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Castiglia SF, Trabassi D, Conte C, Gioiosa V, Sebastianelli G, Abagnale C, Ranavolo A, Di Lorenzo C, Coppola G, Casali C, Serrao M. Local Dynamic Stability of Trunk During Gait is Responsive to Rehabilitation in Subjects with Primary Degenerative Cerebellar Ataxia. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1478-1489. [PMID: 38279000 PMCID: PMC11269439 DOI: 10.1007/s12311-024-01663-4] [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] [Accepted: 01/19/2024] [Indexed: 01/28/2024]
Abstract
This study aimed to assess the responsiveness to the rehabilitation of three trunk acceleration-derived gait indexes, namely the harmonic ratio (HR), the short-term longest Lyapunov's exponent (sLLE), and the step-to-step coefficient of variation (CV), in a sample of subjects with primary degenerative cerebellar ataxia (swCA), and investigate the correlations between their improvements (∆), clinical characteristics, and spatio-temporal and kinematic gait features. The trunk acceleration patterns in the antero-posterior (AP), medio-lateral (ML), and vertical (V) directions during gait of 21 swCA were recorded using a magneto-inertial measurement unit placed at the lower back before (T0) and after (T1) a period of inpatient rehabilitation. For comparison, a sample of 21 age- and gait speed-matched healthy subjects (HSmatched) was also included. At T1, sLLE in the AP (sLLEAP) and ML (sLLEML) directions significantly improved with moderate to large effect sizes, as well as SARA scores, stride length, and pelvic rotation. sLLEML and pelvic rotation also approached the HSmatched values at T1, suggesting a normalization of the parameter. HRs and CV did not significantly modify after rehabilitation. ∆sLLEML correlated with ∆ of the gait subscore of the SARA scale (SARAGAIT) and ∆stride length and ∆sLLEAP correlated with ∆pelvic rotation and ∆SARAGAIT. The minimal clinically important differences for sLLEML and sLLEAP were ≥ 36.16% and ≥ 28.19%, respectively, as the minimal score reflects a clinical improvement in SARA scores. When using inertial measurement units, sLLEAP and sLLEML can be considered responsive outcome measures for assessing the effectiveness of rehabilitation on trunk stability during walking in swCA.
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Affiliation(s)
- Stefano Filippo Castiglia
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy.
- Department of Brain and Behavioral Sciences, University of Pavia, 27100, Pavia, Italy.
| | - Dante Trabassi
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Carmela Conte
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Valeria Gioiosa
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Gabriele Sebastianelli
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Chiara Abagnale
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Alberto Ranavolo
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Via Fontana Candida, 1, Monte Porzio Catone, 00078, Rome, Italy
| | - Cherubino Di Lorenzo
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Carlo Casali
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
| | - Mariano Serrao
- Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso Della Repubblica 79, 04100, Latina, Italy
- Movement Analysis Laboratory, Policlinico Italia, Piazza del Campidano, 6, 00162, Rome, Italy
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Trabassi D, Castiglia SF, Bini F, Marinozzi F, Ajoudani A, Lorenzini M, Chini G, Varrecchia T, Ranavolo A, De Icco R, Casali C, Serrao M. Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia. SENSORS (BASEL, SWITZERLAND) 2024; 24:3613. [PMID: 38894404 PMCID: PMC11175240 DOI: 10.3390/s24113613] [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: 05/09/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.
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Affiliation(s)
- Dante Trabassi
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
| | - Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (F.B.); (F.M.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (F.B.); (F.M.)
| | - Arash Ajoudani
- Department of Advanced Robotics, Italian Institute of Technology, 16163 Genoa, Italy; (A.A.); (M.L.)
| | - Marta Lorenzini
- Department of Advanced Robotics, Italian Institute of Technology, 16163 Genoa, Italy; (A.A.); (M.L.)
| | - Giorgia Chini
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; (G.C.); (T.V.); (A.R.)
| | - Tiwana Varrecchia
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; (G.C.); (T.V.); (A.R.)
| | - Alberto Ranavolo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; (G.C.); (T.V.); (A.R.)
| | - Roberto De Icco
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy;
- Headache Science & Neurorehabilitation Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Carlo Casali
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
- Movement Analysis Laboratory, Policlinico Italia, 00162 Rome, Italy
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Tramontano M, Orejel Bustos AS, Montemurro R, Vasta S, Marangon G, Belluscio V, Morone G, Modugno N, Buzzi MG, Formisano R, Bergamini E, Vannozzi G. Dynamic Stability, Symmetry, and Smoothness of Gait in People with Neurological Health Conditions. SENSORS (BASEL, SWITZERLAND) 2024; 24:2451. [PMID: 38676068 PMCID: PMC11053882 DOI: 10.3390/s24082451] [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: 03/02/2024] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024]
Abstract
Neurological disorders such as stroke, Parkinson's disease (PD), and severe traumatic brain injury (sTBI) are leading global causes of disability and mortality. This study aimed to assess the ability to walk of patients with sTBI, stroke, and PD, identifying the differences in dynamic postural stability, symmetry, and smoothness during various dynamic motor tasks. Sixty people with neurological disorders and 20 healthy participants were recruited. Inertial measurement unit (IMU) sensors were employed to measure spatiotemporal parameters and gait quality indices during different motor tasks. The Mini-BESTest, Berg Balance Scale, and Dynamic Gait Index Scoring were also used to evaluate balance and gait. People with stroke exhibited the most compromised biomechanical patterns, with lower walking speed, increased stride duration, and decreased stride frequency. They also showed higher upper body instability and greater variability in gait stability indices, as well as less gait symmetry and smoothness. PD and sTBI patients displayed significantly different temporal parameters and differences in stability parameters only at the pelvis level and in the smoothness index during both linear and curved paths. This study provides a biomechanical characterization of dynamic stability, symmetry, and smoothness in people with stroke, sTBI, and PD using an IMU-based ecological assessment.
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Affiliation(s)
- Marco Tramontano
- Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, 40138 Bologna, Italy;
- Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Amaranta Soledad Orejel Bustos
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro de Bosis, 00135 Roma, Italy;
| | - Rebecca Montemurro
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
| | - Simona Vasta
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
| | - Gabriele Marangon
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, 66100 Chieti, Italy
| | - Valeria Belluscio
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro de Bosis, 00135 Roma, Italy;
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
- San Raffaele Institute of Sulmona, 67039 Sulmona, Italy
| | | | - Maria Gabriella Buzzi
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
| | - Rita Formisano
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
| | - Elena Bergamini
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro de Bosis, 00135 Roma, Italy;
- Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7b, 24044 Dalmine, BG, Italy
| | - Giuseppe Vannozzi
- Santa Lucia Foundation IRCCS (Institute for Research and Health Care), 00179 Rome, Italy; (A.S.O.B.); (V.B.); (M.G.B.); (R.F.)
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro de Bosis, 00135 Roma, Italy;
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Manto M, Serrao M, Filippo Castiglia S, Timmann D, Tzvi-Minker E, Pan MK, Kuo SH, Ugawa Y. Neurophysiology of cerebellar ataxias and gait disorders. Clin Neurophysiol Pract 2023; 8:143-160. [PMID: 37593693 PMCID: PMC10429746 DOI: 10.1016/j.cnp.2023.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/19/2023] [Accepted: 07/11/2023] [Indexed: 08/19/2023] Open
Abstract
There are numerous forms of cerebellar disorders from sporadic to genetic diseases. The aim of this chapter is to provide an overview of the advances and emerging techniques during these last 2 decades in the neurophysiological tests useful in cerebellar patients for clinical and research purposes. Clinically, patients exhibit various combinations of a vestibulocerebellar syndrome, a cerebellar cognitive affective syndrome and a cerebellar motor syndrome which will be discussed throughout this chapter. Cerebellar patients show abnormal Bereitschaftpotentials (BPs) and mismatch negativity. Cerebellar EEG is now being applied in cerebellar disorders to unravel impaired electrophysiological patterns associated within disorders of the cerebellar cortex. Eyeblink conditioning is significantly impaired in cerebellar disorders: the ability to acquire conditioned eyeblink responses is reduced in hereditary ataxias, in cerebellar stroke and after tumor surgery of the cerebellum. Furthermore, impaired eyeblink conditioning is an early marker of cerebellar degenerative disease. General rules of motor control suggest that optimal strategies are needed to execute voluntary movements in the complex environment of daily life. A high degree of adaptability is required for learning procedures underlying motor control as sensorimotor adaptation is essential to perform accurate goal-directed movements. Cerebellar patients show impairments during online visuomotor adaptation tasks. Cerebellum-motor cortex inhibition (CBI) is a neurophysiological biomarker showing an inverse association between cerebellothalamocortical tract integrity and ataxia severity. Ataxic gait is characterized by increased step width, reduced ankle joint range of motion, increased gait variability, lack of intra-limb inter-joint and inter-segmental coordination, impaired foot ground placement and loss of trunk control. Taken together, these techniques provide a neurophysiological framework for a better appraisal of cerebellar disorders.
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Affiliation(s)
- Mario Manto
- Service des Neurosciences, Université de Mons, Mons, Belgium
- Service de Neurologie, CHU-Charleroi, Charleroi, Belgium
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, University of Rome Sapienza, Polo Pontino, Corso della Repubblica 79 04100, Latina, Italy
- Gait Analysis LAB Policlinico Italia, Via Del Campidano 6 00162, Rome, Italy
| | - Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, University of Rome Sapienza, Polo Pontino, Corso della Repubblica 79 04100, Latina, Italy
- Gait Analysis LAB Policlinico Italia, Via Del Campidano 6 00162, Rome, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy
| | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Elinor Tzvi-Minker
- Department of Neurology, University of Leipzig, Liebigstraße 20, 04103 Leipzig, Germany
- Syte Institute, Hamburg, Germany
| | - Ming-Kai Pan
- Cerebellar Research Center, National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin 64041, Taiwan
- Department and Graduate Institute of Pharmacology, National Taiwan University College of Medicine, Taipei 10051, Taiwan
- Department of Medical Research, National Taiwan University Hospital, Taipei 10002, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei City 11529, Taiwan
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA
| | - Sheng-Han Kuo
- Institute of Biomedical Sciences, Academia Sinica, Taipei City 11529, Taiwan
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, Fukushima Medical University, Fukushima, Japan
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Wang H, Hu B, Huang J, Chen L, Yuan M, Tian X, Shi T, Zhao J, Huang W. Predicting the fatigue in Parkinson's disease using inertial sensor gait data and clinical characteristics. Front Neurol 2023; 14:1172320. [PMID: 37388552 PMCID: PMC10303817 DOI: 10.3389/fneur.2023.1172320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives The study aimed to analyze the clinical features and gait characteristics of patients with Parkinson's disease (PD) who also suffer from fatigue and to develop a model that can help identify fatigue states in the early stages of PD. Methodology A total of 81 PD patients have been enrolled for the Parkinson's Fatigue Scale (PFS-16) assessment and divided into two groups: patients with or without fatigue. Neuropsychological assessments of the two groups, including motor and non-motor symptoms, were collected. The patient's gait characteristics were collected using a wearable inertial sensor device. Results PD patients who experienced fatigue had a more significant impairment of motor symptoms than those who did not, and the experience of fatigue became more pronounced as the disease progressed. Patients with fatigue had more significant mood disorders and sleep disturbances, which can lead to a poorer quality of life. PD patients with fatigue had shorter step lengths, lower velocity, and stride length and increased stride length variability. As for kinematic parameters, PD patients with fatigue had lower shank-forward swing max, trunk-max sagittal angular velocity, and lumbar-max coronal angular velocity than PD patients without fatigue. The binary logistic analysis found that Movement Disorder Society-Unified Parkinson's Disease Rating Scale-I (MDS-UPDRS-I) scores, Hamilton Depression Scale (HAMD) scores, and stride length variability independently predicted fatigue in PD patients. The area under the curve (AUC) of these selected factors in the receiver operating characteristic (ROC) analysis was 0.900. Moreover, HAMD might completely mediate the association between Hamilton Anxiety Scale (HAMA) scores and fatigue (indirect effect: β = 0.032, 95% confidence interval: 0.001-0.062), with a percentage of mediation of 55.46%. Conclusion Combining clinical characteristics and gait cycle parameters, including MDS-UPDRS-I scores, HAMD scores, and stride length variability, can identify PD patients with a high fatigue risk.
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Affiliation(s)
- Hui Wang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Binbin Hu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Juan Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lin Chen
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Min Yuan
- Department of Neurology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Xingfu Tian
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Shi
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiahao Zhao
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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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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Castiglia SF, Trabassi D, Conte C, Ranavolo A, Coppola G, Sebastianelli G, Abagnale C, Barone F, Bighiani F, De Icco R, Tassorelli C, Serrao M. Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:4983. [PMID: 37430896 DOI: 10.3390/s23104983] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/14/2023] [Accepted: 05/16/2023] [Indexed: 07/12/2023]
Abstract
The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.
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Affiliation(s)
- Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, 00078 Monte Porzio Catone, Italy
| | - Dante Trabassi
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Carmela Conte
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Alberto Ranavolo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Gianluca Coppola
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Gabriele Sebastianelli
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Chiara Abagnale
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Francesca Barone
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Federico Bighiani
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Movement Analysis Research Unit, IRCSS Mondino Foundation, 27100 Pavia, Italy
| | - Roberto De Icco
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Movement Analysis Research Unit, IRCSS Mondino Foundation, 27100 Pavia, Italy
| | - Cristina Tassorelli
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Movement Analysis Research Unit, IRCSS Mondino Foundation, 27100 Pavia, Italy
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
- Movement Analysis Laboratory, Policlinico Italia, 00162 Rome, Italy
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8
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Russo M, Amboni M, Barone P, Pellecchia MT, Romano M, Ricciardi C, Amato F. Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:1985. [PMID: 36850582 PMCID: PMC9963713 DOI: 10.3390/s23041985] [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: 12/09/2022] [Revised: 01/04/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson's Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial-temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.
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Affiliation(s)
- Michela Russo
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Marianna Amboni
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, 84081 Baronissi, Italy
- IDC Hermitage Capodimonte, 80133 Naples, Italy
| | - Paolo Barone
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, 84081 Baronissi, Italy
| | - Maria Teresa Pellecchia
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, 84081 Baronissi, Italy
| | - Maria Romano
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Francesco Amato
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
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9
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Castiglia SF, Trabassi D, Tatarelli A, Ranavolo A, Varrecchia T, Fiori L, Di Lenola D, Cioffi E, Raju M, Coppola G, Caliandro P, Casali C, Serrao M. Identification of Gait Unbalance and Fallers Among Subjects with Cerebellar Ataxia by a Set of Trunk Acceleration-Derived Indices of Gait. CEREBELLUM (LONDON, ENGLAND) 2023; 22:46-58. [PMID: 35079958 DOI: 10.1007/s12311-021-01361-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 02/01/2023]
Abstract
This study aimed to assess the ability of 25 gait indices to characterize gait instability and recurrent fallers among persons with primary degenerative cerebellar ataxia (pwCA), regardless of gait speed, and investigate their correlation with clinical and kinematic variables. Trunk acceleration patterns were acquired during the gait of 34 pwCA, and 34 age- and speed-matched healthy subjects (HSmatched) using an inertial measurement unit. We calculated harmonic ratios (HR), percent recurrence, percent determinism, step length coefficient of variation, short-time largest Lyapunov exponent (sLLE), normalized jerk score, log-dimensionless jerk (LDLJ-A), root mean square (RMS), and root mean square ratio of accelerations (RMSR) in each spatial direction for each participant. Unpaired t-tests or Mann-Whitney tests were performed to identify significant differences between the pwCA and HSmatched groups. Receiver operating characteristics were plotted to assess the ability to characterize gait alterations in pwCA and fallers. Optimal cutoff points were identified, and post-test probabilities were calculated. The HRs showed to characterize gait instability and pwCA fallers with high probabilities. They were correlated with disease severity and stance, swing, and double support duration, regardless of gait speed. sLLEs, RMSs, RMSRs, and LDLJ-A were slightly able to characterize the gait of pwCA but failed to characterize fallers.
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Affiliation(s)
- Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy.
| | - Dante Trabassi
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy
| | - Antonella Tatarelli
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, via Fontana Candida, 1, Monte Porzio Catone, 00078, Rome, Italy.,Department of Human Neurosciences, Sapienza University of Rome, viale dell'Università 30, 00185, Rome, Italy
| | - Alberto Ranavolo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, via Fontana Candida, 1, Monte Porzio Catone, 00078, Rome, Italy
| | - Tiwana Varrecchia
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, via Fontana Candida, 1, Monte Porzio Catone, 00078, Rome, Italy
| | - Lorenzo Fiori
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, via Fontana Candida, 1, Monte Porzio Catone, 00078, Rome, Italy.,Department of Physiology and Pharmacology, Sapienza University of Rome, piazzale Aldo Moro, 5, 00185, Rome, Italy
| | - Davide Di Lenola
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy
| | - Ettore Cioffi
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy.,Department of Human Neurosciences, Sapienza University of Rome, viale dell'Università 30, 00185, Rome, Italy
| | - Manikandan Raju
- Department of Human Neurosciences, Sapienza University of Rome, viale dell'Università 30, 00185, Rome, Italy
| | - Gianluca Coppola
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy
| | - Pietro Caliandro
- Unità Operativa Complessa Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli, 8, 00168, Rome, Italy
| | - Carlo Casali
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome-Polo Pontino, Corso della Repubblica 79, 04100, Latina, Italy.,Movement Analysis Laboratory, Policlinico Italia, Piazza del Campidano, 6, 00162, Rome, Italy
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10
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Troisi Lopez E, Sorrentino P, Liparoti M, Minino R, Polverino A, Romano A, Carotenuto A, Amico E, Sorrentino G. The kinectome: A comprehensive kinematic map of human motion in health and disease. Ann N Y Acad Sci 2022; 1516:247-261. [PMID: 35838306 PMCID: PMC9796708 DOI: 10.1111/nyas.14860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson's disease, observing that the patients' kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of frameworks.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | | | - Marianna Liparoti
- Department of Developmental and Social PsychologyUniversity “La Sapienza” of RomeRomeItaly
| | - Roberta Minino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Arianna Polverino
- Institute for Diagnosis and TreatmentHermitage CapodimonteNaplesItaly
| | - Antonella Romano
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Anna Carotenuto
- Alzheimer Unit and Movement Disorders ClinicDepartment of NeurologyCardarelli HospitalNaplesItaly
| | - Enrico Amico
- Institute of Bioengineering, Center for NeuroprostheticsEPFLGenevaSwitzerland,Department of Radiology and Medical InformaticsUniversity of Geneva (UNIGE)GenevaSwitzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and TreatmentHermitage CapodimonteNaplesItaly,Institute of Applied Sciences and Intelligent SystemsCNRPozzuoliItaly
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11
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Castiglia SF, Trabassi D, De Icco R, Tatarelli A, Avenali M, Corrado M, Grillo V, Coppola G, Denaro A, Tassorelli C, Serrao M. Harmonic ratio is the most responsive trunk-acceleration derived gait index to rehabilitation in people with Parkinson's disease at moderate disease stages. Gait Posture 2022; 97:152-158. [PMID: 35961132 DOI: 10.1016/j.gaitpost.2022.07.235] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Harmonic ratios (HRs), recurrence quantification analysis in the antero-posterior direction (RQAdetAP), and stride length coefficient of variation (CV) have recently been shown to characterize gait abnormalities and fall risk in people with Parkinson's disease (pwPD) at moderate disease stages. RESEARCH QUESTION This study aimed to i) assess the internal and external responsiveness to rehabilitation of HR, RQAdetAP, and CV, ii) identify the baseline predictors of normalization of the gait stability indexes, and iii) investigate the correlations between the gait indexes modifications (∆) and clinical and kinematic ∆s in pwPD at Hoehn and Yahr disease staging classification 3. METHODS The trunk acceleration patterns of 21 pwPD and 21 age- and speed-matched healthy subjects (HSmatched) were acquired during gait using an inertial measurement unit at baseline (T0). pwPD were also assessed after a 4-week rehabilitation period (T1). Each participant's HR in the antero-posterior (HRAP), medio-lateral (HRML), and vertical directions, RQAdetAP, CV, spatio-temporal, and kinematic variables were calculated. RESULTS At T1, HRAP and HRML improved to normative values and showed high internal and external responsiveness. Lower HRs and higher pelvic rotation values at baseline were predictors of ∆HRs. A minimal clinically important difference (MCID) ≥ 21.5 % is required to normalize HRAP with 95 % probability. MCID ≥ 36.9 % is required to normalize HRML with 92 % probability. ∆HRAP correlated with ∆HRML and both correlated with ∆stride length and ∆pelvic rotation, regardless of ∆gait speed. RQAdetAP and step length CV were not responsive to rehabilitation. SIGNIFICANCE When using inertial measurement units, HRAP and HRML can be considered as responsive outcome measures for assessing the effectiveness of rehabilitation on trunk smoothness during walking in pwPD at moderate disease stages.
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Affiliation(s)
- Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, Corso della Repubblica 70, 04100 Latina, Italy; Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy.
| | - Dante Trabassi
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, Corso della Repubblica 70, 04100 Latina, Italy
| | - Roberto De Icco
- Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy; Movement Analysis Research Unit, IRCCS Mondino Foundation, via Mondino, 2, 27100 Pavia, Italy
| | - Antonella Tatarelli
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, via Fontana Candida, 1, Monte Porzio Catone, 00078 Rome, Italy; Department of Human Neurosciences, "Sapienza" University of Rome, viale dell'Università, 30, 00185 Rome, Italy
| | - Micol Avenali
- Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy; Movement Analysis Research Unit, IRCCS Mondino Foundation, via Mondino, 2, 27100 Pavia, Italy
| | - Michele Corrado
- Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy; Movement Analysis Research Unit, IRCCS Mondino Foundation, via Mondino, 2, 27100 Pavia, Italy
| | - Valentina Grillo
- Movement Analysis Research Unit, IRCCS Mondino Foundation, via Mondino, 2, 27100 Pavia, Italy
| | - Gianluca Coppola
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, Corso della Repubblica 70, 04100 Latina, Italy
| | - Alessandro Denaro
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, Corso della Repubblica 70, 04100 Latina, Italy
| | - Cristina Tassorelli
- Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy; Movement Analysis Research Unit, IRCCS Mondino Foundation, via Mondino, 2, 27100 Pavia, Italy
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, Corso della Repubblica 70, 04100 Latina, Italy; Movement Analysis Laboratory, Policlinico Italia, piazza del campidano, 6, 00162 Rome, Italy
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12
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Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis. SENSORS 2022; 22:s22103700. [PMID: 35632109 PMCID: PMC9148133 DOI: 10.3390/s22103700] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.
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13
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Troisi Lopez E, Minino R, Sorrentino P, Manzo V, Tafuri D, Sorrentino G, Liparoti M. Sensitivity to gait improvement after levodopa intake in Parkinson's disease: A comparison study among synthetic kinematic indices. PLoS One 2022; 17:e0268392. [PMID: 35551300 PMCID: PMC9098031 DOI: 10.1371/journal.pone.0268392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 04/28/2022] [Indexed: 02/07/2023] Open
Abstract
The synthetic indices are widely used to describe balance and stability during gait. Some of these are employed to describe the gait features in Parkinson's disease (PD). However, the results are sometimes inconsistent, and the same indices are rarely used to compare the individuals affected by PD before and after levodopa intake (OFF and ON condition, respectively). Our aim was to investigate which synthetic measure among Harmonic Ratio, Jerk Ratio, Golden Ratio and Trunk Displacement Index is representative of gait stability and harmony, and which of these are more sensitive to the variations between OFF and ON condition. We found that all indices, except the Jerk Ratio, significantly improve after levodopa. Only the improvement of the Trunk Displacement Index showed a direct correlation with the motor improvement measured through the clinical scale UPDRS-III (Unified Parkinson's Disease Rating Scale-part III). In conclusion, we suggest that the synthetic indices can be useful to detect motor changes induced by, but not all of them clearly correlate with the clinical changes achieved with the levodopa administration. In our analysis, only the Trunk Displacement Index was able to show a clear relationship with the PD clinical motor improvement.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Pierpaolo Sorrentino
- Institut de Neuroscience des Systemès, Aix-Marseille University, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli (NA), Italy
| | - Valentino Manzo
- Alzheimer Unit and Movement Disorders Clinic, Department of Neurology, Cardarelli Hospital, Naples, Italy
| | - Domenico Tafuri
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli (NA), Italy
- Institute for Diagnosis and Care, Hermitage Capodimonte, Naples, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
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14
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Bisi MC, Di Marco R, Ragona F, Darra F, Vecchi M, Masiero S, Del Felice A, Stagni R. Quantitative Characterization of Motor Control during Gait in Dravet Syndrome Using Wearable Sensors: A Preliminary Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:2140. [PMID: 35336311 PMCID: PMC8952819 DOI: 10.3390/s22062140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/26/2022] [Accepted: 03/08/2022] [Indexed: 01/27/2023]
Abstract
Dravet syndrome (DS) is a rare and severe form of genetic epilepsy characterized by cognitive and behavioural impairments and progressive gait deterioration. The characterization of gait parameters in DS needs efficient, non-invasive quantification. The aim of the present study is to apply nonlinear indexes calculated from inertial measurements to describe the dynamics of DS gait. Twenty participants (7 M, age 9-33 years) diagnosed with DS were enrolled. Three wearable inertial measurement units (OPAL, Apdm, Portland, OR, USA; Miniwave, Cometa s.r.l., Italy) were attached to the lower back and ankles and 3D acceleration and angular velocity were acquired while participants walked back and forth along a straight path. Segmental kinematics were acquired by means of stereophotogrammetry (SMART, BTS). Community functioning data were collected using the functional independence measure (FIM). Mean velocity and step width were calculated from stereophotogrammetric data; fundamental frequency, harmonic ratio, recurrence quantification analysis, and multiscale entropy (τ = 1...6) indexes along anteroposterior (AP), mediolateral (ML), and vertical (V) axes were calculated from trunk acceleration. Results were compared to a reference age-matched control group (112 subjects, 6-25 years old). All nonlinear indexes show a disruption of the cyclic pattern of the centre of mass in the sagittal plane, quantitatively supporting the clinical observation of ataxic gait. Indexes in the ML direction were less altered, suggesting the efficacy of the compensatory strategy (widening the base of support). Nonlinear indexes correlated significantly with functional scores (i.e., FIM and speed), confirming their effectiveness in capturing clinically meaningful biomarkers of gait.
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Affiliation(s)
- Maria Cristina Bisi
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale del Risorgimento, 2, 40136 Bologna, Italy; (M.C.B.); (R.S.)
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research, Via Tolara di Sopra, 50, Ozzano dell’Emilia, 40064 Bologna, Italy
| | - Roberto Di Marco
- Department of Neuroscienc, University of Padova, Via Belzoni 160, 35121 Padova, Italy; (R.D.M.); (S.M.)
| | - Francesca Ragona
- Department of Paediatric Neuroscience, Euroepan Reference Network EpiCARE, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133 Milano, Italy;
| | - Francesca Darra
- Pediatric Neurology, University Hospital of Verona, P.Le Stefani, 1, 37121 Verona, Italy;
| | - Marilena Vecchi
- Department of Women and Children Health, University of Padova, Via Nicolò Giustiniani, 3, 35128 Padova, Italy;
| | - Stefano Masiero
- Department of Neuroscienc, University of Padova, Via Belzoni 160, 35121 Padova, Italy; (R.D.M.); (S.M.)
- Padova Neuroscience Centre, University of Padova, Via Giuseppe Orus, 2, 35131 Padova, Italy
| | - Alessandra Del Felice
- Department of Neuroscienc, University of Padova, Via Belzoni 160, 35121 Padova, Italy; (R.D.M.); (S.M.)
- Padova Neuroscience Centre, University of Padova, Via Giuseppe Orus, 2, 35131 Padova, Italy
| | - Rita Stagni
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale del Risorgimento, 2, 40136 Bologna, Italy; (M.C.B.); (R.S.)
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research, Via Tolara di Sopra, 50, Ozzano dell’Emilia, 40064 Bologna, Italy
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15
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Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor. SENSORS 2022; 22:s22020412. [PMID: 35062375 PMCID: PMC8778464 DOI: 10.3390/s22020412] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/23/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023]
Abstract
Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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16
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Putortì A, Corrado M, Avenali M, Martinelli D, Allena M, Cristina S, Grillo V, Martinis L, Tamburin S, Serrao M, Pisani A, Tassorelli C, De Icco R. The Effects of Intensive Neurorehabilitation on Sequence Effect in Parkinson's Disease Patients With and Without Freezing of Gait. Front Neurol 2021; 12:723468. [PMID: 34557151 PMCID: PMC8453149 DOI: 10.3389/fneur.2021.723468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The sequence effect (SE), defined as a reduction in amplitude of repetitive movements, is a common clinical feature of Parkinson's disease (PD) and is supposed to be a major contributor to freezing of gait (FOG). During walking, SE manifests as a step-by-step reduction in step length when approaching a turning point or gait destination, resulting in the so-called destination sequence effect (dSE). Previous studies explored the therapeutic effects of several strategies on SE, but none of them evaluated the role of an intensive rehabilitative program. Objectives: Here we aim to study the effects of a 4-week rehabilitative program on dSE in patients with PD with and without FOG. Methods: Forty-three patients (30 males, 70.6 ± 7.5 years old) with idiopathic PD were enrolled. The subjects were divided into two groups: patients with (PD + FOG, n = 23) and without FOG (PD - FOG, n = 20). All patients underwent a standardized 4-week intensive rehabilitation in-hospital program. At hospital admission (T0) and discharge (T1), all subjects were evaluated with an inertial gait analysis for dSE recording. Results: At T0, the dSE was more negative in the PD + FOG group (-0.80 ± 0.6) when compared to the PD - FOG group (-0.39 ± 0.3) (p = 0.007), even when controlling for several clinical and demographic features. At T1, the dSE was reduced in the overall study population (p = 0.001), with a more pronounced improvement in the PD + FOG group (T0: -0.80 ± 0.6; T1: -0.23 ± 0.4) when compared to the PD - FOG group (T0: -0.39 ± 0.3; T1: -0.22 ± 0.5) (p = 0.012). At T1, we described in the overall study population an improvement in speed, cadence, stride duration, and stride length (p = 0.001 for all variables). Conclusions: dSE is a core feature of PD gait dysfunction, specifically in patients with FOG. A 4-week intensive rehabilitative program improved dSE in PD patients, exerting a more notable beneficial effect in the PD + FOG group.
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Affiliation(s)
- Alessia Putortì
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michele Corrado
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Micol Avenali
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Daniele Martinelli
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Marta Allena
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Silvano Cristina
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Valentina Grillo
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Luca Martinis
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Public Health, Experimental Medicine and Forensic Science, University of Pavia, Pavia, Italy
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Mariano Serrao
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
- Movement Analysis Laboratory, Policlinico Italia, Rome, Italy
| | - Antonio Pisani
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Cristina Tassorelli
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Roberto De Icco
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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