Electronic Stethoscope Research Articles

PS-C10-7: A NOVEL PROFESSIONAL AUTOMATED AUSCULTATORY BLOOD PRESSURE MONITOR FOR OFFICE USE WITH VISUAL DISPLAY OF KOROTKOFF SOUNDS: INBODY BPBIO480KV VALIDATION

Objective: A novel professional automated auscultatory upper arm-cuff blood pressure (BP) monitor (InBody BPBIO480KV) for use in the office or clinic by healthcare professionals was developed. The device records the Korotkoff sounds using an electronic stethoscope embedded to the cuff, which are reproduced and graphically displayed on the device screen during cuff deflation, and automatically interpreted to report systolic and diastolic BP values. This validation study assessed the accuracy of the device in general population according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Universal Standard (ISO 81060-2:2018) and the ISO Amendment 1.2020-01. Design and Method: Subjects were recruited to fulfil the age, gender, BP, arm size and cuff distribution criteria of the AAMI/ESH/ISO Universal Standard in general population using the same arm sequential BP measurement method. Three cuffs of the test device were used for arm circumference 23–28, 28–35 and 33–42 cm. Results: A total of 108 subjects were recruited and 85 were analyzed (mean age 57.3 ± 15.0 [SD] years, 53 men, arm circumference 32.1 ± 5.1 cm, range 23–42 cm). For validation Criterion 1, the mean ± SD of the differences between the test device and reference BP readings (N = 255) was 0.3 ± 5.5/0.6 ± 4.7 mmHg (systolic/diastolic; pass threshold < = 5 ± 8 mmHg). For Criterion 2, the SD of the averaged BP differences between the test device and reference BP per individual (N = 85) was 3.76/3.61 mmHg (systolic/diastolic; pass threshold < = 6.95/6.91 mmHg). Conclusions: The InBody BPBIO480KV professional automated auscultatory BP device for office use comfortably fulfilled all the requirements of the AAMI/ESH/ISO Universal Standard (ISO 81060–2:2018) in general population and can be recommended for clinical use in adults.

Digital stethoscope in detecting innocent and pathological murmurs among children: systematic review

Despite fast developments in non-invasive diagnostic techniques, the initial cardiovascular exam still includes the auscultation of heart sounds with a stethoscope. This systematic review aimed mainly to study the developments in chest auscultation using digital stethoscopes and artificial neural network that interprets the results of electronic stethoscopes in detecting and differentiating innocent and pathological murmurs in the pediatric population. PubMed, Web of Science, Science Direct, EBSCO, Scopus, and Cochrane library were searched. Seven studies were included, with 671 patients. Out of the total population, 217 patients had innocent murmurs, and 403 had pathological murmurs. All the included studies reported that digital auscultation could differentiate pathological and innocent murmurs, which is a fast and cost-saving method. It was found that digital auscultation has a diagnostic benefit in identifying pathological and innocent murmurs. Studies also revealed that a quick and affordable strategy could be a big assistance when making decisions.

A Multimodal Model of ECG and Heart Sound Signal by Considering Normal and Abnormal Heart

Analysis of the opening and closing of heart valves and the movement of blood flow in the heart are important in the domain of early detection of heart conditions. To build this correlation model, multimodal signals from electrocardiography and stethoscope are needed. Multimodal signaling was performed using primary data with the same sampling at 10 seconds by recording the PQRST heart signal in the lying position using electrocardiography and the heart sound in the sitting position using an electronic stethoscope. Experimental results showed that the number of R peaks is the same as the number of S1 sound peaks, and also the number of T peaks with the number of S2 sound peaks, so it can be concluded that there is a regular signal pattern relationship between S1-S2 and the RT wave, namely the relationship at the end of the first peak of the QRS wave. The cardiac signal due to ventricular depolarization (ventricular contraction), the appearance of an S1 heart sound, and the association of the end of the next peak of the T wave of cardiac signals indicate ventricular repolarization and the appearance of an S2 heart sound. This is consistent with the fact that electrical events in cardiac activity occur before mechanical events in normal heart conditions. Based on the study of HRV parameters, heart sound signals can be used to determine HRV parameters. The results show the same number of peaks in normal hearts, while in abnormal hearts, there are differences in results because abnormal heart conditions have an erratic rhythmic pattern.

A novel professional automated auscultatory blood pressure monitor with visual display of Korotkoff sounds: InBody BPBIO480KV validation according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization Universal Standard.

A novel automated auscultatory upper arm-cuff blood pressure (BP) monitor (InBody BPBIO480KV) for office use was developed. An electronic stethoscope embedded in the device cuff records the Korotkoff sounds, which are audible to the user and graphically displayed during cuff deflation. Automated BP measurements are provided, while allowing the user to assess the Korotkoff sounds. The device accuracy was tested using the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Universal Standard (ISO 81060-2:2018) and its Amendment 1.2020-01. Participants were recruited to fulfil the age, sex, BP, arm circumference and cuff distribution criteria of the Universal Standard in general population using the same arm sequential measurement method. Three cuffs of the test device were used for arm circumference 23-28, 28-35 and 33-42 cm. Data from 85 individuals were analysed [mean age 57.3 ± 15.0 (SD) years, 53 men, arm circumference 23-42 cm]. For validation criterion 1, the mean ± SD of the differences between the test device and reference BP readings ( N = 255) was 0.3 ± 5.5/0.6 ± 4.7 mmHg (systolic/diastolic; threshold ≤5 ± 8 mmHg). For criterion 2, the SD of the averaged BP differences per individual ( N = 85) was 3.76/3.61 mmHg (systolic/diastolic; threshold ≤6.95/6.91 mmHg). The InBody BPBIO480KV device for office use, which provides automated auscultatory measurements while reproducing and displaying the Korotkoff sounds, comfortably fulfilled the AAMI/ESH/ISO Universal Standard requirements in general population and can be recommended for clinical use. The assessment of Korotkoff sounds by healthcare professionals for evaluating the quality of automated measurements requires further evaluation.

Regularity and mechanism of fake crackle noise in an electronic stethoscope.

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Electronic Stethoscope Design with IoT (Internet of Things)-based Disease Symptom Detection

Auscultation is a technique or method most often used by medical personnel in the initial examination of patients. One way is to use a stethoscope. However, this method has its drawbacks because the diagnosis is carried out subjectively and cannot be relied on with the accuracy to diagnose the symptoms of heart defects. Thus, the purpose of this study is to create an IoT system for electronic stethoscopes with BPM value output and make analog filters to eliminate noise interference which was a major obstacle in previous studies. The contribution to this study is to make it easier for medical users to monitor vital conditions, namely BPM remotely and produce BPM values in real-time. The method used in this study was to use a mic condensor placed on the patient's chest to detect pressure changes that occurred. This change in pressure causes a change in the voltage output value on the condensor mic. Output dari mic condenser masuk dan diproses di rangkaian PSA. Output sinyal dari PSA masuk ke mikrokontroler yang telah diprogram. Hasil yang dipeoleh dari pengukuran mengasilkan nilai error pengukuran nilai BPM dari 5 responden dan diperoleh nilai error yang dihasilkan dari responden 1 diperoleh error sebesar 0.33 BPM, responden 2 diperoleh nilai error sebesar 0,67 BPM, responden 3 memiliki nilai error sebesar 0,5 BPM, responden 4 nilai error sebesar 0,67 dan responden 5 mempunyai nilai error sebesar 0,67 BPM. The results of the statistical test were also obtained P-Value&gt;0.05 which explained that the resulting value did not have a significant difference and could be used for medical purposes. This research can help make it easier for doctors to analyze and diagnose symptoms of heart defects because this system is equipped with the detection of disease symptoms.

Evaluation of Internet-Connected Real-Time Remote Auscultation: An Open-Label Randomized Controlled Pilot Trial.

The utility of remote auscultation was unknown. This study aimed to evaluate internet-connected real-time remote auscultation using cardiopulmonary simulators. In this open-label randomized controlled trial, the physicians were randomly assigned to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). After the training session, the participants had to classify the ten cardiopulmonary sounds in random order as the test session. In both sessions, the intervention group auscultated with an internet-connected electronic stethoscope. The control group performed direct auscultation using a classical stethoscope. The total scores for correctly identified normal or abnormal cardiopulmonary sounds were 97/100 (97%) in the intervention group and 98/100 (98%) in the control group with no significant difference between the groups (p &gt; 0.99). In cardiac auscultation, the test score in the control group (94%) was superior to that in the intervention group (72%, p &lt; 0.05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. The utility of real-time remote cardiopulmonary auscultation using an internet-connected electronic stethoscope was comparable to that of classical auscultation. Classical cardiac auscultation was superior to real-time remote auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds.

Identification of decompensation episodes in chronic heart failure patients based solely on heart sounds.

Decompensation episodes in chronic heart failure patients frequently result in unplanned outpatient or emergency room visits or even hospitalizations. Early detection of these episodes in their pre-symptomatic phase would likely enable the clinicians to manage this patient cohort with the appropriate modification of medical therapy which would in turn prevent the development of more severe heart failure decompensation thus avoiding the need for heart failure-related hospitalizations. Currently, heart failure worsening is recognized by the clinicians through characteristic changes of heart failure-related symptoms and signs, including the changes in heart sounds. The latter has proven to be largely unreliable as its interpretation is highly subjective and dependent on the clinicians' skills and preferences. Previous studies have indicated that the algorithms of artificial intelligence are promising in distinguishing the heart sounds of heart failure patients from those of healthy individuals. In this manuscript, we focus on the analysis of heart sounds of chronic heart failure patients in their decompensated and recompensated phase. The data was recorded on 37 patients using two types of electronic stethoscopes. Using a combination of machine learning approaches, we obtained up to 72% classification accuracy between the two phases, which is better than the accuracy of the interpretation by cardiologists, which reached 50%. Our results demonstrate that machine learning algorithms are promising in improving early detection of heart failure decompensation episodes.

Heart Sound Signal Analysis for Digital Auscultation

Cardiac auscultation is the examination of the heart by listening to the sound produced by the heart through a stethoscope. Heart sounds can provide information about the functioning of the heart valve condition as well as information about the structural abnormalities of the heart. However, it needs intensive training for mastering. The objective of the work was to develop an algorithm for heart sound signal classification applied to computer-assisted digital auscultation in order to identify pathological events. The method can be described as follow: data collection, pre-processing, segmentation, feature extraction, and classification. The data were collected from volunteers using an electronic stethoscope and from a database available in the internet. The heart sound data were then extracted and split into training, validation, and testing datasets. In the training process the dataset was labeled as normal and abnormal (aortic stenosis, mitral stenosis, aortic regurgitation, pulmonic regurgitation, tricuspid stenosis, flow murmur, and patent ductus arteriosus). The convolution neural network is used as a classifier in the learning process to obtain the learning model. The model was validated and tested using the available datasets. The experimental results show that the algorithm has the capability to classify the heart sound into normal and abnormal with a high detection rate.

Analysis of the Effectiveness of Using Digital Filters in Electronic Stethoscopes

The heart sound produced in some cases of the disease shows a certain pattern. The purpose of this study was to design an electronic stethoscope for cardiac auscultation with the following display. The contribution in this study is being able to show certain patterns that can be diagnosed in the sound signal. So that the pattern can be known when there is a heart disease disorder, an electronic stethoscope will be made for auscultation of the next display, making it easier for users to diagnose heart disease. The heart sound is obtained from the mechanical activity of the heart which is tapped by a condenser mic. The heart sound will be held in a pre-amp circuit, then the filters used are High Pass Filters and Low Pass Filters with an interrupted frequency of 20-95 Hz. The output of the filter circuit will enter the booster circuit. Then it will be processed by the microcontroller. In processing the data that will be displayed on Nextion and Speaker, the author uses Arduino Mega. Based on the test, it can be seen that the digital filter has a slight error rate because it removes the most noise, while in the analog filter there is still a lot of noise. The results of the research that has been done can be implemented using a system that really supports the needs.

Continuous Monitoring Versus Intermittent Auscultation of Wheezes in Patients Presenting With Acute Respiratory Distress

BackgroundAuscultation for an extended period of time using a wearable stethoscope enables objective computerized analysis and longitudinal assessment of lung sounds. However, this auscultation method differs from bedside auscultation in that clinicians are not present to optimize the quality of auscultation. No prior studies have compared these two auscultation methods. ObjectiveThe aim of this study was to compare intermittent auscultation using a conventional stethoscope with continuous auscultation using a wearable stethoscope for wheeze detection in patients who present with acute respiratory distress. MethodsPatients presenting to the emergency department with acute respiratory distress were enrolled. The Strados Remote Electronic Stethoscope Platform (RESP™) was used for continuous auscultation, and intermittent auscultation was performed using a U.S. Food and Drug Administration–cleared electronic stethoscope. A recording was made with an electronic stethoscope. Subsequently, continuous recording was made using RESP™, which continued until the patient was admitted or discharged from the emergency department. The number of captured wheezes in each recording was counted and validated by two board-certified physicians. ResultsFrom May 2018 to May 2019, 43 patients were enrolled in the study. Three patients were excluded from analysis due to incomplete audio recording data. The mean length of recording was 62.3 min for continuous auscultation and 0.7 min for intermittent auscultation; 77.5% (31 of 40) of intermittent recordings contained wheezes, in contrast to 85% (34 of 40) of continuous recordings. ConclusionsExtending the duration of auscultation using a wearable stethoscope in a noisy clinical environment showed comparable performance to standard of care intermittent auscultation in identifying patients who have wheezes.

STETHOSCOPE AND IT'S OVER 200 YEARS LONG JOURNEY

The stethoscope is a medical device for auscultation of internal sounds of human body. It is placed against the skin with two tubes connected to two earpieces. It was invented in France in 1816 by French Physician R.T.H. Laennec at the Necker-Enfants Malades Hospital in Paris. Through the stethoscopes 200 year history there has been a signicant development from the use simple monaural earpiece to the binaural stethoscope followed by the electronic stethoscope which in today's world is used by all doctors and in all specialties. This is a review highlighting all the changes that were made to this wonderful instrument along with the relevant history

Automated identification of innocent Still's murmur using a convolutional neural network.

BackgroundStill's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists.ObjectivesTo develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral.MethodsThe study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm.ResultsA comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943.ConclusionsStill's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.

Cardiopulmonary auscultation enhancement with a two-stage noise cancellation approach

For cardiopulmonary auscultation using electronic stethoscopes, signal quality is a key point. During signal acquisition various background sounds may be inevitably captured, severely degrading the auscultation signal quality. In the existing auscultation denoising methods, conventional adaptive noise canceller (ANC) approaches or shallow-layer artificial neural networks were used, while advanced noise cancellation methods still need to be developed to fulfill real auscultation requirements. In this paper, we propose a novel denoising method for cardiopulmonary auscultation enhancement, which is a two-stage approach with a cascade of ANC and deep neural networks (DNNs). In the first stage, the ANC provides coarsely denoised auscultation signal and estimated interference. In the second stage, a DNN termed the dual-channel interactive noise cancellation network (DINC-Net) is proposed, which exploits both the coarsely denoised auscultation signal and the estimated interference. The DINC-Net consists of two deep encoders extracting features of dual-channel inputs separately, one dual-channel interactive denoising module generating a denoising mask, and one deep decoder giving the denoised output. The performance of the proposed method is evaluated through synthetic data generated using two public heart/lung sound databases, and the great promotion in normalized covariance measure (NCM) and frequency-weighted segmental signal-to-noise ratio (fwSNRseg) has been verified, compared to the existing methods. An online noise cancellation prototype is further developed on an electronic stethoscope, and the signal quality promotion is shown on healthy subjects as well as aortic stenosis patients.

Smartphone as an electronic stethoscope: factors influencing heart sound quality.

Smartphones are equipped with a high-quality microphone which may be used as an electronic stethoscope. We aim to investigate the factors influencing quality of heart sound recorded using a smartphone by non-medical users. An app named Echoes was developed for recording heart sounds using iPhone. Information on phone version and users' characteristics including sex, age, and body mass index (BMI) was collected. Heart sound quality was visually assessed and its relation to phone version and users' characteristics was analysed. A total of 1148 users contributed to 7597 heart sound recordings. Over 80% of users were able to make at least one good-quality recording. Good-, unsure- and bad-quality recordings amounted to 5647 (74.6%), 466 (6.2%) and 1457 (19.2%), respectively. Most good recordings were collected in the first three attempts of the users. Phone version did not significantly change the users' success rate of making a good recording, neither was sex in the first attempt (P = 0.41) or the first three attempts (P = 0.21). Success rate tended to decrease with age in the first attempt (P = 0.06) but not the first three attempts (P = 0.70). BMI did not significantly affect the heart sound quality in a single attempt (P = 0.73) or in three attempts (P = 0.14). Smartphone can be used by non-medical users to record heart sounds in good quality. Age may affect heart sound recording, but hardware, sex, and BMI do not alter the recording.

Convolutional Neural Network for Improvement of Heart Valve Disease Detection

Valvular heart disease (VHD) encompasses a number of common cardiovascular conditions that account for a significant percentage of heart diseases. At present, the acoustic phenomena generated by the abnormal functioning of the heart valves can be recorded and digitized using electronic stethoscopes known as phonocardiographs. The analysis of the phonocardiographic signals has made it possible to indicate that the normal and pathological records differ from each other in terms of both temporal and spectral characteristics. The present work describes the construction and implementation of a Deep Learning (DL) algorithm for the binary classification of normal and abnormal heart sounds. The performance of this approach reached an accuracy higher than 98 % and specificities in the ”Normal” class of up to 99 %.

Evaluation of the electronic stethoscope (FONODOC) as a cardiac screening tool during the preoperative evaluation of children.

An electronic stethoscope with an inbuilt phonocardiogram is a potentially useful tool for paediatric cardiac evaluation in a resource-limited setting. We aimed to compare the acoustic and electronic stethoscopes with respect to the detection of murmurs as compared to the transthoracic echocardiogram (TTE). This was an observational study. Fifty children aged 0-12 years with congenital heart diseases (CHDs) and 50 without CHD scheduled for echocardiography were examined using both stethoscopes. The findings were corroborated with clinical findings and compared with the echocardiography report. Among the 50 cases without CHD, no murmur was detected using either of the stethoscopes. This was in agreement with TTE findings. The calculated specificity of both stethoscopes was 100%. Amongst the 50 cases with CHD, the electronic stethoscope picked up murmurs in 32 cases and missed 18 cases. The acoustic stethoscope picked up murmurs in 29 cases and missed 21 cases. Thus, the sensitivity of electronic and acoustic stethoscopes as compared to TTE was calculated to be 64% and 58%, respectively. The positive predictive value of the electronic stethoscope as compared to TTE was 100% while the negative predictive value was 73%. The kappa statistic was 0.93 suggesting agreement in 93%. Mc-Nemar's test value was 0.24 suggesting that the electronic stethoscope did not offer any advantage over the acoustic stethoscope for the detection of CHD in children. A comparison of the electronic stethoscope with an acoustic stethoscope suggests that the rate of detection of CHD with both stethoscopes is similar and echocardiography remains the gold standard.

Cardiac Acoustic Signal of an unborn baby. A case study.

In medical and laboratory procedures used to obtain useful information to diagnose a disease it is very important not to cause harm to the patient. In the detection of heart disease in fetuses in the mother’s womb, a safe and non-invasive procedure is fetal phonocardiography. In this procedure, an electronic stethoscope captures the sound emitted by the baby’s heart, transforms it into an image and is displayed on a computer screen and a subsequent analysis of that image is performed. This paper shows the procedure to obtain the phonocardiogram of an unborn baby and an algorithm for extracting from it the sounds from the mother’s heart and other physiological noises. The purpose is to recognize if in the acoustic image there are anomalies that allow us to identify congenital or other type lesions in the heart of the fetus.

The use of digital technologies in the practice of veterinary clinics

There is an analysis of the use of digital technologies and digital devices in the practice of veterinary clinics located in large cities of the Central Federal District of the Russian Federation. The method of questioning was used in the work. The respondents were employees of veterinary clinics. It has been established that veterinary clinics actively use digital technologies in their work to promote veterinary services in the Internet space (websites, pages in social networks), to inform and consult clients (e-mail, instant messengers, social networks, chat bots, TV veterinary medicine), to manage clinic (specialized programs «Vetmanager», «ERISCA», etc.), and improving the monitoring of the condition of animals and the diagnosis of pathologies (veterinary control sensors, video cameras, digital equipment). The equipment of veterinary clinics includes the following digital devices: digital thermometer, digital ultrasound scanner, electronic tonometer, digital X-ray machine, digital veterinary monitoring sensor, electronic stethoscope. There are most widely used digital technologies/devices: e-mail, specialized digital devices (digital X-ray machine, ultrasound scanner, etc.), instant messengers, clinic website. The following programs are used by veterinary clinics to automate administrative, economic and medical diagnostic processes: «Vetmanager» – 31.0% of clinics; «ERISCA» – 18.3%, etc. In 14.1% of the analyzed veterinary clinics, specialized software is not used.

Evaluation of Signal Quality from a Wearable Phonocardiogram (PCG) Device and Personalized Calibration

Currently, the only clinically utilized Phonocardiogram (PCG) is an electronic stethoscope used in a hospital or clinical environment. The availability of continuously recorded PCGs can provide a new avenue of research into chronic disease management at home. Researchers have proposed such wearable PCG devices. However, limitations exist in evaluating such devices as PCG recording devices in home-like environments. Here, we evaluate a wearable PCG system in a belt-type form factor with an embedded force sensor, accelerometer, and a single lead ECG to study the feasibility of acquiring diagnostic-grade PCGs while the wearer performs daily activities. We describe qualitative and quantitative exploratory analysis methods for cross-subject comparison of PCG signal quality, wearer comfort, and the impact of activities using Signal-to-Noise (SNR) comparisons and cross-spectral coherence between activity and PCG. The analysis of the data suggests that a common user-chosen method of donning a wearable PCG is not applicable across subjects for obtaining optimal PCG recording quality. We propose a method to calibrate wearable PCG devices using an embedded force sensor and by following a protocol involving feedback from the embedded force sensor to determine the optimal method of wearing the device. Following a similar path to precision medicine using genomic data and the extrapolation of risk, wearable devices with healthcare applications should be developed with the ability to be adapted and calibrated to each individual. In the immediate future this may involve calibration procedures such as those followed in this work, using controlled measurements performed with each patient to tune a device for them.