A Non-Contact Photoplethysmography Technique for the Estimation of Heart Rate via Smartphone

Maestre, Rodolfo; Rivera-Roman, Tomas A.; Fernandez‐Jaramillo, Arturo A.; Guerrón, Nancy; Olmedo, José Javier Serrano

doi:10.3390/app10010154

Cited by 14 publications

(15 citation statements)

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“…In general, a contact-less solution is desirable for this kind of application. Recent development in hardware and software technologies allowed to measure PPG in a contact-less manner, making this technique highly suitable for emotion recognition and social robots applications ( Wieringa, Mastik & Van der Steen, 2005 ; Wong, Pickwell-MacPherson & Zhang, 2010 ; Maestre-Rendon et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of state anxiety by machine learning applied to photoplethysmography data

Perpetuini

Chiarelli

Cardone

et al. 2021

PeerJ

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Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

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Section: Discussionmentioning

confidence: 99%

Prediction of state anxiety by machine learning applied to photoplethysmography data

Perpetuini

Chiarelli

Cardone

et al. 2021

PeerJ

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“…In [6], Maestre-Rendon et al developed smartphone applications to monitor a patient with a cardiovascular disease. Through the use of the camera of the mobile device and without direct contact with the patient, the pulses per minute (PPM) was obtained.…”

Section: Methodsmentioning

confidence: 99%

Special Issue on Signal Processing and Machine Learning for Biomedical Data

Raso

Cascio

2021

Applied Sciences

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“…Monitoring applications often require repeated sampling over a sustained period (from hours up to months) to assess appreciable differences in disease states, and therefore benefit from being low-cost and noninvasive. In recent years, SBI systems have been proposed for monitoring of vital signs, 103 – 109 blood glucose, 110 – 113 blood pressure, 114 , 115 blood oxygenation, 68 , 116 hemoglobin concentration, 72 atrial fibrilation, 117 , 118 jaundice, 73 , 97 , 119 , 120 skin cancer, 121 and diabetic foot ulcers. 55 , 122 All of these applications propose utilizing either contact-based or contactless optical measurements using the smartphone camera, most often by individuals on themselves (i.e., self-monitoring).…”

Section: Context and Applicationsmentioning

confidence: 99%

Smartphone-based imaging systems for medical applications: a critical review

2021

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. Significance: Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed. Aim: Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances. Approach: First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed. Results: The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo , multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications. Conclusions: A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forwar...

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A Non-Contact Photoplethysmography Technique for the Estimation of Heart Rate via Smartphone

Cited by 14 publications

References 14 publications

Prediction of state anxiety by machine learning applied to photoplethysmography data

Prediction of state anxiety by machine learning applied to photoplethysmography data

Special Issue on Signal Processing and Machine Learning for Biomedical Data

Smartphone-based imaging systems for medical applications: a critical review

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