Poster: SpiroMilli: Bringing Ad-hoc Spirometry to 5G Devices
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Poster: SpiroMilli: Bringing Ad-hoc Spirometry to 5G Devices Aakriti Adhikari ∗ ; Austin Hetherington ∗ ; Sanjib Sur Computer Science and Engineering; University of South Carolina, Columbia, USA ∗ (Co-primary author) {aakriti,austinth}@email.sc.edu;[email protected] Phase (Radian) Time (s) 0 1 2 3 0 1 2 3 4 5 Rx1 -2 -1 0 1 2 0 1 2 3 4 5 Rx2 1 2 3 4 5 0 1 2 3 4 5 Rx3 -2 -1 0 1 2 0 1 2 3 4 5 Rx4 Distance-based Transfer Function Beamforming and Reflector Tracking Deep Residual Decoder 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 3.5 4 Flow Rate (L/s) Volume (L) Ground-Truth - Sub. 1 Predicted - Sub. 1 Ground-Truth - Sub. 2 Predicted - Sub. 2 (c) (e) (f) -1 0 1 2 1 2 3 4 5 Phase (Radian) Time (s) Rx2 1 1.4 2.2 2.3 2.4 (b) (d) Distance-invariant 4-channel vibration 250x4 250x128 125x64 62x32 31x128 1X128 25 FEF 50 FEF 75 MMEF FEF PEF FEV1 FVC LSTM Linear FC Layer Feature Extraction Regression Key Spirometry Indicators (a) Exhalation Conv+LeakyRelu+Maxpooling 1X7 CNN-LSTM Regression Vibration Processing Figure 1: (a) Exhalation on a mmWave device; (b) Phase of the reflected signal shows tiny vibrations during airflow; (c) Time- synchronized vibration signals from multiple phased-array antennas; (d) Signal processing to improve fidelity, track moving reflectors, and estimate distance-invariant vibration; (e) CNN-LSTM architecture to map physical vibration to 7 key spirometry indicators; and (f) Predicted flow rate in comparison to ground-truth for two subjects. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile com- puting systems and tools; • Computing methodologies → Neu- ral networks. KEYWORDS Millimeter-Wave, Spirometers, Convolutional Neural Network ACM Reference Format: Aakriti Adhikari ∗ ; Austin Hetherington ∗ ; Sanjib Sur. 2021. Poster: SpiroMilli: Bringing Ad-hoc Spirometry to 5G Devices . In The 22nd International Workshop on Mobile Computing Systems and Applications (HotMobile ’21), February 24–26, 2021, Virtual, United Kingdom. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3446382.3448732 AD-HOC SPIROMETRY MOTIVATION The rapid evolution of the telehealth industry, accelerated recently by stay-at-home directives, has created a demand for more ubiqui- tous health-sensing tools. One such tool is the Spirometer. Spirome- ters have been used in traditional clinics to measure lung capacity (volume) as well as airflow (flow rate) and have wide applicability in the diagnosis of Asthma, COPD, and other pulmonary diseases. In addition, they can be used to diagnose Dyspnea, i.e., shortness of breath, one of the symptoms of the COVID-19 virus. Several spirometers are available commercially for home-use, but they are either costly, cumbersome or provide limited flow information [3]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). HotMobile ’21, February 24–26, 2021, Virtual, United Kingdom © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8323-3/21/02. https://doi.org/10.1145/3446382.3448732 We propose SpiroMilli, a low-barrier means to performing spirom- etry at home using the millimeter-wave (mmWave) technology in 5G-and-beyond devices. To perform a test, users will hold the device in front of their mouth, fully inhale, then sharply exhale. The system will then output seven key indicators, e.g., Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF), etc., along with a flow-volume curve (Fig. 1[f]). SpiroMilli’s key idea is intuitive: Strong airflow in front of the mmWave antenna creates tiny vibrations, and these vibrations affect the phase of reflected signals from nearby objects. For example, a 79 GHz device (wavelength: 3.79 mm) will register a 5 µm displacement as a 1 ◦ phase change. But SpiroMilli faces two primary challenges: (1) Phase changes should only derive from airflow vibrations, yet the sway motions of the hand and face may not allow for it; and (2) Even if the phase change is tracked accurately, a direct mapping between phase change to the seven key indicators and its corresponding flow- volume curve does not exist. SYSTEM DESIGN To overcome the challenges, SpiroMilli proposes two approaches: (1) Beamforming, reflector tracking, and distance calibrating mod- ules which afford us with accurate phase estimation, regardless of idiosyncrasies in the users’ movements; and (2) A machine-learning model that both learns and maps the vibration to key indicators and a flow-volume curve. First, assume that the user holds the device in a static position; the phase of the reflected signals remains static. However, when airflow strikes the surface, the device starts to vibrate. Time-variant changes in the device-to-reflector distance result in phase changes of ∆ϕ (t ) = 4π ∆d (t )/λ, where ∆d (t ) is the change in distance, and λ is the signal wavelength. Fig. 1(b) shows an example of such phase changes for a peak flow-rate of 4.67 L/s. We can remove the unde- sired effect of low-frequency hand sway movements on phase by applying a highpass filter. Then, combining signal beamforming and a novel reflector tracking algorithm, we ensure the source of
Transcript of Poster: SpiroMilli: Bringing Ad-hoc Spirometry to 5G Devices
Poster: SpiroMilli: Bringing Ad-hoc Spirometry to 5G DevicesAakriti Adhikari∗; Austin Hetherington∗; Sanjib Sur
Computer Science and Engineering; University of South Carolina, Columbia, USA∗(Co-primary author)
{aakriti,austinth}@email.sc.edu;[email protected]
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Figure 1: (a) Exhalation on a mmWave device; (b) Phase of the reflected signal shows tiny vibrations during airflow; (c) Time-synchronized vibration signals from multiple phased-array antennas; (d) Signal processing to improve fidelity, track movingreflectors, and estimate distance-invariant vibration; (e) CNN-LSTMarchitecture tomap physical vibration to 7 key spirometryindicators; and (f) Predicted flow rate in comparison to ground-truth for two subjects.
CCS CONCEPTS•Human-centered computing→Ubiquitous andmobile com-puting systems and tools; •Computingmethodologies→Neu-ral networks.
KEYWORDSMillimeter-Wave, Spirometers, Convolutional Neural Network
ACM Reference Format:Aakriti Adhikari∗; AustinHetherington∗; Sanjib Sur. 2021. Poster: SpiroMilli:Bringing Ad-hoc Spirometry to 5G Devices . In The 22nd InternationalWorkshop on Mobile Computing Systems and Applications (HotMobile ’21),February 24–26, 2021, Virtual, United Kingdom. ACM, New York, NY, USA,2 pages. https://doi.org/10.1145/3446382.3448732
AD-HOC SPIROMETRY MOTIVATIONThe rapid evolution of the telehealth industry, accelerated recentlyby stay-at-home directives, has created a demand for more ubiqui-tous health-sensing tools. One such tool is the Spirometer. Spirome-ters have been used in traditional clinics to measure lung capacity(volume) as well as airflow (flow rate) and have wide applicabilityin the diagnosis of Asthma, COPD, and other pulmonary diseases.In addition, they can be used to diagnose Dyspnea, i.e., shortnessof breath, one of the symptoms of the COVID-19 virus. Severalspirometers are available commercially for home-use, but they areeither costly, cumbersome or provide limited flow information [3].
Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).HotMobile ’21, February 24–26, 2021, Virtual, United Kingdom© 2021 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-8323-3/21/02.https://doi.org/10.1145/3446382.3448732
Wepropose SpiroMilli, a low-barriermeans to performing spirom-etry at home using the millimeter-wave (mmWave) technology in5G-and-beyond devices. To perform a test, users will hold the devicein front of their mouth, fully inhale, then sharply exhale. The systemwill then output seven key indicators, e.g., Forced Vital Capacity(FVC), Peak Expiratory Flow (PEF), etc., along with a flow-volumecurve (Fig. 1[f]). SpiroMilli’s key idea is intuitive: Strong airflow infront of the mmWave antenna creates tiny vibrations, and thesevibrations affect the phase of reflected signals from nearby objects.For example, a 79 GHz device (wavelength: 3.79 mm) will register a5 µm displacement as a 1◦ phase change.
But SpiroMilli faces two primary challenges: (1) Phase changesshould only derive from airflow vibrations, yet the sway motionsof the hand and face may not allow for it; and (2) Even if thephase change is tracked accurately, a direct mapping between phasechange to the seven key indicators and its corresponding flow-volume curve does not exist.
SYSTEM DESIGNTo overcome the challenges, SpiroMilli proposes two approaches:(1) Beamforming, reflector tracking, and distance calibrating mod-ules which afford us with accurate phase estimation, regardless ofidiosyncrasies in the users’ movements; and (2) A machine-learningmodel that both learns and maps the vibration to key indicatorsand a flow-volume curve.
First, assume that the user holds the device in a static position;the phase of the reflected signals remains static. However, whenairflow strikes the surface, the device starts to vibrate. Time-variantchanges in the device-to-reflector distance result in phase changesof ∆ϕ(t) = 4π∆d(t)/λ, where ∆d(t) is the change in distance, and λis the signal wavelength. Fig. 1(b) shows an example of such phasechanges for a peak flow-rate of 4.67 L/s. We can remove the unde-sired effect of low-frequency hand sway movements on phase byapplying a highpass filter. Then, combining signal beamformingand a novel reflector tracking algorithm, we ensure the source of
HotMobile ’21, February 24–26, 2021, Virtual, United Kingdom Aakriti Adhikari∗ ; Austin Hetherington∗ ; Sanjib Sur
the strongest reflections remains the same throughout the measure-ment. In the future, we will adopt a distance-based transfer functionso phase changes can accurately predict the flow rate passing atthe lips regardless of device distance.
Second, the relationship between device vibration and spirome-try indicators is complex and has never been explored before. Weapply a Convolutional Neural Network that learns the representa-tive features in the vibration with LSTM to capture the temporaldependency and variations [2]. CNN-LSTM can then be used tomap physical vibrations to the seven key indicators. In addition tothe key indicators, clinicians also use the flow-volume curve as adiagnostic tool [3]. To this end, we have used a deep residual de-coder architecture and open-source data from the CDC containing155,000 spirometry tests [1] to learn the mapping between the keyindicators and its flow-volume curve. Figs. 1(e–f) show the CNN-LSTM architecture along with the predicted flow-volume graphs
for two experimental subjects. In the future, we will evaluate ourmethod with different device form factors, multiple subjects, andunder various environmental conditions.
ACKNOWLEDGEMENTThis work was supported in part by the US National Science Foun-dation through NSF CNS-1910853.
REFERENCES[1] CDC. 2014. NHANES Comprehensive Data List. (2014). https://wwwn.cdc.gov/
nchs/nhanes/search/datapage.aspx[2] Dwaipayan Biswas and Luke Everson and Muqing Liu and Madhuri Panwar and
Bram-Ernst Verhoef and Shrishail Patki and Chris H. Kim and Amit Acharyya andChris Van Hoof and Mario Konijnenburg and Nick Van Helleputte. 2019. CorNET:Deep Learning Framework for PPG-Based Heart Rate Estimation and BiometricIdentification in Ambulant Environment. IEEE Transactions on Biomedical Circuitsand Systems 13, 2 (2019).
[3] MFI Medical. 2020. Spirometry, Oximetry and Mobile-Health.https://www.spirometry.com/. (2020).
