Smartphone Recordings Research Articles

Empowering patients: leveraging the untapped potential of smartphone recordings in dermatology consultations.

During consultations, patients can be overwhelmed with new diagnoses, complex treatment plans, particularly when bad news is broken (such as a new skin cancer diagnosis). Patients may be unable to recall nuanced medical information, particularly if elderly, attending on their own, with lower literacy, with impaired sensory abilities and where English may not be their native language. Given the ubiquity of smartphones, recording conversations during consultations—with the doctor’s consent—has become an accessible tool for patients. Allowing patients to record their consultations can empower them, enhance recall, and reduce decisional regret, particularly in emotionally charged settings such as skin cancer clinics where cognitive processing may be impaired.

Multisensory Fusion for Unsupervised Spatiotemporal Speaker Diarization.

Speaker diarization consists of answering the question of "who spoke when" in audio recordings. In meeting scenarios, the task of labeling audio with the corresponding speaker identities can be further assisted by the exploitation of spatial features. This work proposes a framework designed to assess the effectiveness of combining speaker embeddings with Time Difference of Arrival (TDOA) values from available microphone sensor arrays in meetings. We extract speaker embeddings using two popular and robust pre-trained models, ECAPA-TDNN and X-vectors, and calculate the TDOA values via the Generalized Cross-Correlation (GCC) method with Phase Transform (PHAT) weighting. Although ECAPA-TDNN outperforms the Xvectors model, we utilize both speaker embedding models to explore the potential of employing a computationally lighter model when spatial information is exploited. Various techniques for combining the spatial-temporal information are examined in order to determine the best clustering method. The proposed framework is evaluated on two multichannel datasets: the AVLab Speaker Localization dataset and a multichannel dataset (SpeaD-M3C) enriched in the context of the present work with supplementary information from smartphone recordings. Our results strongly indicate that the integration of spatial information can significantly improve the performance of state-of-the-art deep learning diarization models, presenting a 2-3% reduction in DER compared to the baseline approach on the evaluated datasets.

정상 성인에서 스마트폰 녹음을 위한 마이크 유형 간 음향학적 측정치 비교*

정상 성인에서 스마트폰 녹음을 위한 마이크 유형 간 음향학적 측정치 비교*

Smartphone-Derived Seismocardiography: Robust Approach for Accurate Cardiac Energy Assessment in Patients with Various Cardiovascular Conditions.

Seismocardiography (SCG), a method for measuring heart-induced chest vibrations, is gaining attention as a non-invasive, accessible, and cost-effective approach for cardiac pathologies, diagnosis, and monitoring. This study explores the integration of SCG acquired through smartphone technology by assessing the accuracy of metrics derived from smartphone recordings and their consistency when performed by patients. Therefore, we assessed smartphone-derived SCG's reliability in computing median kinetic energy parameters per record in 220 patients with various cardiovascular conditions. The study involved three key procedures: (1) simultaneous measurements of a validated hardware device and a commercial smartphone; (2) consecutive smartphone recordings performed by both clinicians and patients; (3) patients' self-conducted home recordings over three months. Our findings indicate a moderate-to-high reliability of smartphone-acquired SCG metrics compared to those obtained from a validated device, with intraclass correlation (ICC) > 0.77. The reliability of patient-acquired SCG metrics was high (ICC > 0.83). Within the cohort, 138 patients had smartphones that met the compatibility criteria for the study, with an observed at-home compliance rate of 41.4%. This research validates the potential of smartphone-derived SCG acquisition in providing repeatable SCG metrics in telemedicine, thus laying a foundation for future studies to enhance the precision of at-home cardiac data acquisition.

Examining recording quality from two methods of remote data collection in a study of vowel reduction

Remote recording quality of speech data varies significantly by recording devices, formats, and platforms, and past work has asserted that fine-grained sociolinguistic work should not be conducted remotely, while broad questions, such as analyses of the relative position of phonemes in the vowel space, may be amendable to remote data collection. In this study, lossless offline remote recordings taken via smartphone and lossy web-based recording performed over Gorilla are compared to traditional laboratory recordings in order to determine how accurately the remote options replicate a study of English vowel reduction. Four measures of reduction are examined: Relative duration, Euclidean distance, Pillai scores, and normalized formant values of stressed and unstressed vowels. Temporal analyses and Pillai scores were unaffected by recording method, while Euclidean distance and formant values exhibited some statistically significant changes but remained largely in line with laboratory data. These findings indicate that remote offline recording via smartphone or Gorilla may hold promise for studying vowel reduction and other phenomena requiring a similar degree of precision in formant analysis, but researchers should be aware of the specific distortions likely to be incurred with each method, with smartphone recordings having a stronger impact than Gorilla on low and back vowels.

Errors in Metered Dose Inhaler Use Amongst Pediatric Asthma Patients.

The aim of this paper is to use easily accessible smartphones as a straightforward means for physicians to objectively check Medical Device Inhaler (MDI) technique, without the need for additional devices. Additionally, we seek to assess the frequency of inhaler technique errors and their impact on asthma control. Thirty-two children between the ages of 5 and 18 receiving asthma therapy through MDIs were included. Three surveys were administered to all participants to gauge device history, asthma control, and patient characteristics. Patient technique was scored using inhaler audio signals recorded with a smartphone. For subjects that were able, forced oscillation technique (FOT) was performed during tidal breathing conditions before and after corticosteroid administration. 81% (25/31) of participants used their MDIs incorrectly with the most common errors being rapid shallow breathing, inadequate breath-holding, and excessive actuations. Poor inhaler technique correlated with poorly controlled asthma symptoms. The use of smartphone recordings can a convenient way to evaluate technique errors and could allow patients to demonstrate and refine their technique and usage without a doctor's visit, ensuring proper technique and enhancing treatment effectiveness.

Indirectness in Female Conversations: Exploring Differences in Interactions with Females and Males in Mosuli Arabic Conversations

This study explores the use of indirectness in female conversations, specifically examining the differences in indirectness when interacting with females and males. The hypothesis posits that females employ indirectness differently based on the gender of their interlocutors and that politeness serves as the main motivation for using indirect communication. The research aims to investigate the patterns of indirectness in informal everyday conversations. Data were collected from Mosuli Arabic participants, aged 30-50, with similar social backgrounds but varying educational levels. The study adopts Holtgraves' speech act theory (2002) and Grice's cooperative principle (1975) as the theoretical framework for data analysis. The analysis focuses on 12 selected extracts from smartphone recordings of family, friends, and relative gatherings. Findings reveal that females employ indirectness in distinct ways when interacting with females and males. Politeness, particularly positive politeness, emerges as a primary motivation for using indirect communication in both contexts. In female-female interactions, the nature of the relationship between interlocutors plays a significant role, leading to indirectness being used for intensity, politeness, or even shaming. Cultural factors influence the occurrence of impolite and shaming messages among Arab females. Findings also reveal that in interactions with males, politeness remains prominent, but indirectness is also employed for criticism and defending others. The use of indirectness with males appears to be less influenced by the relationship or age between interlocutors. These findings highlight the complex nature of indirect communication, which is influenced by factors like politeness, dynamics within relationships, and cultural influences.

Impact of naturalistic smartphone positioning on acoustic measures of voicea).

Smartphone technology has been used for at-home health monitoring, but there are few available applications (apps) for tracking acoustic measures of voice for those with chronic voice problems. Current apps limit the user by restricting the range of smartphone positions to those that are unnatural and non-interactive. Therefore, we aimed to understand how more natural smartphone positions impacted the accuracy of acoustic measures in comparison to clinically acquired and derived measures. Fifty-six adults (11 vocally healthy, 45 voice disordered, aged 18-80 years) completed voice recordings while holding their smartphones in four different positions (e.g., as if reading from the phone, up to the ear, etc.) while a head-mounted high-quality microphone attached to a handheld acoustic recorder simultaneously captured voice recordings. Comparisons revealed that mean fundamental frequency (Hz), maximum phonation time (s), and cepstral peak prominence (CPP; dB) were not impacted by phone position; however, CPP was significantly lower on smartphone recordings than handheld recordings. Spectral measures (low-to-high spectral ratio, harmonics-to-noise ratio) were impacted by the phone position and the recording device. These results indicate that more natural phone positions can be used to capture specific voice measures, but not all are directly comparable to clinically derived values.

Digital Literacy Inherency within Narratives Subject at SMP Kristen Palangka Raya

Our intention of this research is to show the research subjects' narratives, namely teachers who teach at Palangka Raya Christian Junior High School, Central Kalimantan, when dealing with the governance and presence of digital literacy. Digital literacy has become a serious discourse in the form of important cabinet government programs (2019-2024). We used interview method as our qualitative research method by searching for data in the form of narratives to the research subjects. Narratives in audio form (smartphone recording) are then transcribed into verbatim transcripts and developed based on the reality of narratives in the field. Based on the narrative reality at field level, the author found that although digital literacy experiences obstacles in terms of facilities, there is hope that digital literacy will get room to move not only in schools, but also outside schools.

Smartphone Recordings are Comparable to “Gold Standard” Recordings for Acoustic Measurements of Voice

PurposeThe purpose of this study was to assess the relationship and comparability of cepstral and spectral measures of voice obtained from a high-cost “flat” microphone and precision sound level meter (SLM) vs. high-end and entry level models of commonly and currently used smartphones (iPhone i12 and iSE; Samsung s21 and s9 smartphones). Device comparisons were also conducted in different settings (sound-treated booth vs. typical “quiet” office room) and at different mouth-to-microphone distances (15 and 30 cm). MethodsThe SLM and smartphone devices were used to record a series of speech and vowel samples from a prerecorded diverse set of 24 speakers representing a wide range of sex, age, fundamental frequency (F0), and voice quality types. Recordings were analyzed for the following measures: smoothed cepstral peak prominence (CPP in dB); the low vs high spectral ratio (L/H Ratio in dB); and the Cepstral Spectral Index of Dysphonia (CSID). ResultsA strong device effect was observed for L/H Ratio (dB) in both vowel and sentence contexts and for CSID in the sentence context. In contrast, device had a weak effect on CPP (dB), regardless of context. Recording distance was observed to have a small-to-moderate effect on measures of CPP and CSID but had a negligible effect on L/H Ratio. With the exception of L/H Ratio in the vowel context, setting was observed to have a strong effect on all three measures. While these aforementioned effects resulted in significant differences between measures obtained with SLM vs. smartphone devices, the intercorrelations of the measurements were extremely strong (r's > 0.90), indicating that all devices were able to capture the range of voice characteristics represented in the voice sample corpus. Regression modeling showed that acoustic measurements obtained from smartphone recordings could be successfully converted to comparable measurements obtained by a "gold standard" (precision SLM recordings conducted in a sound-treated booth at 15 cm) with small degrees of error. ConclusionsThese findings indicate that a variety of commonly available modern smartphones can be used to collect high quality voice recordings usable for informative acoustic analysis. While device, setting, and distance can have significant effects on acoustic measurements, these effects are predictable and can be accounted for using regression modeling.

Classification of Parkinson's disease from smartphone recording data using time-frequency analysis and convolutional neural network.

Parkinson's disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affects most patients is voice impairment. Voice samples are non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible medical self-diagnosis tool by using only one-second voice recording. The data from one of the largest mobile PD studies, the mPower study, was used. A total of 29,798 ten-second voice recordings on smartphone from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by participants saying /aa/ for ten seconds into an iPhone microphone. A dataset comprising 385,143 short one-second audio samples was generated from the original ten-second voice recordings. The samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. We achieve a respectable classification performance using a generalized approach on a dataset with a large number of samples. The result emphasizes that an analysis based on one-second clip recorded on a smartphone could be a promising non-invasive and remotely available PD biomarker.

Channel Intensity and Edge-Based Estimation of Heart Rate via Smartphone Recordings

Smartphones, today, come equipped with a wide variety of sensors and high-speed processors that can capture, process, store, and communicate different types of data. Coupled with their ubiquity in recent years, these devices show potential as practical and portable healthcare monitors that are both cost-effective and accessible. To this end, this study focuses on examining the feasibility of smartphones in estimating the heart rate (HR), using video recordings of the users’ fingerprints. The proposed methodology involves two-stage processing that combines channel-intensity-based approaches (Channel-Intensity mode/Counter method) and a novel technique that relies on the spatial and temporal position of the recorded fingerprint edges (Edge-Detection mode). The dataset used here included 32 fingerprint video recordings taken from 6 subjects, using the rear camera of 2 smartphone models. Each video clip was first validated to determine whether it was suitable for Channel-Intensity mode or Edge-Detection mode, followed by further processing and heart rate estimation in the selected mode. The relative accuracy for recordings via the Edge-Detection mode was 93.04%, with a standard error of estimates (SEE) of 6.55 and Pearson’s correlation r > 0.91, while the Channel-Intensity mode showed a relative accuracy of 92.75%, with an SEE of 5.95 and a Pearson’s correlation r > 0.95. Further statistical analysis was also carried out using Pearson’s correlation test and the Bland–Altman method to verify the statistical significance of the results. The results thus show that the proposed methodology, through smartphones, is a potential alternative to existing technologies for monitoring a person’s heart rate.

A Preliminary Investigation of the Reliability of Acoustic Parameters of Voice through Smartphone Recordings in Individuals with Dysphonia.

Telepractice offers the opportunity to receive care at home without risk of exposure to healthcare acquired infections, especially during a pandemic. Hence, establishing the reliability of the diagnosis of dysphonia via a smartphone is fundamental to providing an alternative service delivery model. A total of 20 participants participated in the study. Recordings of sentence-based voice samples were done using a standardized microphone and the software used in labs and on smartphones. Comparisons were made of acoustic and perceptual voice in real-time and recorded samples speech in persons with typical vs pathological voice. Results revealed no significant differences perceptually between real-time voice and recorded voice in individuals with typical and pathological voices. In acoustic analysis, there was no significant difference in Fundamental frequency (F0) and Auditory Voice Quality Index (AVQI) between real-time voice and recorded voice in individuals with typical and pathological voice.

Extreme racism-related events and poor sleep in African-American women

RationaleMuch of the research linking racism-related stressors to poor health has focused on fairly non-violent forms of racism that directly impact individuals under study. Exposure to particularly extreme and/or violent racist events are increasingly visible via smartphone recordings and social media, with consistent anecdotal reports of the effects of seeing and hearing about these events on sleep among minorities who racially identify with the victims. ObjectiveThis study examines whether exposure to direct and vicarious racism-related events (RREs), including more extreme events, are associated with sleep quality. Additionally, we examine effects of less and more violent direct RREs and vicarious RREs witnessed in person and via social media. MethodsAmong 422 African-American women, we assessed exposure to RREs using a modified version of the Race-Related Events Scale and assessed sleep quality using the Pittsburgh Sleep Quality Index (PSQI). Linear regression analyses were used to model continuous global sleep. ResultsDirect (β = 0.24 [95% CI: 0.13, 0.35]) RREs were associated with worse continuous global sleep quality scores in analyses adjusted for sociodemographics and risk factors for poor sleep. More violent direct RREs (β = 0.59 [95% CI: 0.30, 0.89]) had stronger associations with poor sleep quality than less violent direct RREs (β = 0.25 [95% CI: 0.11, 0.40]). Vicarious RREs overall (β = 0.04 [95% CI: 0.14, 0.21]) and those witnessed via social media (β = −0.07 [95% CI: 0.29, 0.14]) were not associated with global sleep quality; conversely, vicarious RREs witnessed in person were (β = 0.52 [95% CI: 0.21, 0.83]). ConclusionExtreme, direct experiences of racism, particularly those that are violent in nature, are associated with poor sleep quality. However, extreme vicarious experiences are not-- unless witnessed in person.

Extreme racism-related events and poor sleep in African-American women

RationaleMuch of the research linking racism-related stressors to poor health has focused on fairly non-violent forms of racism that directly impact individuals under study. Exposure to particularly extreme and/or violent racist events are increasingly visible via smartphone recordings and social media, with consistent anecdotal reports of the effects of seeing and hearing about these events on sleep among minorities who racially identify with the victims. ObjectiveThis study examines whether exposure to direct and vicarious racism-related events (RREs), including more extreme events, are associated with sleep quality. Additionally, we examine effects of less and more violent direct RREs and vicarious RREs witnessed in person and via social media. MethodsAmong 422 African-American women, we assessed exposure to RREs using a modified version of the Race-Related Events Scale and assessed sleep quality using the Pittsburgh Sleep Quality Index (PSQI). Linear regression analyses were used to model continuous global sleep. ResultsDirect (β = 0.24 [95% CI: 0.13, 0.35]) RREs were associated with worse continuous global sleep quality scores in analyses adjusted for sociodemographics and risk factors for poor sleep. More violent direct RREs (β = 0.59 [95% CI: 0.30, 0.89]) had stronger associations with poor sleep quality than less violent direct RREs (β = 0.25 [95% CI: 0.11, 0.40]). Vicarious RREs overall (β = 0.04 [95% CI: 0.14, 0.21]) and those witnessed via social media (β = −0.07 [95% CI: 0.29, 0.14]) were not associated with global sleep quality; conversely, vicarious RREs witnessed in person were (β = 0.52 [95% CI: 0.21, 0.83]). ConclusionExtreme, direct experiences of racism, particularly those that are violent in nature, are associated with poor sleep quality. However, extreme vicarious experiences are not-- unless witnessed in person.

A novel smartphone app for blood pressure measurement: a proof-of-concept study against an arterial catheter

Smartphones may provide a highly available access to simplified hypertension screening in environments with limited health care resources. Most studies involving smartphone blood pressure (BP) apps have focused on validation in static conditions without taking into account intraindividual BP variations. We report here the first experimental evidence of smartphone-derived BP estimation compared to an arterial catheter in a highly dynamic context such as induction of general anesthesia. We tested a smartphone app (OptiBP) on 121 patients requiring general anesthesia and invasive BP monitoring. For each patient, ten 1-min segments aligned in time with ten smartphone recordings were extracted from the continuous invasive BP. A total of 1152 recordings from 119 patients were analyzed. After exclusion of 2 subjects and rejection of 565 recordings due to BP estimation not generated by the app, we retained 565 recordings from 109 patients (acceptance rate 51.1%). Concordance rate (CR) and angular CR demonstrated values of more than 90% for systolic (SBP), diastolic (DBP) and mean (MBP) BP. Error grid analysis showed that 98% of measurement pairs were in no- or low-risk zones for SBP and MBP, of which more than 89% in the no-risk zone. Evaluation of accuracy and precision [bias ± standard deviation (95% limits of agreement)] between the app and the invasive BP was 0.0 ± 7.5 mmHg [− 14.9, 14.8], 0.1 ± 2.9 mmHg [− 5.5, 5.7], and 0.1 ± 4.2 mmHg [− 8.3, 8.4] for SBP, DBP and MBP respectively. To the best of our knowledge, this is the first time a smartphone app was compared to an invasive BP reference. Its trending ability was investigated in highly dynamic conditions, demonstrating high concordance and accuracy. Our study could lead the way for mobile devices to leverage the measurement of BP and management of hypertension.

The Potential and Limitations of Mobile Health and Insertable Cardiac Monitors in the Detection of Atrial Fibrillation in Cryptogenic Stroke Patients: Preliminary Results From the REMOTE Trial.

AimThis paper presents the preliminary results from the ongoing REMOTE trial. It aims to explore the opportunities and hurdles of using insertable cardiac monitors (ICMs) and photoplethysmography-based mobile health (PPG-based mHealth) using a smartphone or smartwatch to detect atrial fibrillation (AF) in cryptogenic stroke and transient ischemic attack (TIA) patients.Methods and ResultsCryptogenic stroke or TIA patients (n = 39) received an ICM to search for AF and were asked to use a blinded PPG-based mHealth application for 6 months simultaneously. They were randomized to smartphone or smartwatch monitoring. In total, 68,748 1-min recordings were performed using PPG-based mHealth. The number of mHealth recordings decreased significantly over time in both smartphone and smartwatch groups (p < 0.001 and p = 0.002, respectively). Insufficient signal quality was more frequently observed in smartwatch (43.3%) compared to smartphone recordings (17.8%, p < 0.001). However, when looking at the labeling of the mHealth recordings on a patient level, there was no significant difference in signal quality between both groups. Moreover, the use of a smartwatch resulted in significantly more 12-h periods (91.4%) that were clinically useful compared to smartphone users (84.8%) as they had at least one recording of sufficient signal quality. Simultaneously, continuous data was collected from the ICMs, resulting in approximately 6,660,000 min of data (i.e., almost a 100-fold increase compared to mHealth). The ICM algorithm detected AF and other cardiac arrhythmias in 10 and 19 patients, respectively. However, these were only confirmed after adjudication by the remote monitoring team in 1 (10%) and 5 (26.3%) patients, respectively. The confirmed AF was also detected by PPG-based mHealth.ConclusionBased on the preliminary observations, our paper illustrates the potential as well as the limitations of PPG-based mHealth and ICMs to detect AF in cryptogenic stroke and TIA patients in four elements: (i) mHealth was able to detect AF in a patient in which AF was confirmed on the ICM; (ii) Even state-of-the-art ICMs yielded many false-positive AF registrations; (iii) Both mHealth and ICM still require physician revision; and (iv) Blinding of the mHealth results impairs compliance and motivation.

Breaking Age Barriers With Automatic Voice-Based Depression Detection

Adults over the age of 60 years are a rising population at-risk for depression, and there is a need to create automatic screening for this illness. Most existing voice-based depression datasets comprise speakers younger than 60 and variations in speech due to age and depression are not well understood. In this study, which uses Patient Health Questionnaires for depression severity ground-truth, automatic depression detection is explored using acoustic-based prosodic, spectral, landmark, and voice quality features derived from smartphone recordings from 152 speakers in four different age ranges (e.g., 18–34, 35–48, 49–62, and 63–79). An age-dependent modeling paradigm for voice-based depression detection is proposed and evaluated. Results show that age-dependent models improve voice-based automatic depression classification accuracy with up to 10% absolute gains when compared with an age-agnostic model. Further, when compared with age-agnostic and gender-dependent models, age-dependent models often produced greater depressed class identification f-score sensitivity (up to 0.39 absolute gains). While automatically extracted acoustic voice features lead to statistically significant depression detection accuracy gains over the age-agnostic modeling baseline (4%–9% absolute), manually extracted voice quality features also are useful (4%–7% absolute gains over baseline). This investigation demonstrates the benefits of implementing age modeling to improve voice-based depression screening via smart devices.

Fasting blood glucose in a Ghanaian adult is causally affected by malaria parasite load: a mechanistic case study using convergent cross mapping

BackgroundAdults with diabetes mellitus (DM) in malaria-endemic areas might be more susceptible to Plasmodium infection than healthy individuals. Herein, the study was aimed at verifying the hypothesis that increased fasting blood glucose (FBG) promotes parasite growth as reflected by increased parasite density.MethodsSeven adults without DM were recruited in rural Ghana to determine the relationships between FBG and malaria parasite load. Socio-economic data were recorded in questionnaire-based interviews. Over a period of 6 weeks, FBG and Plasmodium sp. Infection were measured in peripheral blood samples photometrically and by polymerase chain reaction (PCR)-assays, respectively. Daily physical activity and weather data were documented via smartphone recording. For the complex natural systems of homeostatic glucose control and Plasmodium sp. life cycle, empirical dynamic modelling was applied.ResultsAt baseline, four men and three women (median age, 33 years; interquartile range, 30–48) showed a median FBG of 5.5 (5.1–6.0 mmol/L); one participant had an asymptomatic Plasmodium sp. infection (parasite density: 240/µL). In this participant, convergent cross mapping (CCM) for 34 consecutive days, showed that FBG was causally affected by parasite density (p < 0.02), while the reciprocal relationship was not discernible (p > 0.05). Additionally, daily ambient temperature affected parasite density (p < 0.01).ConclusionIn this study population living in a malaria-endemic area, time series analyses were successfully piloted for the relationships between FBG and Plasmodium sp. density. Longer observation periods and larger samples are required to confirm these findings and determine the direction of causality.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.