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4. Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Author

Micó-Amigo, M. Encarna, Bonci, Tecla, Paraschiv-Ionescu, Anisoara, Ullrich, Martin, Kirk, Cameron, Soltani, Abolfazl, Küderle, Arne, Gazit, Eran, Salis, Francesca, Alcock, Lisa, Aminian, Kamiar, Becker, Clemens, Bertuletti, Stefano, Brown, Philip, Buckley, Ellen, Cantu, Alma, Carsin, Anne-Elie, Caruso, Marco, Caulfield, Brian, Cereatti, Andrea, Chiari, Lorenzo, D’Ascanio, Ilaria, Eskofier, Bjoern, Fernstad, Sara, Froehlich, Marcel, Garcia-Aymerich, Judith, Hansen, Clint, Hausdorff, Jeffrey M., Hiden, Hugo, Hume, Emily, Keogh, Alison, Kluge, Felix, Koch, Sarah, Maetzler, Walter, Megaritis, Dimitrios, Mueller, Arne, Niessen, Martijn, Palmerini, Luca, Schwickert, Lars, Scott, Kirsty, Sharrack, Basil, Sillén, Henrik, Singleton, David, Vereijken, Beatrix, Vogiatzis, Ioannis, Yarnall, Alison J., Rochester, Lynn, Mazzà, Claudia, and Del Din, Silvia

Published
2024
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5. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

Author

Kirk, Cameron, Küderle, Arne, Micó-Amigo, M. Encarna, Bonci, Tecla, Paraschiv-Ionescu, Anisoara, Ullrich, Martin, Soltani, Abolfazl, Gazit, Eran, Salis, Francesca, Alcock, Lisa, Aminian, Kamiar, Becker, Clemens, Bertuletti, Stefano, Brown, Philip, Buckley, Ellen, Cantu, Alma, Carsin, Anne-Elie, Caruso, Marco, Caulfield, Brian, Cereatti, Andrea, Chiari, Lorenzo, D’Ascanio, Ilaria, Garcia-Aymerich, Judith, Hansen, Clint, Hausdorff, Jeffrey M., Hiden, Hugo, Hume, Emily, Keogh, Alison, Kluge, Felix, Koch, Sarah, Maetzler, Walter, Megaritis, Dimitrios, Mueller, Arne, Niessen, Martijn, Palmerini, Luca, Schwickert, Lars, Scott, Kirsty, Sharrack, Basil, Sillén, Henrik, Singleton, David, Vereijken, Beatrix, Vogiatzis, Ioannis, Yarnall, Alison J., Rochester, Lynn, Mazzà, Claudia, Eskofier, Bjoern M., and Del Din, Silvia

Published
2024
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6. Relation of gait measures with mild unilateral knee pain during walking using machine learning

Author

Bacon, Kathryn L, Felson, David T, Jafarzadeh, S Reza, Kolachalama, Vijaya B, Hausdorff, Jeffrey M, Gazit, Eran, Segal, Neil A, Lewis, Cora E, Nevitt, Michael C, and Kumar, Deepak

Subjects
Allied Health and Rehabilitation Science, Health Sciences, Sports Science and Exercise, Arthritis, Osteoarthritis, Pain Research, Clinical Research, Aging, Chronic Pain, Musculoskeletal, Humans, Female, Middle Aged, Male, Cross-Sectional Studies, Gait, Walking, Knee Joint, Pain, Osteoarthritis, Knee, Machine Learning, Biomechanical Phenomena, Multicenter Osteoarthritis Study Investigators
Abstract

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique ("super learning") to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64-1.00) and aOR 0.79 (0.66-0.95), respectively].

Published
2022

7. World guidelines for falls prevention and management for older adults: a global initiative

Author

Montero-Odasso, Manuel, van der Velde, Nathalie, Martin, Finbarr C, Petrovic, Mirko, Tan, Maw Pin, Ryg, Jesper, Aguilar-Navarro, Sara, Alexander, Neil B, Becker, Clemens, Blain, Hubert, Bourke, Robbie, Cameron, Ian D, Camicioli, Richard, Clemson, Lindy, Close, Jacqueline, Delbaere, Kim, Duan, Leilei, Duque, Gustavo, Dyer, Suzanne M, Freiberger, Ellen, Ganz, David A, Gómez, Fernando, Hausdorff, Jeffrey M, Hogan, David B, Hunter, Susan MW, Jauregui, Jose R, Kamkar, Nellie, Kenny, Rose-Anne, Lamb, Sarah E, Latham, Nancy K, Lipsitz, Lewis A, Liu-Ambrose, Teresa, Logan, Pip, Lord, Stephen R, Mallet, Louise, Marsh, David, Milisen, Koen, Moctezuma-Gallegos, Rogelio, Morris, Meg E, Nieuwboer, Alice, Perracini, Monica R, Pieruccini-Faria, Frederico, Pighills, Alison, Said, Catherine, Sejdic, Ervin, Sherrington, Catherine, Skelton, Dawn A, Dsouza, Sabestina, Speechley, Mark, Stark, Susan, Todd, Chris, Troen, Bruce R, van der Cammen, Tischa, Verghese, Joe, Vlaeyen, Ellen, Watt, Jennifer A, Masud, Tahir, Singh, Devinder Kaur Ajit, Aguilar-Navarro, Sara G, Caona, Edgar Aguilera, Allen, Natalie, Anweiller, Cedric, Avila-Funes, Alberto, Santos, Renato Barbosa, Batchelor, Francis, Beauchamp, Marla, Birimoglu, Canan, Bohlke, Kayla, Bourke, Robert, Bouzòn, Christina Alonzo, Bridenbaugh, Stephanie, Buendia, Patricio Gabriel, Cameron, Ian, Canning, Colleen, Cano-Gutierrez, Carlos Alberto, Carbajal, Juan Carlos, de Abreu, Daniela Cristina Carvalho, Casas-Herrero, Alvaro, Ceriani, Alejandro, Cesari, Matteo, Chiari, Lorenzo, Alemǻn, Luis Manuel Cornejo, Dawson, Rik, Doody, Paul, Dyer, Suzanne, Ellmers, Toby, Fairhall, Nicola, Ferruci, Luigi, and Frith, James

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Public Health, Health Sciences, Clinical Sciences, Aging, Prevention, Clinical Research, Patient Safety, Physical Injury - Accidents and Adverse Effects, Health and social care services research, 8.1 Organisation and delivery of services, 7.1 Individual care needs, Management of diseases and conditions, Good Health and Well Being, Aged, Caregivers, Humans, Independent Living, Quality of Life, Risk Assessment, falls, injury, aged, guidelines, recommendations, clinical practice, world, global, consensus, older people, Task Force on Global Guidelines for Falls in Older Adults, Public Health and Health Services, Psychology, Geriatrics, Clinical sciences, Health services and systems, Applied and developmental psychology
Abstract

Backgroundfalls and fall-related injuries are common in older adults, have negative effects on functional independence and quality of life and are associated with increased morbidity, mortality and health related costs. Current guidelines are inconsistent, with no up-to-date, globally applicable ones present.Objectivesto create a set of evidence- and expert consensus-based falls prevention and management recommendations applicable to older adults for use by healthcare and other professionals that consider: (i) a person-centred approach that includes the perspectives of older adults with lived experience, caregivers and other stakeholders; (ii) gaps in previous guidelines; (iii) recent developments in e-health and (iv) implementation across locations with limited access to resources such as low- and middle-income countries.Methodsa steering committee and a worldwide multidisciplinary group of experts and stakeholders, including older adults, were assembled. Geriatrics and gerontological societies were represented. Using a modified Delphi process, recommendations from 11 topic-specific working groups (WGs), 10 ad-hoc WGs and a WG dealing with the perspectives of older adults were reviewed and refined. The final recommendations were determined by voting.Recommendationsall older adults should be advised on falls prevention and physical activity. Opportunistic case finding for falls risk is recommended for community-dwelling older adults. Those considered at high risk should be offered a comprehensive multifactorial falls risk assessment with a view to co-design and implement personalised multidomain interventions. Other recommendations cover details of assessment and intervention components and combinations, and recommendations for specific settings and populations.Conclusionsthe core set of recommendations provided will require flexible implementation strategies that consider both local context and resources.

Published
2022

11. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Author

Micó-Amigo, M. Encarna, Bonci, Tecla, Paraschiv-Ionescu, Anisoara, Ullrich, Martin, Kirk, Cameron, Soltani, Abolfazl, Küderle, Arne, Gazit, Eran, Salis, Francesca, Alcock, Lisa, Aminian, Kamiar, Becker, Clemens, Bertuletti, Stefano, Brown, Philip, Buckley, Ellen, Cantu, Alma, Carsin, Anne-Elie, Caruso, Marco, Caulfield, Brian, Cereatti, Andrea, Chiari, Lorenzo, D’Ascanio, Ilaria, Eskofier, Bjoern, Fernstad, Sara, Froehlich, Marcel, Garcia-Aymerich, Judith, Hansen, Clint, Hausdorff, Jeffrey M., Hiden, Hugo, Hume, Emily, Keogh, Alison, Kluge, Felix, Koch, Sarah, Maetzler, Walter, Megaritis, Dimitrios, Mueller, Arne, Niessen, Martijn, Palmerini, Luca, Schwickert, Lars, Scott, Kirsty, Sharrack, Basil, Sillén, Henrik, Singleton, David, Vereijken, Beatrix, Vogiatzis, Ioannis, Yarnall, Alison J., Rochester, Lynn, Mazzà, Claudia, and Del Din, Silvia

Published
2023
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14. Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease

Author

Ghosal, Rahul, Varma, Vijay R., Volfson, Dmitri, Urbanek, Jacek, Hausdorff, Jeffrey M., Watts, Amber, and Zipunnikov, Vadim

Subjects
Statistics - Applications
Abstract

Wearable data is a rich source of information that can provide deeper understanding of links between human behaviours and human health. Existing modelling approaches use wearable data summarized at subject level via scalar summaries using regression techniques, temporal (time-of-day) curves using functional data analysis (FDA), and distributions using distributional data analysis (DDA). We propose to capture temporally local distributional information in wearable data using subject-specific time-by-distribution (TD) data objects. Specifically, we propose scalar on time-by-distribution regression (SOTDR) to model associations between scalar response of interest such as health outcomes or disease status and TD predictors. We show that TD data objects can be parsimoniously represented via a collection of time-varying L-moments that capture distributional changes over the time-of-day. The proposed method is applied to the accelerometry study of mild Alzheimer's disease (AD). Mild AD is found to be significantly associated with reduced maximal level of physical activity, particularly during morning hours. It is also demonstrated that TD predictors attain much stronger associations with clinical cognitive scales of attention, verbal memory, and executive function when compared to predictors summarized via scalar total activity counts, temporal functional curves, and quantile functions. Taken together, the present results suggest that the SOTDR analysis provides novel insights into cognitive function and AD.

Published
2021
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15. Distributional data analysis via quantile functions and its application to modelling digital biomarkers of gait in Alzheimer's Disease

Author

Ghosal, Rahul, Varma, Vijay R., Volfson, Dmitri, Hillel, Inbar, Urbanek, Jacek, Hausdorff, Jeffrey M., Watts, Amber, and Zipunnikov, Vadim

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

With the advent of continuous health monitoring with wearable devices, users now generate their unique streams of continuous data such as minute-level step counts or heartbeats. Summarizing these streams via scalar summaries often ignores the distributional nature of wearable data and almost unavoidably leads to the loss of critical information. We propose to capture the distributional nature of wearable data via user-specific quantile functions (QF) and use these QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also propose to represent QFs via user-specific L-moments, robust rank-based analogs of traditional moments, and use L-moments as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually consistent interpretations: in terms of quantile levels by SOQFR and in terms of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal distributional data via Joint and Individual Variation Explained (JIVE) using L-moments. The proposed methods are illustrated in a study of association of digital gait biomarkers with cognitive function in Alzheimer's disease (AD). Our analysis shows that the proposed methods demonstrate higher predictive performance and attain much stronger associations with clinical cognitive scales compared to simple distributional summaries.

Published
2021
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18. Loss-analysis via Attention-scale for Physiologic Time Series

Author

Yang, Jiawei and Hausdorff, Jeffrey M.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Physics - Data Analysis, Statistics and Probability
Abstract

Physiologic signals have properties across multiple spatial and temporal scales, which can be shown by the complexity-analysis of the coarse-grained physiologic signals by scaling techniques such as the multiscale. Unfortunately, the results obtained from the coarse-grained signals by the multiscale may not fully reflect the properties of the original signals because there is a loss caused by scaling techniques and the same scaling technique may bring different losses to different signals. Another problem is that multiscale does not consider the key observations inherent in the signal. Here, we show a new analysis method for time series called the loss-analysis via attention-scale. We show that multiscale is a special case of attention-scale. The loss-analysis can complement to the complexity-analysis to capture aspects of the signals that are not captured using previously developed measures. This can be used to study ageing, diseases, and other physiologic phenomenon.

Published
2020

19. Association Between Frailty and Free-Living Walking Performance in People With Multiple Sclerosis

Author

Zanotto, Tobia, Galperin, Irina, Mirelman, Anat, Chen, Lingjun, Regev, Keren, Karni, Arnon, Schmitz-Hubsch, Tanja, Paul, Friedemann, Lynch, Sharon G., Akinwuntan, Abiodun E., Devos, Hannes, Hausdorff, Jeffrey M., and Sosnoff, Jacob J.

Subjects
Frail elderly -- Health aspects, Multiple sclerosis -- Care and treatment -- Patient outcomes
Abstract

Objective. The purpose of this study was to examine the association between frailty and the quantity and quality of freeliving walking and the mediating effect of frailty on the relationship between disability and walking performance in people with multiple sclerosis (MS). Methods. Ninety-nine people with relapsing-remitting MS (mean age = 49.3 [SD = 9.8] years; 73.7% women; Expanded Disability Status Scale [EDSS] score range = 2.0-6.0) wore a triaxial accelerometer for 7 days. Recorded measures reflected the quantity (daily step counts, number of 30-second walking bouts, and signal vector magnitude [SVM]) and quality (gait speed, step cadence, step and stride regularity, and sample entropy) of walking. For each walking quality measure, the typical (median), best (90th percentile), and worst (10th percentile) values were calculated. Frailty was evaluated through a 38-item frailty index. Results. Participants were classified as not frail (n = 31), moderately frail (n = 34), and severely frail (n = 34) on the basis of established procedures. Patients who were moderately and severely frail exhibited poorer performance in all measures of walking quantity and quality, except for sample entropy, than individuals who were not frail. No differences in freeliving walking performance were observed between the moderately and severely frail groups. Frailty did not mediate the relationship between disability (EDSS) and measures of walking quality. Conversely, frailty had a significant mediating effect on the relationship between disability and measures of walking quantity, such as daily step counts (indirect effect: b =-220.42, 95% CI = -452.03 to -19.65) and SVM (indirect effect: b =-1.00, 95% CI = -1.86 to -0.30). Conclusion. Frailty is associated with poorer free-living walking performance in people with MS. The study findings suggest that frailty, rather than disability, may be primarily responsible for the lower amount of physical activity performed by people with MS in the real world. Impact. The observation that frailty and disability are differently related to measures of walking quality and quantity underscores the importance of a targeted approach to rehabilitation in people with MS. Keywords: Disability, Frailty, Gait, Multiple Sclerosis, Physical Activity, Walking, Introduction Frailty is a biological syndrome that reflects decreased reserve and resistance to stressors arising from cumulative declines across multiple physiologic systems. (1) Although aging plays a central role in [...]

Published
2023
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20. Daily-Living Freezing of Gait as Quantified Using Wearables in People With Parkinson Disease: Comparison With Self-Report and Provocation Tests

Author

Denk, Diana, Herman, Talia, Zoetewei, Demi, Ginis, Pieter, Brozgol, Marina, Thumm, Pablo Cornejo, Decaluwe, Eva, Ganz, Natalie, Palmerini, Luca, Giladi, Nir, Nieuwboer, Alice, and Hausdorff, Jeffrey M.

Subjects
Biosensors -- Usage, Activities of daily living -- Health aspects, Parkinson's disease -- Complications and side effects -- Care and treatment, Gait disorders -- Diagnosis
Abstract

Objective. Freezing of gait (FOG) is an episodic, debilitating phenomenon that is common among people with Parkinson disease. Multiple approaches have been used to quantify FOG, but the relationships among them have not been well studied. In this cross-sectional study, we evaluated the associations among FOG measured during unsupervised daily-living monitoring, structured in-home FOG-provoking tests, and self-report. Methods. Twenty-eight people with Parkinson disease and FOG were assessed using self-report questionnaires, percentage of time spent frozen (%TF) during supervised FOG-provoking tasks in the home while off and on dopaminergic medication, and %TF evaluated using wearable sensors during 1 week of unsupervised daily-living monitoring. Correlations between those 3 assessment approaches were analyzed to quantify associations. Further, based on the %TF difference between in-home off-medication testing and in-home on-medication testing, the participants were divided into those responding to Parkinson disease medication (responders) and those not responding to Parkinson disease medication (nonresponders) in order to evaluate the differences in the other FOG measures. Results. The %TF during unsupervised daily living was mild to moderately correlated with the %TF during a subset of the tasks of the in-home off-medication testing but not the on-medication testing or self-report. Responders and nonresponders differed in the %TF during the personal "hot spot" task of the provoking protocol while off medication (but not while on medication) but not in the total scores of the self-report questionnaires or the measures of FOG evaluated during unsupervised daily living. Conclusion. The %TF during daily living was moderately related to FOG during certain in-home FOG-provoking tests in the off-medication state. However, this measure of FOG was not associated with self-report or FOG provoked in the on-medication state. These findings suggest that to fully capture FOG severity, it is best to assess FOG using a combination of all 3 approaches. Impact. These findings suggest that several complementary approaches are needed to provide a complete assessment of FOG severity. Keywords: Daily-Living Monitoring, FOG Assessment, FOG Severity, Freezing of Gait, Introduction Freezing of gait (FOG) is an episodic, unpredictable phenomenon that commonly impacts people with Parkinson disease. Up to 70% to 80% of people with Parkinson disease experience FOG, especially [...]

Published
2022
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28. Design and validation of a multi-task, multi-context protocol for real-world gait simulation

Author

Scott, Kirsty, Bonci, Tecla, Salis, Francesca, Alcock, Lisa, Buckley, Ellen, Gazit, Eran, Hansen, Clint, Schwickert, Lars, Aminian, Kamiar, Bertuletti, Stefano, Caruso, Marco, Chiari, Lorenzo, Sharrack, Basil, Maetzler, Walter, Becker, Clemens, Hausdorff, Jeffrey M., Vogiatzis, Ioannis, Brown, Philip, Del Din, Silvia, Eskofier, Björn, Paraschiv-Ionescu, Anisoara, Keogh, Alison, Kirk, Cameron, Kluge, Felix, Micó-Amigo, Encarna M., Mueller, Arne, Neatrour, Isabel, Niessen, Martijn, Palmerini, Luca, Sillen, Henrik, Singleton, David, Ullrich, Martin, Vereijken, Beatrix, Froehlich, Marcel, Brittain, Gavin, Caulfield, Brian, Koch, Sarah, Carsin, Anne-Elie, Garcia-Aymerich, Judith, Kuederle, Arne, Yarnall, Alison, Rochester, Lynn, Cereatti, Andrea, and Mazzà, Claudia

Published
2022
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29. Which Gait Tasks Produce Reliable Outcome Measures of Freezing of Gait in Parkinson's Disease?

Author

Zoetewei, Demi, Ginis, Pieter, Goris, Maaike, Gilat, Moran, Herman, Talia, Brozgol, Marina, Thumm, Pablo Cornejo, Hausdorff, Jeffrey M., Nieuwboer, Alice, and D'Cruz, Nicholas

Subjects
DUAL-task paradigm, GAIT disorders, STATISTICAL reliability, PARKINSON'S disease, MEASUREMENT errors
Abstract

Background: Measurement of freezing of gait (FOG) relies on the sensitivity and reliability of tasks to provoke FOG. It is currently unclear which tasks provide the best outcomes and how medication state plays into this. Objective: To establish the sensitivity and test-retest reliability of various FOG-provoking tasks for presence and severity of FOG, with (ON) and without (OFF) dopaminergic medication. Methods: FOG-presence and percentage time frozen (% TF) were derived from video annotations of a home-based FOG-provoking protocol performed in OFF and ON. This included: the four meter walk (4MW), Timed Up and Go (TUG) single (ST) and dual task (DT), 360° turns in ST and DT, a doorway condition, and a personalized condition. Sensitivity was tested at baseline in 63 definite freezers. Test-retest reliability was evaluated over 5 weeks in 26 freezers. Results: Sensitivity and test-retest reliability were highest for 360° turns and higher in OFF than ON. Test-retest intra-class correlation coefficients of % TF varied between 0.63–0.90 in OFF and 0.18–0.87 in ON, and minimal detectable changes (MDCs) were high. The optimal protocol included TUG ST, 360° turns ST, 360° turns DT and a doorway condition, provoking FOG in all freezers in OFF and 91.9% in ON and this could be done reliably in 95.8% (OFF) and 84.0% (ON) of the sample. Combining OFF and ON further improved outcomes. Conclusions: The highest sensitivity and reliability was achieved with a multi-trigger protocol performed in OFF + ON. However, the high MDCs for % TF underscore the need for further optimization of FOG measurement. Plain Language Summary: Freezing of gait is a very burdensome and episodic symptom in Parkinson's disease that is difficult to measure. Measurement of freezing is needed to determine whether someone has freezing and how severe this is, and relies on observation during a freezing-triggering protocol. However, it is unclear what protocol is sufficiently sensitive to trigger freezing in many freezers, and whether freezing can be triggered reliably at different timepoints. Here, we investigated 1) which tasks can trigger freezing-presence and freezing-severity sensitively and reliably, 2) how medication state influences this, and 3) what task combination was most reliable. Sixty-three patients with daily freezing performed several freezing-triggering tasks in their homes, both with (ON) and without (OFF) anti-Parkinsonian medication. In twenty-six patients, the measurement was repeated 5 weeks later to determine test-retest reliability. First, we found that performing 360° turns in place with a cognitive dual task was the most sensitive and reliable task to trigger FOG. Second, sensitivity and reliability were better in OFF than in ON. Third, the most reliable protocol included: the Timed-Up and Go, 360° turns in place with and without the dual task, and a doorway condition. This protocol triggered freezing in all patients in OFF and 91.9% in ON and did so reliably in 95.8% (OFF) and 84.0% (ON) of the sample. We recommend to measure freezing with this protocol in OFF + ON, which further improved reliability. However, the measurement error for freezing-severity was high, even for this optimal protocol, underscoring the need for further optimization of freezing measurement. [ABSTRACT FROM AUTHOR]

Published
2024
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32. A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

Author

Zadka, Assaf, primary, Rabin, Neta, additional, Gazit, Eran, additional, Mirelman, Anat, additional, Nieuwboer, Alice, additional, Rochester, Lynn, additional, Del Din, Silvia, additional, Pelosin, Elisa, additional, Avanzino, Laura, additional, Bloem, Bastiaan R., additional, Della Croce, Ugo, additional, Cereatti, Andrea, additional, and Hausdorff, Jeffrey M., additional

Published
2024
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33. Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

Author

Brand, Yonatan E., primary, Kluge, Felix, additional, Palmerini, Luca, additional, Paraschiv-Ionescu, Anisoara, additional, Becker, Clemens, additional, Cereatti, Andrea, additional, Maetzler, Walter, additional, Sharrack, Basil, additional, Vereijken, Beatrix, additional, Yarnall, Alison J., additional, Rochester, Lynn, additional, Din, Silvia Del, additional, Muller, Arne, additional, Buchman, Aron S., additional, Hausdorff, Jeffrey M., additional, and Perlman, Or, additional

Published
2024
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38. Supplementary material for Machine learning analysis of wearable sensor data from mobility testing distinguishes Parkinson's disease from other forms of parkinsonism

Author

Khalil, Rana M., Cummings, Michael P., Khalil, Rana M., Shulman, Lisa M., Gruber-Baldini, Ann L., Hausdorff, Jeffrey M., von Coelln, Rainer, Khalil, Rana M., Cummings, Michael P., Khalil, Rana M., Shulman, Lisa M., Gruber-Baldini, Ann L., Hausdorff, Jeffrey M., and von Coelln, Rainer

Abstract

Parkinson's Disease (PD) and other forms of parkinsonism share characteristic motor symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation creates a diagnostic challenge, underscoring the need for objective differentiation tools. In this study, we analyzed wearable sensor data collected during mobility testing from 260 PD participants and 18 participants with etiologically diverse forms of parkinsonism. Our findings illustrate that machine learning-based analysis of data from a single wearable sensor can effectively distinguish idiopathic PD from non-PD parkinsonism with a balanced accuracy of 83.5%, comparable to expert diagnosis. Moreover, we found that diagnostic performance can be improved through severity-based partitioning of participants, achieving a balanced accuracy of 95.9%, 91.2% and 100% for mild, moderate and severe cases, respectively. Beyond its diagnostic implications, our results suggest the possibility of streamlining the testing protocol by using the Timed Up and Go test as a single mobility task. Furthermore, we present a detailed analysis of several case studies of challenging scenarios commonly encountered in clinical practice, including diagnostic uncertainty at the initial visit, and changes in clinical diagnosis at a subsequent visit. Together, these findings demonstrate the potential of applying machine learning on sensor-based measures of mobility to distinguish between PD and other forms of parkinsonism.

Published
2024

39. Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Author

Khalil, Rana M., Shulman, Lisa M., Gruber-Baldini, Ann L., Shakya, Sunita, Fenderson, Rebecca, Van Hoven, Maxwell, Hausdorff, Jeffrey M., von Coelln, Rainer, and Cummings, Michael P.

Subjects
MACHINE learning, PARKINSON'S disease, WEARABLE technology, MOVEMENT disorders, ELECTRONIC data processing, GYROSCOPES, ACCELEROMETERS
Abstract

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Discriminating between indoor and outdoor environments during daily living activities using local magnetic field characteristics and machine learning techniques

Author

Marcianò, Vincenzo, primary, Bertuletti, Stefano, additional, Caruso, Marco, additional, Bonci, Tecla, additional, Alcock, Lisa, additional, Gazit, Eran, additional, Ireson, Neil, additional, Bevilacqua, Antonio, additional, Mazzà, Claudia, additional, Ciravegna, Fabio, additional, Din, Silvia Del, additional, Hausdorff, Jeffrey M., additional, Ifrim, Georgiana, additional, Caulfield, Brian, additional, and Cereatti, Andrea, additional

Published
2024
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