
6
|
Kim S, Kim HS, Yoo J. Sarcopenia classification model for musculoskeletal patients using smart insole and artificial intelligence gait analysis. J Cachexia Sarcopenia Muscle 2023; 14:2793-2803. [PMID: 37884824 PMCID: PMC10751435 DOI: 10.1002/jcsm.13356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/23/2023] [Accepted: 09/19/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND The relationship between physical function, musculoskeletal disorders and sarcopenia is intricate. Current physical function tests, such as the gait speed test and the chair stand test, have limitations in eliminating subjective influences. To overcome this, smart devices utilizing inertial measurement unit sensors and artificial intelligence (AI)-based methods are being developed. METHODS We employed cutting-edge technologies, including the smart insole device and pose estimation based on AI, along with three classification models: random forest (RF), support vector machine and artificial neural network, to classify control and sarcopenia groups. Patient data of 83 individuals were divided into train and test sets, with approximately 67% allocated for training. Classification models were implemented using RStudio, considering individual and combined variables obtained through pose estimation and smart insole measurements. RESULTS Performance evaluation of the classification models utilized accuracy, precision, recall and F1-score indicators. Using only pose estimation variables, accuracy ranged from 0.92 to 0.96, with F1-scores of 0.94-0.97. Key variables identified by the RF model were 'Hip_dif', 'Ankle_dif' and 'Hipankle_dif'. Combining variables from both methods increased accuracy to 0.80-1.00, with F1-scores of 0.73-1.00. CONCLUSIONS In our study, a classification model that integrates smart insole and pose estimation technology was assessed. The RF model showed impressive results, particularly in the case of the Hip and Ankle variables. The growth of advanced measurement technologies suggests a promising avenue for identifying and utilizing additional digital biomarkers in the management of various disorders. The convergence of AI technologies with diagnostics and treatment approaches a promising future for enhanced interventions in conditions like sarcopenia.
Collapse
Affiliation(s)
- Shinjune Kim
- Department of Biomedical Research InstituteInha University HospitalIncheonSouth Korea
| | - Hyeon Su Kim
- Department of Biomedical Research InstituteInha University HospitalIncheonSouth Korea
| | - Jun‐Il Yoo
- Department of Orthopaedic SurgeryInha University HospitalIncheonSouth Korea
| |
Collapse
|
7
|
Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [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: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
Collapse
Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
| |
Collapse
|
8
|
Bailey CA, Mir-Orefice A, Uchida TK, Nantel J, Graham RB. Smartwatch-Based Prediction of Single-Stride and Stride-to-Stride Gait Outcomes Using Regression-Based Machine Learning. Ann Biomed Eng 2023; 51:2504-2517. [PMID: 37400746 DOI: 10.1007/s10439-023-03290-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/17/2023] [Indexed: 07/05/2023]
Abstract
Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7-11%; intraclass correlation coefficient (ICC2,1) 0.60-0.86], and SVM models were best for spatiotemporal variability (% error: 18-22%; ICC2,1 = 0.47-0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.
Collapse
Affiliation(s)
| | | | - Thomas K Uchida
- Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Ryan B Graham
- School of Human Kinetics, University of Ottawa, Ottawa, Canada.
| |
Collapse
|
9
|
Gothard AT, Hott JW, Anton SR. Dynamic Characterization of a Low-Cost Fully and Continuously 3D Printed Capacitive Pressure-Sensing System for Plantar Pressure Measurements. SENSORS (BASEL, SWITZERLAND) 2023; 23:8209. [PMID: 37837039 PMCID: PMC10575072 DOI: 10.3390/s23198209] [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: 07/11/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023]
Abstract
In orthopedics, the evaluation of footbed pressure distribution maps is a valuable gait analysis technique that aids physicians in diagnosing musculoskeletal and gait disorders. Recently, the use of pressure-sensing insoles to collect pressure distributions has become more popular due to the passive collection of natural gait data during daily activities and the reduction in physical strain experienced by patients. However, current pressure-sensing insoles face the limitations of low customizability and high cost. Previous works have shown the ability to construct customizable pressure-sensing insoles with capacitive sensors using fused-deposition modeling (FDM) 3D printing. This work explores the feasibility of low-cost fully and continuously 3D printed pressure sensors for pressure-sensing insoles using three sensor designs, which use flexible thermoplastic polyurethane (TPU) as the dielectric layer and either conductive TPU or conductive polylactic acid (PLA) for the conductive plates. The sensors are paired with a commercial capacitance-to-voltage converter board to form the sensing system. Dynamic sensor performance is evaluated via sinusoidal compressive tests at frequencies of 1, 3, 5, and 7 Hz, with pressure levels varying from 14.33 to 23.88, 33.43, 52.54, and 71.65 N/cm2 at each frequency. Five sensors of each type are tested. Results show that all sensors display significant hysteresis and nonlinearity. The PLA-TPU sensor with 10% infill is the best-performing sensor with the highest average sensitivity and lowest average hysteresis and linearity errors. The range of average sensitivities, hysteresis, and linearity errors across the entire span of tested pressures and frequencies for the PLA-TPU sensor with 10% infill is 11.61-20.11·10-4 V/(N/cm2), 11.9-31.8%, and 9.0-22.3%, respectively. The significant hysteresis and linearity error are due to the viscoelastic properties of TPU, and some additional nonlinear effects may be due to buckling of the infill walls of the dielectric.
Collapse
Affiliation(s)
| | | | - Steven R. Anton
- Dynamic and Smart Systems Laboratory, Department of Mechanical Engineering, Tennessee Technological University, Cookeville, TN 38505, USA; (A.T.G.); (J.W.H.)
| |
Collapse
|
10
|
Kim S, Park S, Lee S, Seo SH, Kim HS, Cha Y, Kim JT, Kim JW, Ha YC, Yoo JI. Assessing physical abilities of sarcopenia patients using gait analysis and smart insole for development of digital biomarker. Sci Rep 2023; 13:10602. [PMID: 37391464 PMCID: PMC10313812 DOI: 10.1038/s41598-023-37794-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/28/2023] [Indexed: 07/02/2023] Open
Abstract
The aim of this study is to compare variable importance across multiple measurement tools, and to use smart insole and artificial intelligence (AI) gait analysis to create variables that can evaluate the physical abilities of sarcopenia patients. By analyzing and comparing sarcopenia patients with non sarcopenia patients, this study aims to develop predictive and classification models for sarcopenia and discover digital biomarkers. The researchers used smart insole equipment to collect plantar pressure data from 83 patients, and a smart phone to collect video data for pose estimation. A Mann-Whitney U was conducted to compare the sarcopenia group of 23 patients and the control group of 60 patients. Smart insole and pose estimation were used to compare the physical abilities of sarcopenia patients with a control group. Analysis of joint point variables showed significant differences in 12 out of 15 variables, but not in knee mean, ankle range, and hip range. These findings suggest that digital biomarkers can be used to differentiate sarcopenia patients from the normal population with improved accuracy. This study compared musculoskeletal disorder patients to sarcopenia patients using smart insole and pose estimation. Multiple measurement methods are important for accurate sarcopenia diagnosis and digital technology has potential for improving diagnosis and treatment.
Collapse
Affiliation(s)
- Shinjune Kim
- Department of Biomedical Research Institute, Inha University Hospital, Incheon, Republic of Korea
| | - Seongjin Park
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju, Republic of Korea
| | - Sangyeob Lee
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju, Republic of Korea
| | - Sung Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju, Republic of Korea
| | - Hyeon Su Kim
- Department of Biomedical Research Institute, Inha University Hospital, Incheon, Republic of Korea
| | - Yonghan Cha
- Department of Orthopaedic Surgery, Daejeon Eulji Medical Center, Daejeon, Republic of Korea
| | - Jung-Taek Kim
- Department of Orthopedic Surgery, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jin-Woo Kim
- Department of Orthopaedic Surgery, Nowon Eulji Medical Center, Seoul, Republic of Korea
| | - Yong-Chan Ha
- Department of Orthopaedic Surgery, Bumin Medical Center, Seoul, Republic of Korea
| | - Jun-Il Yoo
- Department of Orthopedic Surgery, Inha University Hospital, 27, Inhang-ro, Jung-gu, Incheon, Republic of Korea.
| |
Collapse
|
11
|
Abou Ghaida H, Poffo L, Le Page R, Goujon JM. Effect of Sensor Size, Number and Position under the Foot to Measure the Center of Pressure (CoP) Displacement and Total Center of Pressure (CoPT) Using an Anatomical Foot Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:4848. [PMID: 37430761 DOI: 10.3390/s23104848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/07/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
Ambulatory instrumented insoles are widely used in real-time monitoring of the plantar pressure in order to calculate balance indicators such as Center of Pressure (CoP) or Pressure Maps. Such insoles include many pressure sensors; the required number and surface area of the sensors used are usually determined experimentally. Additionally, they follow the common plantar pressure zones, and the quality of measurement is usually strongly related to the number of sensors. In this paper, we experimentally investigate the robustness of an anatomical foot model, combined with a specific learning algorithm, to measure the static displacement of the center of pressure (CoP) and the center of total pressure (CoPT), as a function of the number, size, and position of sensors. Application of our algorithm to the pressure maps of nine healthy subjects shows that only three sensors per foot, with an area of about 1.5 × 1.5 cm2, are needed to give a good approximation of the CoP during quiet standing when placed on the main pressure areas.
Collapse
Affiliation(s)
- Hussein Abou Ghaida
- Univ Rennes, CNRS, Institut FOTON-UMR 6082, 6 rue de Kerampont CS 80518, F-22305 Lannion, France
| | - Luiz Poffo
- Univ Rennes, CNRS, Institut FOTON-UMR 6082, 6 rue de Kerampont CS 80518, F-22305 Lannion, France
| | - Ronan Le Page
- Univ Rennes, CNRS, Institut FOTON-UMR 6082, 6 rue de Kerampont CS 80518, F-22305 Lannion, France
| | - Jean-Marc Goujon
- Univ Rennes, CNRS, Institut FOTON-UMR 6082, 6 rue de Kerampont CS 80518, F-22305 Lannion, France
| |
Collapse
|
12
|
Lloyd DG, Saxby DJ, Pizzolato C, Worsey M, Diamond LE, Palipana D, Bourne M, de Sousa AC, Mannan MMN, Nasseri A, Perevoshchikova N, Maharaj J, Crossley C, Quinn A, Mulholland K, Collings T, Xia Z, Cornish B, Devaprakash D, Lenton G, Barrett RS. Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport 2023:S1440-2440(23)00070-1. [PMID: 37149408 DOI: 10.1016/j.jsams.2023.04.001] [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: 05/31/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries. DESIGN Narrative review. METHODS Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field. RESULTS Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions. CONCLUSIONS Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.
Collapse
Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Matthew Worsey
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Dinesh Palipana
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Medicine, Dentistry and Health, Griffith University, Australia
| | - Matthew Bourne
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Ana Cardoso de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Malik Muhammad Naeem Mannan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Nataliya Perevoshchikova
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Jayishni Maharaj
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claire Crossley
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Alastair Quinn
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Kyle Mulholland
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Tyler Collings
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Zhengliang Xia
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Bradley Cornish
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Daniel Devaprakash
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; VALD Performance, Australia
| | | | - Rodney S Barrett
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| |
Collapse
|
13
|
Kromołowska K, Kluza K, Kańtoch E, Sulikowski P. Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner's Feet. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23042323. [PMID: 36850921 PMCID: PMC9959378 DOI: 10.3390/s23042323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/12/2023]
Abstract
The objective of the research presented in this paper was to provide a novel open-source strain gauge system that shall enable the measurement of the pressure of a runner's feet on the ground and the presentation of the results of that measurement to the user. The system based on electronic shoe inserts with 16 built-in pressure sensors laminated in a transparent film was created, consisting of two parts: a mobile application and a wearable device. The developed system provides a number of advantages in comparison with existing solutions, including no need for calibration, an accurate and frequent measurement of pressure distribution, placement of electronics on the outside of a shoe, low cost, and an open-source approach to encourage enhancements and open collaboration.
Collapse
Affiliation(s)
- Klaudia Kromołowska
- AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland
- Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland
| | - Krzysztof Kluza
- AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland
| | - Eliasz Kańtoch
- AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland
| | - Piotr Sulikowski
- Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, ul. Żołnierska 49, 71-210 Szczecin, Poland
| |
Collapse
|
14
|
Jeong BO, Jeong SJ, Park K, Kim BH, Yim SV, Kim S. Effects of three-dimensional image based insole for healthy volunteers: a pilot clinical trial. Transl Clin Pharmacol 2023; 31:49-58. [PMID: 37034127 PMCID: PMC10079510 DOI: 10.12793/tcp.2023.31.e5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/03/2023] Open
Abstract
Insoles are used to treat various foot diseases, including plantar foot, diabetic foot ulcers, and refractory plantar fasciitis. In this study, we investigated the effects of 3-dimensional image-based (3-D) insole in healthy volunteers with no foot diseases. Additionally, the comfort of the 3-D insole was compared with that of a custom-molded insole. A single-center, randomized, open clinical trial was conducted to address the effectiveness of insole use in a healthy population with no foot or knee disease. Two types of arch support insoles were evaluated for their effectiveness: a 3-D insole and a custom-molded insole. Fifty Korean volunteers participated in the study and were randomly allocated into the "3-D insole" (n = 40) or "custom-molding insole" (n = 10) groups. All subjects wore 3-D insoles or custom-molded insoles for 2 weeks. The sense of wearing shoes (Visual Analog Scale [VAS] and score) and fatigue of the foot were used to assess the insole effects at the end of the 2-week study period. The 3-D insole groups showed significantly improved sense of wearing shoes (VAS, p = 0.0001; score, p = 0.0002) and foot fatigue (p = 0.0005) throughout the study period. Although the number of subjects was different, the custom-molding insole group showed no significant changes in the sense of wearing shoes (VAS, 0.1188; score, p = 0.1483). Foot fatigue in the 3-D insole group improved significantly (p = 0.0005), which shows that a 3-D insole might have favorable effects on foot health in a healthy population. Trial Registration Clinical Research Information Service Identifier: KCT0008100.
Collapse
Affiliation(s)
- Bi O Jeong
- Department of Orthopedic Surgery, Kyung Hee University Medical Center, Seoul 02447, Korea
| | - Su Jin Jeong
- Medical Science Research Institute, Kyung Hee University Medical Center, Seoul 02447, Korea
| | | | - Bo-Hyung Kim
- Department of Clinical Pharmacology and Therapeutics, Kyung Hee University Medical Center, Seoul 02447, Korea
- East-West Medical Research Institute, Kyung Hee University, Seoul 02447, Korea
| | - Sung-Vin Yim
- Department of Clinical Pharmacology and Therapeutics, Kyung Hee University Medical Center, Seoul 02447, Korea
| | - Sehyun Kim
- Graduate School of Dankook University, Yongin 16890, Korea
| |
Collapse
|
15
|
Wolff C, Steinheimer P, Warmerdam E, Dahmen T, Slusallek P, Schlinkmann C, Chen F, Orth M, Pohlemann T, Ganse B. Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle. Front Bioeng Biotechnol 2023; 11:1110099. [PMID: 36873371 PMCID: PMC9975497 DOI: 10.3389/fbioe.2023.1110099] [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: 11/28/2022] [Accepted: 02/03/2023] [Indexed: 02/17/2023] Open
Abstract
The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmeanload and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.
Collapse
Affiliation(s)
- Christian Wolff
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Patrick Steinheimer
- Department of Trauma, Hand and Reconstructive Surgery, Saarland University, Homburg, Germany
| | - Elke Warmerdam
- Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Saarland University, Homburg, Germany
| | - Tim Dahmen
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Philipp Slusallek
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | | | - Fei Chen
- German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
| | - Marcel Orth
- Department of Trauma, Hand and Reconstructive Surgery, Saarland University, Homburg, Germany
| | - Tim Pohlemann
- Department of Trauma, Hand and Reconstructive Surgery, Saarland University, Homburg, Germany
| | - Bergita Ganse
- Department of Trauma, Hand and Reconstructive Surgery, Saarland University, Homburg, Germany.,Werner Siemens-Endowed Chair for Innovative Implant Development (Fracture Healing), Saarland University, Homburg, Germany
| |
Collapse
|
16
|
Chatzaki C, Skaramagkas V, Kefalopoulou Z, Tachos N, Kostikis N, Kanellos F, Triantafyllou E, Chroni E, Fotiadis DI, Tsiknakis M. Can Gait Features Help in Differentiating Parkinson's Disease Medication States and Severity Levels? A Machine Learning Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249937. [PMID: 36560313 PMCID: PMC9787905 DOI: 10.3390/s22249937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 05/14/2023]
Abstract
Parkinson's disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.
Collapse
Affiliation(s)
- Chariklia Chatzaki
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
- Correspondence:
| | - Vasileios Skaramagkas
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
| | | | - Nikolaos Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
| | | | | | | | - Elisabeth Chroni
- Department of Neurology, Patras University Hospital, 26404 Patra, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
| | - Manolis Tsiknakis
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
| |
Collapse
|
17
|
Samarentsis AG, Makris G, Spinthaki S, Christodoulakis G, Tsiknakis M, Pantazis AK. A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:9725. [PMID: 36560095 PMCID: PMC9782173 DOI: 10.3390/s22249725] [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: 11/04/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19 MPa−1, a wide working pressure range (<872.4 kPa), excellent stability and durability (at least 2.280 cycles), great linearity (R2=0.993), fast response/recovery time (142−160 ms), low hysteresis (DH<10%) and the ability to support a broad spectrum of gait speeds (30−70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications.
Collapse
Affiliation(s)
- Anastasios G. Samarentsis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece
| | - Georgios Makris
- Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece
| | - Sofia Spinthaki
- Department of Physics, University of Crete, 70013 Heraklion, Greece
| | - Georgios Christodoulakis
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| | - Manolis Tsiknakis
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| | - Alexandros K. Pantazis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece
| |
Collapse
|
18
|
Gait Improvement by Alerted Push-Off via Heating of Insole Tip. Healthcare (Basel) 2022; 10:healthcare10122461. [PMID: 36553985 PMCID: PMC9777980 DOI: 10.3390/healthcare10122461] [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: 11/02/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
This study investigated the change in the joint angles of the lower limb during gait by heating the tip of the insole to make a conscious push-off with the warm part. Fifteen healthy males performed treadmill walking under three different conditions: CONTROL walked as usual, INST was instructed to extend the stride with a push-off from the ball of foot to the toe, and HEAT was asked to walk while attempting to push off the warm area, which was attached to the disposable warmer to the area from the ball of foot to the toe of the insole. A 3D-motion capture system with infrared cameras was used to analyze the gait. The hip joint angle increased significantly under the INST and HEAT. Although the ankle dorsi-flexion at heel strike did not differ significantly for these conditions, ankle plantar-flexion significantly increased at toe-off under the INST and HEAT. Especially, effect size (d) in increased plantar-flexion was large in HEAT (=1.50), whereas it was moderate in INST (=0.68). These results suggest that a heated stimulus during gait enhanced the consciousness of push-off and increased leg swing and ankle plantar-flexion during the terminal stance phase, which may increase the stride length.
Collapse
|
19
|
Parati M, Gallotta M, Muletti M, Pirola A, Bellafà A, De Maria B, Ferrante S. Validation of Pressure-Sensing Insoles in Patients with Parkinson's Disease during Overground Walking in Single and Cognitive Dual-Task Conditions. SENSORS (BASEL, SWITZERLAND) 2022; 22:6392. [PMID: 36080851 PMCID: PMC9460700 DOI: 10.3390/s22176392] [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: 06/30/2022] [Revised: 07/23/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
There is a need for unobtrusive and valid tools to collect gait parameters in patients with Parkinson's disease (PD). The novel promising tools are pressure-sensing insoles connected to a smartphone app; however, few studies investigated their measurement properties during simple or challenging conditions in PD patients. This study aimed to examine the validity and reliability of gait parameters computed by pressure-sensing insoles (FeetMe® insoles, Paris, France). Twenty-five PD patients (21 males, mean age: 69 (7) years) completed two walking assessment sessions. In each session, participants walked on an electronic pressure-sensitive walkway (GaitRite®, CIR System Inc., Franklin, NJ, USA) without other additional instructions (i.e., single-task condition) and while performing a concurrent cognitive task (i.e., dual-task condition). Spatiotemporal gait parameters were measured simultaneously using the pressure-sensing insoles and the electronic walkway. Concurrent validity was assessed by correlation coefficients and Bland-Altman methodology. Test-retest reliability was examined by intraclass correlation coefficients (ICC) and minimal detectable changes (MDC). The validity results showed moderate to excellent correlations and good agreement between the two systems. Concerning test-retest reliability, moderate-to-excellent ICC values and acceptable MDC demonstrated the repeatability of the measured gait parameters. Our findings support the use of these insoles as complementary instruments to conventional tools during single and dual-task conditions.
Collapse
Affiliation(s)
- Monica Parati
- Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, 20138 Milan, Italy
| | - Matteo Gallotta
- Istituti Clinici Scientifici Maugeri IRCCS, 20138 Milan, Italy
| | - Manuel Muletti
- Istituti Clinici Scientifici Maugeri IRCCS, 20138 Milan, Italy
| | - Annalisa Pirola
- Istituti Clinici Scientifici Maugeri IRCCS, 20138 Milan, Italy
| | - Alice Bellafà
- Istituti Clinici Scientifici Maugeri IRCCS, 20138 Milan, Italy
| | | | - Simona Ferrante
- Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
| |
Collapse
|
20
|
Subramaniam S, Faisal AI, Deen MJ. Wearable Sensor Systems for Fall Risk Assessment: A Review. Front Digit Health 2022; 4:921506. [PMID: 35911615 PMCID: PMC9329588 DOI: 10.3389/fdgth.2022.921506] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/22/2022] [Indexed: 01/14/2023] Open
Abstract
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.
Collapse
Affiliation(s)
| | - Abu Ilius Faisal
- Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - M. Jamal Deen
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada
- Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
- *Correspondence: M. Jamal Deen
| |
Collapse
|
21
|
Shabani S, Bourke AK, Muaremi A, Praestgaard J, O'Keeffe K, Argent R, Brom M, Scotti C, Caulfield B, Walsh LC. An Automatic Foot and Shank IMU Synchronization Algorithm: Proof-of-concept. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4210-4213. [PMID: 36083916 DOI: 10.1109/embc48229.2022.9871162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
When using wearable sensors for measurement and analysis of human performance, it is often necessary to integrate and synchronise data from separate sensor systems. This paper describes a synchronization technique between IMUs attached to the shanks and insoles attached at the feet and aims to solve the need to compute the ankle joint angle, which relies on synchronized sensor data. This will additionally enable concurrent analysis using gait kinematic and kinetic features. A proof-of-concept of the algorithm, which relies on cross-correlation of gyroscope sensor data from the shank and foot, to align the sensor systems is demonstrated. The algorithm output is validated against those signals synchronized using manually annotated heel-strike and toe-off ground-truth signal landmarks, identified in both the shank and feet signals using previously published definitions. Results demonstrate that the developed algorithm is capable of synchronizing both sensor systems, based on IMU data from both healthy participants and participants suffering from knee osteoarthritis, with a mean lag time bias of 25.56ms when compared to the ground truth. A proof-of-concept of technique to synchronise IMUs attached to the shanks and insoles attached at the feet is demonstrated and offers an alternative approach to sensor system synchronisation.
Collapse
|
22
|
Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males. SENSORS 2022; 22:s22093499. [PMID: 35591188 PMCID: PMC9100257 DOI: 10.3390/s22093499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/28/2022] [Accepted: 04/28/2022] [Indexed: 02/04/2023]
Abstract
Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.
Collapse
|
23
|
Effect of an Innovative Biofeedback Insole on Patient Rehabilitation after Total Knee Arthroplasty. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Partial weight bearing is fundamental to rehabilitation in the early stages following lower limb surgery. However, it remains debated as to how to properly achieve partial weight bearing while avoiding complications from excessive or premature load. Of the devices currently on the market, instrumented insoles coupled with force-sensitive resistors (FSRs) are among the best options in today’s clinical practice. Still, although several of these systems have been developed in the last few years, only some have been validated, leaving insufficient information on their application in rehabilitation after total knee replacement (TKR). To address this research gap, we evaluated the performance of an innovative biofeedback insole system featuring an extremely low response time for real-time force feedback. We randomly recruited 30 patients who underwent total knee arthroplasty. All patients used the new programmable insole for partial weight bearing per post-operative rehabilitation protocol. Our results confirm their inability to perform a correct gait with low partial weight bearing (<30–50% of their bodyweight). Partial weight bearing with a correct gait in the post-operative period is not obtainable without a measuring system. This new biofeedback insole is thus one of the most indicated and can improve rehabilitation compliance, therefore allowing continual patient monitoring for faster discharge and fast-track rehabilitation.
Collapse
|