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Liuzzi P, Carpinella I, Anastasi D, Gervasoni E, Lencioni T, Bertoni R, Carrozza MC, Cattaneo D, Ferrarin M, Mannini A. Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors. Sci Rep 2023; 13:8640. [PMID: 37244933 PMCID: PMC10224964 DOI: 10.1038/s41598-023-35744-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.
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Affiliation(s)
- Piergiuseppe Liuzzi
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
- Scuola Superiore Sant'Anna, Istituto di BioRobotica, 56025, Pontedera, Italy
| | - Ilaria Carpinella
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy.
| | - Denise Anastasi
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Elisa Gervasoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Tiziana Lencioni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Rita Bertoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | | | - Davide Cattaneo
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
- Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università di Milano, 20122, Milan, Italy
| | - Maurizio Ferrarin
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Andrea Mannini
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
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Mascia G, De Lazzari B, Camomilla V. Machine learning aided jump height estimate democratization through smartphone measures. Front Sports Act Living 2023; 5:1112739. [PMID: 36845828 PMCID: PMC9947475 DOI: 10.3389/fspor.2023.1112739] [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: 11/30/2022] [Accepted: 01/20/2023] [Indexed: 02/11/2023] Open
Abstract
Introduction The peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors. Methods For this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps - 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps - 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error. Results The multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features. Discussion The study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.
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Affiliation(s)
- Guido Mascia
- Department of Movement, Human and Health Science, University of Rome “Foro Italico”, Rome, Italy,Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, Rome, Italy
| | - Beatrice De Lazzari
- Department of Movement, Human and Health Science, University of Rome “Foro Italico”, Rome, Italy,Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, Rome, Italy
| | - Valentina Camomilla
- Department of Movement, Human and Health Science, University of Rome “Foro Italico”, Rome, Italy,Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, Rome, Italy,Correspondence: Valentina Camomilla,
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Carcreff L, Payen G, Grouvel G, Massé F, Armand S. Three-Dimensional Lower-Limb Kinematics from Accelerometers and Gyroscopes with Simple and Minimal Functional Calibration Tasks: Validation on Asymptomatic Participants. SENSORS 2022; 22:s22155657. [PMID: 35957218 PMCID: PMC9370908 DOI: 10.3390/s22155657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/15/2022] [Accepted: 07/26/2022] [Indexed: 02/04/2023]
Abstract
The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use.
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Affiliation(s)
- Lena Carcreff
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
- Nantes Université, Movement-Interactions-Performance, MIP, UR4334, F-44000 Nantes, France
- Correspondence:
| | - Gabriel Payen
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
- Gait Up SA, 1020 Renens, Switzerland; (G.P.); (F.M.)
| | - Gautier Grouvel
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
| | - Fabien Massé
- Gait Up SA, 1020 Renens, Switzerland; (G.P.); (F.M.)
| | - Stéphane Armand
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
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Mascia G, Brasiliano P, Di Feo P, Cereatti A, Camomilla V. A functional calibration protocol for ankle plantar-dorsiflexion estimate using magnetic and inertial measurement units: Repeatability and reliability assessment. J Biomech 2022; 141:111202. [PMID: 35751925 DOI: 10.1016/j.jbiomech.2022.111202] [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: 11/02/2021] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022]
Abstract
The ankle joint complex presents a tangled functional anatomy, which understanding is fundamental to effectively estimate its kinematics on the sagittal plane. Protocols based on the use of magnetic and inertial measurement units (MIMUs) currently do not take in due account this factor. To this aim, a joint coordinate system for the ankle joint complex is proposed, along with a protocol to perform its anatomical calibration using MIMUs, consisting in a combination of anatomical functional calibrations of the tibiotalar axis and static acquisitions. Protocol repeatability and reliability were tested according to the metrics proposed in Schwartz et al. (2004) involving three different operators performing the protocol three times on ten participants, undergoing instrumented gait analysis through both stereophotogrammetry and MIMUs. Instrumental reliability was evaluated comparing the MIMU-derived kinematic traces with the stereophotogrammetric ones, obtained with the same protocol, through the linear fit method. A total of 270 gait cycles were considered. Results showed that the protocol was repeatable and reliable for what concerned the operators (0.4 ± 0.4 deg and 0.8 ± 0.5 deg, respectively). Instrumental reliability analysis showed a mean RMSD of 3.0 ± 1.3 deg, a mean offset of 9.4 ± 8.4 deg and a mean linear relationship strength of R2 = 0.88 ± 0.08. With due caution, the protocol can be considered both repeatable and reliable. Further studies should pay attention to the other ankle degrees of freedom as well as on the angular convention to compute them.
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Affiliation(s)
- Guido Mascia
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy
| | - Paolo Brasiliano
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy
| | - Paolo Di Feo
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy
| | - Andrea Cereatti
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Electronics and Telecommunications, Polytechnic of Turin, Torino, Italy
| | - Valentina Camomilla
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Roma, Italy; Department of Human, Sports, and Health Science, University of Rome "Foro Italico", Roma, Italy.
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Preatoni E, Bergamini E, Fantozzi S, Giraud LI, Orejel Bustos AS, Vannozzi G, Camomilla V. The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:3225. [PMID: 35590914 PMCID: PMC9105988 DOI: 10.3390/s22093225] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 02/06/2023]
Abstract
Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.
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Affiliation(s)
- Ezio Preatoni
- Department for Health, University of Bath, Bath BA2 7AY, UK; (E.P.); (L.I.G.)
- Centre for Health and Injury and Illness Prevention in Sport, University of Bath, Bath BA2 7AY, UK
| | - Elena Bergamini
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
| | - Silvia Fantozzi
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;
- Health Sciences and Technologies—Interdepartmental Centre for Industrial Research, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
| | - Lucie I. Giraud
- Department for Health, University of Bath, Bath BA2 7AY, UK; (E.P.); (L.I.G.)
| | - Amaranta S. Orejel Bustos
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
| | - Giuseppe Vannozzi
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
| | - Valentina Camomilla
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy; (E.B.); (A.S.O.B.); (V.C.)
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
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