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Nguyen DT, Zeng Q, Tian X, Chia P, Wu C, Liu Y, Ho JS. Ambient health sensing on passive surfaces using metamaterials. SCIENCE ADVANCES 2024; 10:eadj6613. [PMID: 38181071 PMCID: PMC10776016 DOI: 10.1126/sciadv.adj6613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 12/01/2023] [Indexed: 01/07/2024]
Abstract
Ambient sensors can continuously and unobtrusively monitor a person's health and well-being in everyday settings. Among various sensing modalities, wireless radio-frequency sensors offer exceptional sensitivity, immunity to lighting conditions, and privacy advantages. However, existing wireless sensors are susceptible to environmental interference and unable to capture detailed information from multiple body sites. Here, we present a technique to transform passive surfaces in the environment into highly sensitive and localized health sensors using metamaterials. Leveraging textiles' ubiquity, we engineer metamaterial textiles that mediate near-field interactions between wireless signals and the body for contactless and interference-free sensing. We demonstrate that passive surfaces functionalized by these metamaterials can provide hours-long cardiopulmonary monitoring with accuracy comparable to gold standards. We also show the potential of distributed sensors and machine learning for continuous blood pressure monitoring. Our approach enables passive environmental surfaces to be harnessed for ambient sensing and digital health applications.
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Affiliation(s)
- Dat T. Nguyen
- Integrative Sciences and Engineering Program, National University of Singapore, Singapore 119077, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Qihang Zeng
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Xi Tian
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
| | - Patrick Chia
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
| | - Changsheng Wu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
| | - Yuxin Liu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
| | - John S. Ho
- Integrative Sciences and Engineering Program, National University of Singapore, Singapore 119077, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- SIA-NUS Digital Aviation Corporate Laboratory, National University of Singapore, Singapore 117602, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
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Zhang Z, Kan EC. Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition. IEEE SENSORS JOURNAL 2023; 23:20116-20128. [PMID: 38510062 PMCID: PMC10950291 DOI: 10.1109/jsen.2023.3294329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.
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Affiliation(s)
- Zijing Zhang
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Edwin C Kan
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
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Zhang Z, Conroy TB, Krieger AC, Kan EC. Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors. IEEE Trans Biomed Eng 2023; 70:1208-1218. [PMID: 37815956 DOI: 10.1109/tbme.2022.3212619] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
OBJECTIVE Respiratory disturbances during sleep are a prevalent health condition that affects a large adult population. The gold standard to evaluate sleep disorders including apnea is overnight polysomnography, which requires a trained technician for live monitoring and post-processing scoring. Currently, the disorder events can hardly be predicted using the respiratory waveforms preceding the events. The objective of this paper is to develop an autonomous system to detect and predict respiratory events reliably based on real-time covert sensing. METHODS A bed-integrated radio-frequency (RF) sensor by near-field coherent sensing (NCS) was employed to retrieve continuous respiratory waveforms without user's awareness. Overnight recordings were collected from 27 patients in the Weill Cornell Center for Sleep Medicine. We extracted respiratory features to feed into the random-forest machine learning model for disorder detection and prediction. The technician annotation, derived from observation by polysomnography, was used as the ground truth during the supervised learning. RESULTS Apneic event detection achieved a sensitivity and specificity up to 88.6% and 89.0% for k-fold validation, and 83.1% and 91.6% for subject-independent validation. Prediction of forthcoming apneic events could be made up to 90 s in advance. Apneic event prediction achieved a sensitivity and specificity up to 81.3% and 82.1% for k-fold validation, and 80.5% and 82.4% for subject-independent validation. The most important features for event detection and prediction can be assessed in the learning model. CONCLUSION A bed-integrated RF sensor can covertly and reliably detect and predict apneic events. SIGNIFICANCE Predictive warning of the sleep disorders in advance can intervene serious apnea, especially for infants, servicemen, and patients with chronic conditions.
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Sharma P, Zhang Z, Conroy TB, Hui X, Kan EC. Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:8047. [PMID: 36298396 PMCID: PMC9610852 DOI: 10.3390/s22208047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/13/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
This work presents a study on users' attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user's baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively.
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Zhang Z, Sharma P, Conroy TB, Phongtankuel V, Kan EC. Objective Scoring of Physiologically Induced Dyspnea by Non-Invasive RF Sensors. IEEE Trans Biomed Eng 2022; 69:432-442. [PMID: 34255624 PMCID: PMC8743005 DOI: 10.1109/tbme.2021.3096462] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Dyspnea, also known as the patient's feeling of difficult or labored breathing, is one of the most common symptoms for respiratory disorders. Dyspnea is usually self-reported by patients using, for example, the Borg scale from 0 - 10, which is however subjective and problematic for those who refuse to cooperate or cannot communicate. The objective of this paper was to develop a learning-based model that can evaluate the correlation between the self-report Borg score and the respiratory metrics for dyspnea induced by exertion and increased airway resistance. METHODS A non-invasive wearable radio-frequency sensor by near-field coherent sensing was employed to retrieve continuous respiratory data with user comfort and convenience. Self-report dyspnea scores and respiratory features were collected on 32 healthy participants going through various physical and breathing exercises. A machine learning model based on the decision tree and random forest then produced an objective dyspnea score. RESULTS For unseen data as well as unseen participants, the objective dyspnea score can be in reasonable agreement with the self-report score, and the importance factor of each respiratory metrics can be assessed. CONCLUSION An objective dyspnea score can potentially complement or substitute the self-report for physiologically induced dyspnea. SIGNIFICANCE The method can potentially formulate a baseline for clinical dyspnea assessment and help caregivers track dyspnea continuously, especially for patients who cannot report themselves.
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Affiliation(s)
- Zijing Zhang
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Pragya Sharma
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Thomas Bradley Conroy
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Veerawat Phongtankuel
- Geriatrics and Palliative Medicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Edwin C. Kan
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
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Hui X, Zhou J, Sharma P, Conroy TB, Zhang Z, Kan EC. Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:756-764. [PMID: 34310320 DOI: 10.1109/tbcas.2021.3099865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild coughs are usually overlooked in the early stage, not only because they are barely noticeable by the person and the people around, but also because the present recording method is not comfortable, private, or reliable for long-term monitoring. In this paper, a wearable radio-frequency (RF) sensor is presented to recognize the mild cough signal directly from the local trachea vibration characteristics, and can isolate interferences from nearby people. The sensor operates at the ultra-high-frequency band, and can couple the RF energy to the upper respiratory track by the near field of the sensing antenna. The retrieved tissue vibration caused by the cough airflow burst can then be analyzed by a convolutional neural network trained on the frequency-time spectra. The sensing antenna design is analyzed for performance improvement. During the human study of 5 participants over 100 minutes of prescribed routines, the overall recognition ratio is above 90% and the false positive ratio during other routines is below 2.09%.
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