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Botonis OK, Harari Y, Embry KR, Mummidisetty CK, Riopelle D, Giffhorn M, Albert MV, Heike V, Jayaraman A. Wearable airbag technology and machine learned models to mitigate falls after stroke. J Neuroeng Rehabil 2022; 19:60. [PMID: 35715823 PMCID: PMC9205156 DOI: 10.1186/s12984-022-01040-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population. METHODS We collected data from a wearable airbag's inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15 control) history of stroke while performing a series of falls (842 falls total) and non-falls (961 non-falls total) in a laboratory setting. A leave-one-subject-out crossvalidation was used to compare the performance of two identical machine learned models (adaptive boosting classifier) trained on cohort-dependent data (control or stroke) to pre-detect falls in the stroke cohort. RESULTS The average performance of the model trained on stroke data (recall = 0.905, precision = 0.900) had statistically significantly better recall (P = 0.0035) than the model trained on control data (recall = 0.800, precision = 0.944), while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior-posterior (AP) falls (stroke-trained model's F1-score was 35% higher, P = 0.019). Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the AUC (Area under the receiver operating characteristic) for classifying AP falls for both models (P < 0.04). Preliminary analysis suggests that users with more severe stroke impairments benefit further from a stroke-trained model. The optimal lead time (time interval pre-impact to detect falls) differed between control- and stroke-trained models. CONCLUSIONS These results demonstrate the importance of population sensitivity, non-falls data, and optimal lead time for machine learned pre-impact fall detection specific to stroke. Existing fall mitigation technologies should be challenged to include data of neurologically impaired individuals in model development to adequately detect falls in other high fall risk populations. Trial registration https://clinicaltrials.gov/ct2/show/NCT05076565 ; Unique Identifier: NCT05076565. Retrospectively registered on 13 October 2021.
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Affiliation(s)
- Olivia K Botonis
- Max Nader Rehabilitation Technologies and Outcomes Lab, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Yaar Harari
- Max Nader Rehabilitation Technologies and Outcomes Lab, Shirley Ryan AbilityLab, Chicago, IL, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
| | - Kyle R Embry
- Max Nader Rehabilitation Technologies and Outcomes Lab, Shirley Ryan AbilityLab, Chicago, IL, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
| | | | - David Riopelle
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.,Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Matt Giffhorn
- Max Nader Rehabilitation Technologies and Outcomes Lab, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Mark V Albert
- Department of Computer Science and Engineering, Department of Biomedical Engineering, University of North Texas, Denton, TX, USA
| | - Vallery Heike
- Department of BioMechanical Engineering, Delft University of Technology, Delft, The Netherlands.,Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Arun Jayaraman
- Max Nader Rehabilitation Technologies and Outcomes Lab, Shirley Ryan AbilityLab, Chicago, IL, USA. .,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
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O'Brien MK, Botonis OK, Larkin E, Carpenter J, Martin-Harris B, Maronati R, Lee K, Cherney LR, Hutchison B, Xu S, Rogers JA, Jayaraman A. Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study. Digit Biomark 2021; 5:167-175. [PMID: 34723069 DOI: 10.1159/000517144] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/10/2021] [Indexed: 11/19/2022] Open
Abstract
Introduction Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning. Methods Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls). Results These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037). Discussion Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.
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Affiliation(s)
- Megan K O'Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA
| | - Olivia K Botonis
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | - Elissa Larkin
- Think and Speak Lab, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | - Julia Carpenter
- Think and Speak Lab, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | - Bonnie Martin-Harris
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Rachel Maronati
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | | | - Leora R Cherney
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA.,Think and Speak Lab, Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Brianna Hutchison
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Shuai Xu
- Departments of Materials Science and Engineering, Center for Bio-Integrated Electronics, Biomedical Engineering, Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA
| | - John A Rogers
- Departments of Materials Science and Engineering, Center for Bio-Integrated Electronics, Biomedical Engineering, Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA
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