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Nissen M, Perez CA, Jaeger KM, Bleher H, Flaucher M, Huebner H, Danzberger N, Titzmann A, Pontones CA, Fasching PA, Beckmann MW, Eskofier BM, Leutheuser H. Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study. JMIR Pediatr Parent 2023; 6:e50765. [PMID: 38109377 PMCID: PMC10750977 DOI: 10.2196/50765] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 12/20/2023] Open
Abstract
Background Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study. Objective The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy. Methods A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective. Results A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated "above average" in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes. Conclusions Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.
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Affiliation(s)
- Michael Nissen
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Carlos A Perez
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Katharina M Jaeger
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Hannah Bleher
- Department of Social Ethics, University of Bonn, Bonn, Germany
| | - Madeleine Flaucher
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nina Danzberger
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Adriana Titzmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Constanza A Pontones
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Heike Leutheuser
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
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Marx A, Di Stefano F, Leutheuser H, Chin-Cheong K, Pfister M, Burckhardt MA, Bachmann S, Vogt JE. Blood glucose forecasting from temporal and static information in children with T1D. Front Pediatr 2023; 11:1296904. [PMID: 38155742 PMCID: PMC10752933 DOI: 10.3389/fped.2023.1296904] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Background The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term (30 min) and long-term (2 h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30 min (RMSE of 1.66 mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50 mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.
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Affiliation(s)
- Alexander Marx
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | | | | | - Marc Pfister
- Pediatric Pharmacology and Pharmacometrics, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
- Pediatric Endocrinolgy and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
| | - Sara Bachmann
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
- Pediatric Endocrinolgy and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
| | - Julia E. Vogt
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
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Nissen M, Barrios Campo N, Flaucher M, Jaeger KM, Titzmann A, Blunck D, Fasching PA, Engelhardt V, Eskofier BM, Leutheuser H. Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data. NPJ Digit Med 2023; 6:189. [PMID: 37821584 PMCID: PMC10567694 DOI: 10.1038/s41746-023-00935-3] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/29/2023] [Indexed: 10/13/2023] Open
Abstract
During pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms. To date, the resulting data are primarily used from a commercial rather than a scientific perspective. In this work, we aim to examine symptom occurrence, course, and their correlation throughout pregnancy. Self-reported app data of a pregnancy symptom tracker is used. In this context, we present methods to handle noisy real-world app data from commercial applications to understand the trajectory of user and patient-reported data. We report real-world evidence from patient-reported outcomes that exceeds previous works: 1,549,186 tracked symptoms from 183,732 users of a smartphone pregnancy app symptom tracker are analyzed. The majority of users track symptoms on a single day. These data are generalizable to those users who use the tracker for at least 5 months. Week-by-week symptom report data are presented for each symptom. There are few or conflicting reports in the literature on the course of diarrhea, fatigue, headache, heartburn, and sleep problems. A peak in fatigue in the first trimester, a peak in headache reports around gestation week 15, and a steady increase in the reports of sleeping difficulty throughout pregnancy are found. Our work highlights the potential of secondary use of industry data. It reveals and clarifies several previously unknown or disputed symptom trajectories and relationships. Collaboration between academia and industry can help generate new scientific knowledge.
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Affiliation(s)
- Michael Nissen
- Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany.
| | - Nuria Barrios Campo
- Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany
| | - Madeleine Flaucher
- Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany
| | - Katharina M Jaeger
- Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany
| | - Adriana Titzmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsstraße 21/23, 91054, Erlangen, Bavaria, Germany
| | - Dominik Blunck
- Department of Health Management, Institute of Management, Friedrich-Alexander-Universität Erlangen-Nürnberg, Lange Gasse 20, 90403, Nürnberg, Bavaria, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsstraße 21/23, 91054, Erlangen, Bavaria, Germany
| | - Victoria Engelhardt
- Keleya Digital-Health Solutions GmbH, Max-Beer-Straße 25, 10119, Berlin, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany
- Translational Digital Health Group, Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Bavaria, Germany
| | - Heike Leutheuser
- Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany
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Flaucher M, Zakreuskaya A, Nissen M, Mocker A, Fasching PA, Beckmann MW, Eskofier BM, Leutheuser H. Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review. Oncologist 2023; 28:e847-e858. [PMID: 37536278 PMCID: PMC10546835 DOI: 10.1093/oncolo/oyad217] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/07/2023] [Indexed: 08/05/2023] Open
Abstract
Breast cancer is affecting millions of people worldwide. If not appropriately handled, the side effects of different modalities of cancer treatment can negatively impact patients' quality of life and cause treatment interruptions. In recent years, mobile health (mHealth) interventions have shown promising opportunities to support breast cancer care. Numerous studies implemented mobile health interventions aiming to support patients with breast cancer, for example, through physical activity promotion or educational content. Nonetheless, current literature reveals that real-world evidence for the actual benefits remains unclear. In this systematic review, we focus on analyzing the methodology used in recent studies to determine the effects of mHealth applications and wearable devices on the outcome of patients with breast cancer. We followed the PRISMA guideline for the selection, analysis, and reporting of relevant studies found in the databases of Medline, Scopus, Web of Science, and Cochrane Library. A total of 276 unique records were identified, and 20 studies met the inclusion criteria. Study quality was assessed with the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool for Quantitative Studies. While many of the studies used standardized questionnaires as patient-reported outcome measures, there was minimal use of objective measurements, such as activity sensors. Adoption, drop-out rates, and usage behavior of users of the mobile health intervention were often not reported. Future work should clearly define the focus and desired outcome of mHealth interventions and select outcome measures accordingly. Greater transparency facilitates the interpretation of results and conclusions about the real-world evidence of mobile health in breast cancer care.
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Affiliation(s)
- Madeleine Flaucher
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anastasiya Zakreuskaya
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Nissen
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Alexander Mocker
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M Eskofier
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Heike Leutheuser
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Gabler E, Nissen M, Altstidl TR, Titzmann A, Packhauser K, Maier A, Fasching PA, Eskofier BM, Leutheuser H. Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083405 DOI: 10.1109/embc40787.2023.10340336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL_PLANES_DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and explainability.Clinical relevance- To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.
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Nissen M, Flaucher M, Jaeger KM, Huebner H, Danzberger N, Titzmann A, Pontones CA, Fasching PA, Eskofier BM, Leutheuser H. WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38082860 DOI: 10.1109/embc40787.2023.10340204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser's insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical practice.Clinical relevance- WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation.
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Kushioka J, Sun R, Zhang W, Muaremi A, Leutheuser H, Odonkor CA, Smuck M. Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors. Sensors (Basel) 2022; 22:9301. [PMID: 36502003 PMCID: PMC9739785 DOI: 10.3390/s22239301] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)-neurogenic diseases, and knee osteoarthritis (KOA)-structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.
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Affiliation(s)
- Junichi Kushioka
- Department of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USA
| | - Ruopeng Sun
- Department of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USA
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA 94305, USA
| | - Wei Zhang
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Amir Muaremi
- Novartis Institutes for BioMedical Research, 4056 Basel, Switzerland
| | - Heike Leutheuser
- Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Charles A. Odonkor
- Department of Orthopedics and Rehabilitation, Division of Physiatry, Yale School of Medicine, New Haven, CT 06510, USA
| | - Matthew Smuck
- Department of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USA
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA 94305, USA
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Odonkor CA, Taraben S, Tomkins-Lane C, Zhang W, Muaremi A, Leutheuser H, Sun R, Smuck M. Examining the Association Between Self-Reported Estimates of Function and Objective Measures of Gait and Physical Capacity in Lumbar Stenosis. Arch Rehabil Res Clin Transl 2021; 3:100147. [PMID: 34589697 PMCID: PMC8463455 DOI: 10.1016/j.arrct.2021.100147] [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] [Indexed: 11/28/2022] Open
Abstract
Objective: To evaluate the association of self-reported physical function with subjective and objective measures as well as temporospatial gait features in lumbar spinal stenosis (LSS). Design: Cross-sectional pilot study. Setting: Outpatient multispecialty clinic. Participants: Participants with LSS and matched controls without LSS (n=10 per group; N=20). Interventions: Not applicable. Main Outcome Measures: Self-reported physical function (36-Item Short Form Health Survey [SF-36] physical functioning domain), Oswestry Disability Index, Swiss Spinal Stenosis Questionnaire, the Neurogenic Claudication Outcome Score, and inertia measurement unit (IMU)-derived temporospatial gait features Results: Higher self-reported physical function scores (SF-36 physical functioning) correlated with lower disability ratings, neurogenic claudication, and symptom severity ratings in patients with LSS (P<.05). Compared with controls without LSS, patients with LSS have lower scores on physical capacity measures (median total distance traveled on 6-minute walk test: controls 505 m vs LSS 316 m; median total distance traveled on self-paced walking test: controls 718 m vs LSS 174 m). Observed differences in IMU-derived gait features, physical capacity measures, disability ratings, and neurogenic claudication scores between populations with and without LSS were statistically significant. Conclusions: Further evaluation of the association of IMU-derived temporospatial gait with self-reported physical function, pain related-disability, neurogenic claudication, and spinal stenosis symptom severity score in LSS would help clarify their role in tracking LSS outcomes.
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Affiliation(s)
- Charles A Odonkor
- Department of Orthopedics and Rehabilitation, Division of Physiatry, Yale School of Medicine, New Haven, CT.,Orthopedics and Rehabilitation, Interventional Pain Medicine and Physiatry, Yale New Haven Hospital, New Haven, CT
| | - Salam Taraben
- Frank H. Netter School of Medicine, Quinnipiac University, Hamden, CT
| | - Christy Tomkins-Lane
- Department of Health and Physical Education, Mount Royal University, Calgary, Canada
| | - Wei Zhang
- Department of Essential Medicine and Health Product, World Health Organization, Geneva, Switzerland
| | - Amir Muaremi
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Heike Leutheuser
- Central Institute for Medical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Ruopeng Sun
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA
| | - Matthew Smuck
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA
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Odonkor C, Kuwabara A, Tomkins-Lane C, Zhang W, Muaremi A, Leutheuser H, Sun R, Smuck M. Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis. Gait Posture 2020; 80:96-100. [PMID: 32497982 DOI: 10.1016/j.gaitpost.2020.05.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications. RESEARCH QUESTION In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls? METHODS We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test- 6MWT, self-paced walk test - SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared - ωp2) between patients and controls. RESULTS We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination. SIGNIFICANCE These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA.
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Affiliation(s)
- Charles Odonkor
- Department of Orthopaedics & Rehabilitation, Yale University, New Haven, CT, United States
| | - Anne Kuwabara
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA, United States.
| | - Christy Tomkins-Lane
- Department of Health and Physical Education, Mount Royal University, Calgary, Canada
| | - Wei Zhang
- Laboratory of Movement Analysis and Measurements, École Polytechnique Fédérale De Lausanne, Lausanne, Switzerland
| | - Amir Muaremi
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Heike Leutheuser
- Central Institute for Medical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Ruopeng Sun
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA, United States
| | - Matthew Smuck
- Division of Physical Medicine and Rehabilitation, Stanford University, Stanford, CA, United States
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Zheng PZ, Muaremi A, Leutheuser H, Norden J, Sinha A, Eskofier BM, Smuck M. Poster 109: Discriminating Physical Performance Phenotypes of Patients with Chronic Low Back Pain. PM R 2017. [DOI: 10.1016/j.pmrj.2017.08.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | - Amir Muaremi
- Stanford Univ, Santa Clara, California, United States
| | | | - Justin Norden
- Stanford Univ, Santa Clara, California, United States
| | - Aman Sinha
- Stanford Univ, Santa Clara, California, United States
| | | | - Matthew Smuck
- Stanford Univ, Santa Clara, California, United States
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Tobola A, Leutheuser H, Pollak M, Spies P, Hofmann C, Weigand C, Eskofier BM, Fischer G. Self-Powered Multiparameter Health Sensor. IEEE J Biomed Health Inform 2017; 22:15-22. [PMID: 28574370 DOI: 10.1109/jbhi.2017.2708041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance. We introduced ULPSEK-Ultra-Low-Power Sensor Evaluation Kit-for evaluation of biomedical sensors and monitoring applications (http://ulpsek.com). ULPSEK includes a multiparameter sensor measuring and processing electrocardiogram, respiration, motion, body temperature, and photoplethysmography. Instead of a battery, ULPSEK is powered using an efficient body heat harvester. The harvester produced 171 W on average, which was sufficient to power the sensor below 25 C ambient temperature. We present design issues regarding the power supply and the power distribution network of the ULPSEK sensor platform. Due to the security aspect of self-powered health sensors, we suggest a hybrid solution consisting of a battery charged by a harvester.
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Leutheuser H, Heyde C, Roecker K, Gollhofer A, Eskofier BM. Reference-Free Adjustment of Respiratory Inductance Plethysmography for Measurements during Physical Exercise. IEEE Trans Biomed Eng 2017; 64:2836-2846. [PMID: 28278451 DOI: 10.1109/tbme.2017.2675941] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Respiratory inductance plethysmography (RIP) provides an unobtrusive method for measuring breathing characteristics. Accurately adjusted RIP provides reliable measurements of ventilation during rest and exercise if data are acquired via two elastic measuring bands surrounding the rib cage (RC) and abdomen (AB). Disadvantageously, the most accurate reported adjusted model for RIP in literature-least squares regression-requires simultaneous RIP and flowmeter (FM) data acquisition. An adjustment method without simultaneous measurement (reference-free) of RIP and FM would foster usability enormously. METHODS In this paper, we develop generalizable, functional, and reference-free algorithms for RIP adjustment incorporating anthropometric data. Further, performance of only one-degree of freedom (RC or AB) instead of two (RC and AB) is investigated. We evaluate the algorithms with data from 193 healthy subjects who performed an incremental running test using three different datasets: training, reliability, and validation dataset. The regression equation is improved with machine learning techniques such as sequential forward feature selection and 10-fold cross validation. RESULTS Using the validation dataset, the best reference-free adjustment model is the combination of both bands with 84.69% breaths within 20% limits of equivalence compared to 43.63% breaths using the best comparable algorithm from literature. Using only one band, we obtain better results using the RC band alone. CONCLUSION Reference-free adjustment for RIP reveals tidal volume differences of up to 0.25 l when comparing to the best possible adjustment currently present which needs the simultaneous measurement of RIP and FM. SIGNIFICANCE This demonstrates that RIP has the potential for usage in wide applications in ambulatory settings.
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Wasza J, Fischer P, Leutheuser H, Oefner T, Bert C, Maier A, Hornegger J. Real-Time Respiratory Motion Analysis Using 4-D Shape Priors. IEEE Trans Biomed Eng 2016; 63:485-95. [DOI: 10.1109/tbme.2015.2463769] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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14
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Cibis T, Groh BH, Gatermann H, Leutheuser H, Eskofier BM. Wearable real-time ecg monitoring with emergency alert system for scuba diving. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:6074-7. [PMID: 26737677 DOI: 10.1109/embc.2015.7319777] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Medical diagnosis is the first level for recognition and treatment of diseases. To realize fast diagnosis, we propose a concept of a basic framework for the underwater monitoring of a diver's ECG signal, including an alert system that warns the diver of predefined medical emergency situations. The framework contains QRS detection, heart rate calculation and an alert system. After performing a predefined study protocol, the algorithm's accuracy was evaluated with 10 subjects in a dry environment and with 5 subjects in an underwater environment. The results showed that, in 3 out of 5 dives as well as in dry environment, data transmission remained stable. In these cases, the subjects were able to trigger the alert system. The evaluated data showed a clear ECG signal with a QRS detection accuracy of 90 %. Thus, the proposed framework has the potential to detect and to warn of health risks. Further developments of this sample concept can imply an extension for monitoring different biomedical parameters.
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Gradl S, Leutheuser H, Elgendi M, Lang N, Eskofier BM. Temporal correction of detected R-peaks in ECG signals: A crucial step to improve QRS detection algorithms. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:522-5. [PMID: 26736314 DOI: 10.1109/embc.2015.7318414] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In the last decade the interest for heart rate variability analysis has increased tremendously. Related algorithms depend on accurate temporal localization of the heartbeat, e.g. the R-peak in electrocardiogram signals, especially in the presence of arrhythmia. This localization can be delivered by numerous solutions found in the literature which all lack an exact specification of their temporal precision. We implemented three different state-of-the-art algorithms and evaluated the precision of their R-peak localization. We suggest a method to estimate the overall R-peak temporal inaccuracy-dubbed beat slackness-of QRS detectors with respect to normal and abnormal beats. We also propose a simple algorithm that can complement existing detectors to reduce this slackness. Furthermore we define improvements to one of the three detectors allowing it to be used in real-time on mobile devices or embedded hardware. Across the entire MIT-BIH Arrhythmia Database, the average slackness of all the tested algorithms was 9ms for normal beats and 13ms for abnormal beats. Using our complementing algorithm this could be reduced to 4ms for normal beats and to 7ms for abnormal beats. The presented methods can be used to significantly improve the precision of R-peak detection and provide an additional measurement for QRS detector performance.
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Gabsteiger F, Leutheuser H, Reis P, Lochmann M, Eskofier BM. ICA-based reduction of electromyogenic artifacts in EEG data: comparison with and without EMG data. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:3861-4. [PMID: 25570834 DOI: 10.1109/embc.2014.6944466] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Analysis of electroencephalography (EEG) recorded during movement is often aggravated or even completely hindered by electromyogenic artifacts. This is caused by the overlapping frequencies of brain and myogenic activity and the higher amplitude of the myogenic signals. One commonly employed computational technique to reduce these types of artifacts is Independent Component Analysis (ICA). ICA estimates statistically independent components (ICs) that, when linearly combined, closely match the input (sensor) data. Removing the ICs that represent artifact sources and re-mixing the sources returns the input data with reduced noise activity. ICs of real-world data are usually not perfectly separated, actual sources, but a mixture of these sources. Adding additional input signals, predominantly generated by a single IC that is already part of the original sensor data, should increase that IC's separability. We conducted this study to evaluate this concept for ICA-based electromyogenic artifact reduction in EEG using EMG signals as additional inputs. To acquire the appropriate data we worked with nine human volunteers. The EEG and EMG were recorded while the study volunteers performed seven exercises designed to produce a wide range of representative myogenic artifacts. To evaluate the effect of the EMG signals we estimated the sources of each dataset once with and once without the EMG data. The ICs were automatically classified as either `myogenic' or `non-myogenic'. We removed the former before back projection. Afterwards we calculated an objective measure to quantify the artifact reduction and assess the effect of including EMG signals. Our study showed that the ICA-based reduction of electromyogenic artifacts can be improved by including the EMG data of artifact-inducing muscles. This approach could prove beneficial for locomotor disorder research, brain-computer interfaces, neurofeedback, and most other areas where brain activity during movement has to be analyzed.
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Leutheuser H, Gradl S, Kugler P, Anneken L, Arnold M, Achenbach S, Eskofier BM. Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:2690-3. [PMID: 25570545 DOI: 10.1109/embc.2014.6944177] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.
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Heldberg BE, Kautz T, Leutheuser H, Hopfengartner R, Kasper BS, Eskofier BM. Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2015:5593-5596. [PMID: 26737560 DOI: 10.1109/embc.2015.7319660] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days. A wearable sensor system, e.g. a wristband, serving as diagnostic tool or event monitor, would allow unobtrusive ambulatory long-term monitoring while reducing costs. Previous studies showed that seizures with motor symptoms such as generalized tonic-clonic seizures can be detected by measuring the electrodermal activity (EDA) and motion measuring acceleration (ACC). In this study, EDA and ACC from 8 patients were analyzed. In extension to previous studies, different types of seizures, including seizures without motor activity, were taken into account. A hierarchical classification approach was implemented in order to detect different types of epileptic seizures using data from wearable sensors. Using a k-nearest neighbor (kNN) classifier an overall sensitivity of 89.1% and an overall specificity of 93.1% were achieved, for seizures without motor activity the sensitivity was 97.1% and the specificity was 92.9%. The presented method is a first step towards a reliable ambulatory monitoring system for epileptic seizures with and without motor activity.
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Leutheuser H, Heyde C, Gollhofer A, Eskofier BM. Comparison of a priori calibration models for respiratory inductance plethysmography during running. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:6393-6. [PMID: 25571459 DOI: 10.1109/embc.2014.6945091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Respiratory inductive plethysmography (RIP) has been introduced as an alternative for measuring ventilation by means of body surface displacement (diameter changes in rib cage and abdomen). Using a posteriori calibration, it has been shown that RIP may provide accurate measurements for ventilatory tidal volume under exercise conditions. Methods for a priori calibration would facilitate the application of RIP. Currently, to the best knowledge of the authors, none of the existing ambulant procedures for RIP calibration can be used a priori for valid subsequent measurements of ventilatory volume under exercise conditions. The purpose of this study is to develop and validate a priori calibration algorithms for ambulant application of RIP data recorded in running exercise. We calculated Volume Motion Coefficients (VMCs) using seven different models on resting data and compared the root mean squared error (RMSE) of each model applied on running data. Least squares approximation (LSQ) without offset of a two-degree-of-freedom model achieved the lowest RMSE value. In this work, we showed that a priori calibration of RIP exercise data is possible using VMCs calculated from 5 min resting phase where RIP and flowmeter measurements were performed simultaneously. The results demonstrate that RIP has the potential for usage in ambulant applications.
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Mullan P, Kanzler CM, Lorch B, Schroeder L, Winkler L, Laich L, Riedel F, Richer R, Luckner C, Leutheuser H, Eskofier BM, Pasluosta C. Unobtrusive heart rate estimation during physical exercise using photoplethysmographic and acceleration data. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2015:6114-6117. [PMID: 26737687 DOI: 10.1109/embc.2015.7319787] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Photoplethysmography (PPG) is a non-invasive, inexpensive and unobtrusive method to achieve heart rate monitoring during physical exercises. Motion artifacts during exercise challenge the heart rate estimation from wrist-type PPG signals. This paper presents a methodology to overcome these limitation by incorporating acceleration information. The proposed algorithm consisted of four stages: (1) A wavelet based denoising, (2) an acceleration based denoising, (3) a frequency based approach to estimate the heart rate followed by (4) a postprocessing step. Experiments with different movement types such as running and rehabilitation exercises were used for algorithm design and development. Evaluation of our heart rate estimation showed that a mean absolute error 1.96 bpm (beats per minute) with standard deviation of 2.86 bpm and a correlation of 0.98 was achieved with our method. These findings suggest that the proposed methodology is robust to motion artifacts and is therefore applicable for heart rate monitoring during sports and rehabilitation.
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Heyde C, Leutheuser H, Eskofier B, Roecker K, Gollhofer A. Respiratory inductance plethysmography-a rationale for validity during exercise. Med Sci Sports Exerc 2014; 46:488-95. [PMID: 24042313 DOI: 10.1249/mss.0000000000000130] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION The aim of this study was to provide a rationale for future validations of a priori calibrated respiratory inductance plethysmography (RIP) when used under exercise conditions. Therefore, the validity of a posteriori-adjusted gain factors and accuracy in resultant breath-by-breath RIP data recorded under resting and running conditions were examined. METHODS Healthy subjects, 98 men and 88 women (mean ± SD: height = 175.6 ± 8.9 cm, weight = 68.9 ± 11.1 kg, age = 27.1 ± 8.3 yr), underwent a standardized test protocol, including a period of standing still, an incremental running test on treadmill, and multiple periods of recovery. Least square regression was used to calculate gain factors, respectively, for complete individual data sets as well as several data subsets. In comparison with flowmeter data, the validity of RIP in breathing rate (fR) and inspiratory tidal volume (VTIN) were examined using coefficients of determination (R). Accuracy was estimated from equivalence statistics. RESULTS Calculated gains between different data subsets showed no equivalence. After gain adjustment for the complete individual data set, fR and VTIN between methods were highly correlated (R = 0.96 ± 0.04 and 0.91 ± 0.05, respectively) in all subjects. Under conditions of standing still, treadmill running, and recovery, 86%, 98%, and 94% (fR) and 78%, 97%, and 88% (VTIN), respectively, of all breaths were accurately measured within ± 20% limits of equivalence. CONCLUSION In case of the best possible gain adjustment, RIP confidentially estimates tidal volume accurately within ± 20% under exercise conditions. Our results can be used as a rationale for future validations of a priori calibration procedures.
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Affiliation(s)
- Christian Heyde
- 1Department of Sport and Sport Science, Albert Ludwigs University of Freiburg, Freiburg, GERMANY; 2Digital Sports Group, Pattern Recognition Lab, University of Erlangen-Nürnberg, Nürnberg, GERMANY; and 3Applied Public Health, Furtwangen University, Furtwangen, GERMANY
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Gradl S, Leutheuser H, Kugler P, Biermann T, Kreil S, Kornhuber J, Bergner M, Eskofier B. Somnography using unobtrusive motion sensors and Android-based mobile phones. Annu Int Conf IEEE Eng Med Biol Soc 2014; 2013:1182-5. [PMID: 24109904 DOI: 10.1109/embc.2013.6609717] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50% of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. Ten healthy male subjects were recorded during two consecutive nights. Sensors from the Shimmer platform were placed in the bed to record accelerometer data, while reference hypnograms were collected using a SOMNOwatch system. A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals. The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness, REM and non-REM sleep. Additionally the algorithm was implemented on an Android mobile phone. Averaged over all subjects, the classifier had a mean accuracy of 79.0 % (SD 9.2%) for the three classes. The mobile phone implementation was able to run in realtime during all experiments. In future this will lead to a method for simple and unobtrusive somnography using mobile phones.
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Leutheuser H, Schuldhaus D, Eskofier BM. Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS One 2013; 8:e75196. [PMID: 24130686 PMCID: PMC3793992 DOI: 10.1371/journal.pone.0075196] [Citation(s) in RCA: 114] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Accepted: 08/11/2013] [Indexed: 11/19/2022] Open
Abstract
Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research, the dataset can be used in the evaluation process of new classification algorithms and could speed up the process of getting the best performing and most appropriate DLA classification system.
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Affiliation(s)
- Heike Leutheuser
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
- * E-mail: (HL); (DS)
| | - Dominik Schuldhaus
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
- * E-mail: (HL); (DS)
| | - Bjoern M. Eskofier
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
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Leutheuser H, Gabsteiger F, Hebenstreit F, Reis P, Lochmann M, Eskofier B. Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013:6804-6807. [PMID: 24111306 DOI: 10.1109/embc.2013.6611119] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment. Nevertheless, EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes. Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data. We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algorithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.
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