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Rigot SK, Maronati R, Lettenberger A, O'Brien MK, Alamdari K, Hoppe-Ludwig S, McGuire M, Looft JM, Wacek A, Cave J, Sauerbrey M, Jayaraman A. Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis. Arch Phys Med Rehabil 2024; 105:546-557. [PMID: 37907160 DOI: 10.1016/j.apmr.2023.10.008] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVE To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. DESIGN Cross-sectional study. SETTING General community setting (ie, institutional research laboratory and community free-living). PARTICIPANTS Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. RESULTS All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). CONCLUSIONS Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.
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Affiliation(s)
- Stephanie K Rigot
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Northwestern University, Department of Physical Medicine & Rehabilitation, Chicago, IL
| | - Rachel Maronati
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - Ahalya Lettenberger
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Rice University, Department of Bioengineering, Houston, TX
| | - Megan K O'Brien
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Northwestern University, Department of Physical Medicine & Rehabilitation, Chicago, IL
| | - Kayla Alamdari
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - Shenan Hoppe-Ludwig
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - Matthew McGuire
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - John M Looft
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Division of Rehabilitation Science, University of Minnesota Medical School, Minneapolis, MN
| | - Amber Wacek
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN
| | - Juan Cave
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN
| | - Matthew Sauerbrey
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN
| | - Arun Jayaraman
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Northwestern University, Department of Physical Medicine & Rehabilitation, Chicago, IL; Northwestern University, Department of Physical Therapy & Human Movement Sciences, Chicago, IL.
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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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