Beats Per Minute Research Articles

Safety and Efficacy of External Nasal Dilator Strips with N95 Respirator Masks by Emergency Department Personnel.

INTRODUCTIONThe coronavirus disease 2019 (COVID-19) pandemic has prompted increased use of personal protective equipment (PPE) to maintain the health and safety of caregivers. This study was conducted in 2020 to evaluate the safety and efficacy of external nasal dilator strips (ENDS) coupled with N95 respirators in a sample of community hospital emergency department personnel.METHODSAfter obtaining written consent, the authors tested participants’ response to exercise (i.e., walking up 10 flights of stairs) while wearing an N95 respirator, both with and without an ENDS. The authors measured participants’ heart rate and respiratory effort responses over four minutes following their exercise trial. A convenience sample of these personnel also repeated their respirator fit testing while wearing an ENDS with the N95 style they had previously been fitted for.RESULTSA total of N = 50 participants were enrolled. Peak heart rate while wearing an ENDS was 125 beats per minute (BPM) with the ENDS versus 130 BPM without (p = 0.21). The Borg Exertion Score while wearing an ENDS peaked at 13 with the ENDS versus 14 without (p = 0.08). However, when subjects were surveyed before and after the trial upon whether they would consider using an ENDS beneath their N95 using a scale of 1-5, their interest in this significantly increased (p = 0.004). Four of the 13 (31%) participants who completed repeated fit testing while wearing the ENDS beneath their N95 respirator failed the repeat testing.CONCLUSIONSThese results first suggest that a sizable proportion of ED personnel may fail N95 fit testing while wearing an ENDS beneath the N95 mask for which they had been previously fitted. Although providers’ subjective interest in use of ENDS increased, these results also demonstrate that use of an ENDS beneath an N95 respirator may not significantly increase exercise tolerance.

1-Dimentional Convolutional Neural Network based Heart Rate Estimation Using Photoplethysmogram Signals

Recently, as the importance of healthcare has increased, researches are being conducted to measure health status in real time. Heart Rate (HR) measurement is one of the important health conditions that measure heart beat rates. HR measurement can be performed using Photoplethysmogram (PPG) or Electrocardiogram (ECG) signals. Since the PPG or ECG signals are different from people to people, conventional HR estimator occasionally results in large errors. To develop a reliable HR estimator, an HR estimation technique using PPG is proposed in this paper, based on a deep learning technique. The proposed HR estimation technique has the following key features. We develop a new artificial neural network which is 1-Dimensional Convolutional Neural Network (1D-CNN) composed of ten convolutional layers and two fully connected layers. To assess the estimation performance, cross validation is used. The training and verification of the proposed 1D-CNN technique are performed on Python 3.7.5 with Keras 2.0 library. The proposed HR estimation technique performs training and verification using field PPG data. Overfitting is prevented by increasing the limited training data by data augmentation. In training, the loss function is the Mean Square Error (MSE), which is commonly used in regression problems. In the verification, the error between the predicted HR and the actual HR is compared using Mean Absolute Error (MAE). As a result of the final performance verification through cross validation, the proposed technique shows an MAE of 1.23 Beats Per Minute (BPM). This results indicate that the proposed technique enables quick and accurate HR estimation with only PPG signals. Therefore, if this technique is applied to medical and wearable devices, the proposed technique can replace the existing HR monitors.

Data-Driven Computer Choreography Based on Kinect and 3D Technology

As a form of artistic expression, dance accompanied by music enriches the cultural life of human beings and stimulates the creative enthusiasm of the public. Choreography is usually done by professional choreographers. It is highly professional and time-consuming. The development of technology is changing the way of artistic creation. The development of motion capture technology and artificial intelligence makes computer-based automatic choreography possible. This paper proposes a method of music choreography based on deep learning. First, we use Kinect to extract and filter actions and get actions with high authenticity and continuity. Then, based on the constant Q transformation, the overall note density and beats per minute (BPM) of the target music are extracted, and preliminary matching is performed with features such as action speed and spatiality, and then, the local features of the music and action segments based on rhythm and intensity are matched. The experimental results show that the method proposed in this paper can effectively synthesize dance movements. The speed and other characteristics of each movement segment in the synthesis result are very uniform, and the overall choreography is more aesthetic.

Development of a Low-Cost Portable Heartbeat Monitor Using an Infrared Sensor and a Microcontroller-Digital Stethoscope

The problem of shortage of medical devices in rural areas of Nigeria, cause a budget to be limited and constrained. Therefore, developing medical devices at low cost is one solution. This paper proposes an improvement, portability, and cost-effectiveness of the heart rate measuring devices being applied in the Rural Local medical in Akure South Local Government Area of Ondo State of Nigeria; through the development of a Low-Cost Portable Heart Beats Monitor using an Infrared Sensor and a Microcontroller. The development of the prototype is to function as an assisting device for a user which has a busy lifestyle to monitor the heart rate at any place in the real-time condition. This report contains the detailed procedures involved in designing the heartbeat monitoring circuit, developing the circuit design, and carrying out the performance evaluation of the system developed. In this project, Infrared LED and photodiode have been used as a sensor to measure a heart rate and a PIC16F876A microcontroller acts as a controller to process the input and output of the device. The development of the prototype starts with designing the sensor circuit, signal conditioning circuit, voltage regulator circuit, microcontroller circuit, and dual power supply unit. After the functionality test, these circuits were integrated with the output display. The prototype design is able to display the heart rate measurement in beats per minute (BPM) and the condition of heart rate either low, normal, or high heart rate on a 16x2 matrix Liquid Crystal Display (LCD).

A Wearable Head Mounted Display Bio-Signals Pad System for Emotion Recognition.

Multimodal bio-signals acquisition based on wearable devices and using virtual reality (VR) as stimulus source are promising techniques in emotion recognition research field. Numerous studies have shown that emotional states can be better evoked through Immersive Virtual Environments (IVE). The main goal of this paper is to provide researchers with a system for emotion recognition in VR environments. In this paper, we present a wearable forehead bio-signals acquisition pad which is attached to Head-Mounted Displays (HMD), termed HMD Bio Pad. This system can simultaneously record emotion-related two-channel electroencephalography (EEG), one-channel electrodermal activity (EDA), photoplethysmograph (PPG) and skin temperature (SKT) signals. In addition, we develop a human-computer interaction (HCI) interface which researchers can carry out emotion recognition research using VR HMD as stimulus presentation device. To evaluate the performance of the proposed system, we conducted different experiments to validate the multimodal bio-signals quality, respectively. To validate EEG signal, we have assessed the performance in terms of EEG eyes-blink task and eyes-open and eyes-closed task. The EEG eyes-blink task indicates that the proposed system can achieve comparable EEG signal quality in comparison to the dedicated bio-signals measuring device. The eyes-open and eyes-closed task proves that the proposed system can efficiently record alpha rhythm. Then we used signal-to-noise ratio (SNR) and Skin Conductance Reaction (SCR) signal to validate the performance for EDA acquisition system. A filtered EDA signal, with a high mean SNR of 28.52 dB, is plotted on HCI interface. Moreover, the SCR signal related to stimulus response can be correctly extracted from EDA signal. The SKT acquisition system has been validated effectively by the temperature change experiment when subjects are in unpleasant emotion. The pulse rate (PR) estimated from PPG signal achieved the low mean average absolute error (AAE), which is 1.12 beats per minute (BPM) over 8 recordings. In summary, the proposed HMD Bio Pad offers a portable, comfortable and easy-to-wear device for recording bio-signals. The proposed system could contribute to emotion recognition research in VR environments.

Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19.

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).

Using a Portable Gait Rhythmogram to Examine the Effect of Music Therapy on Parkinson's Disease-Related Gait Disturbance.

External cues improve walking by evoking internal rhythm formation related to gait in the brain in patients with Parkinson’s disease (PD). This study examined the usefulness of using a portable gait rhythmogram (PGR) in music therapy on PD-related gait disturbance. A total of 19 subjects with PD who exhibited gait disturbance were evaluated for gait speed and step length during a 10 m straight walking task. Moreover, acceleration, cadence, and trajectory of the center of the body were estimated using a PGR. Walking tasks were created while incorporating music intervention that gradually increased in tempo from 90 to 120 beats per minute (BPM). We then evaluated whether immediate improvement in gait could be recognized even without music after walking tasks by comparing pre- (pre-MT) and post-music therapy (post-MT) values. Post-MT gait showed significant improvement in acceleration, gait speed, cadence, and step length. During transitions throughout the walking tasks, acceleration, gait speed, cadence, and step length gradually increased in tasks with music. With regard to the trajectory of the center of the body, we recognized a reduction in post-MT medio-lateral amplitude. Music therapy immediately improved gait disturbance in patients with PD, and the effectiveness was objectively shown using PGR.

An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress

Life in modern societies is fast-paced and full of stress-inducing demands. The development of stress monitoring methods is a growing area of research due to the personal and economic advantages that timely detection provides. Studies have shown that speech-based features can be utilised to robustly predict several physiological markers of stress, including emotional state, continuous heart rate, and the stress hormone, cortisol. In this contribution, we extend previous works by the authors, utilising three German language corpora including more than 100 subjects undergoing a Trier Social Stress Test protocol. We present cross-corpus and transfer learning results which explore the efficacy of the speech signal to predict three physiological markers of stress—sequentially measured saliva-based cortisol, continuous heart rate as beats per minute (BPM), and continuous respiration. For this, we extract several features from audio as well as video and apply various machine learning architectures, including a temporal context-based Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). For the task of predicting cortisol levels from speech, deep learning improves on results obtained by conventional support vector regression—yielding a Spearman correlation coefficient (ρ) of 0.770 and 0.698 for cortisol measurements taken 10 and 20 min after the stress period for the two corpora applicable—showing that audio features alone are sufficient for predicting cortisol, with audiovisual fusion to an extent improving such results. We also obtain a Root Mean Square Error (RMSE) of 38 and 22 BPM for continuous heart rate prediction on the two corpora where this information is available, and a normalised RMSE (NRMSE) of 0.120 for respiration prediction (−10: 10). Both of these continuous physiological signals show to be highly effective markers of stress (based on cortisol grouping analysis), both when available as ground truth and when predicted using speech. This contribution opens up new avenues for future exploration of these signals as proxies for stress in naturalistic settings.

Dexmedetomidine in combination with ketamine for pediatric procedural sedation or premedication: A meta-analysis.

To evaluate effectiveness of combinational use of dexmedetomidine and ketamine (DEX-KET) for pediatric procedural sedation or premedication. Relevant studies were identified after a literature search in electronic databases and study selection was based on precise eligibility criteria. Meta-analyses of mean differences were performed to examine differences in sedation onset and recovery times between DEX-KET and comparators. Changes from baseline in heart rate (HR), respiratory rate, oxygen saturation, and mean arterial pressure (MAP), were pooled. Meta-analyses of proportions were performed to estimate incidence of adverse events. 15 studies (1087 patients) were included. Onset of sedation was significantly shorter in DEX-KET than in DEX group. HR declined in DEX-KET group from start (-3.5 beats per minute (BPM) [95% CI: -5.1, -1.9]) through midpoint (-7.2 BPM [95% CI: -12.1, -2.3]) and at end of sedation (-8.7 BPM [95% CI: -13.1, -4.4]). Decrease in HR after DEX administration at start was -11.6 BPM [95% CI: -16.0, -7.1] and remained consistent afterward. There was no change in MAP during DEX-KET sedation. However, after DEX administration, MAP decreased by -6.9 [95% CI: -10.4, -3.3] at start, -7.8 [95% CI: -11.4, -4.2] at middle, and by -6.6 [95% CI: -14.4, 1.1] at end of sedation. Incidence of hypotension was 3% [95% CI: 0, 9] in DEX-KET, 7% [95% CI: 2, 14] in DEX, and 0% [95% CI: 0, 2] in KET groups. Incidence of bradycardia was 2% [95% CI: 0, 6] with DEX-KET and 12% [95% CI: 5, 20] with DEX. Incidence of oxygen desaturation was 3% [95% CI: 0, 8] in DEX-KET, 2% [95% CI: 0, 6] in DEX, 12% [95% CI: 5, 20] in KET, and 13% [95% CI: 6, 21] in PROP-KET groups. MIDA-KET sedation had 13% [95% CI: 4, 25] incidence of tachycardia. DEX-KET for pediatric sedation results in better sedation outcomes than DEX or KET by shortening onset of sedation and recovery while maintaining hemodynamic and respiratory stability with low incidence of adverse events. DEX sedation was associated with higher incidence of bradycardia. Higher incidence of oxygen desaturation was observed with KET and PROP-KET whereas MIDA-KET was associated with higher incidence of tachycardia.

Q-PPG: Energy-Efficient PPG-Based Heart Rate Monitoring on Wearable Devices.

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor. Unfortunately, most standard approaches of this kind rely on hand-tuned parameters, which impair their generalization capabilities and their applicability to real data in the field. In contrast, methods based on deep learning, despite their better generalization, are considered to be too complex to deploy on wearable devices. In this work, we tackle these limitations, proposing a design space exploration methodology to automatically generate a rich family of deep Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a single "seed" model. Our flow involves a cascade of two Neural Architecture Search (NAS) tools and a hardware-friendly quantizer, whose combination yields both highly accurate and extremely lightweight models. When tested on the PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean Absolute Error. Furthermore, we deploy our TCNs on an embedded platform featuring a STM32WB55 microcontroller, demonstrating their suitability for real-time execution. Our most accurate quantized network achieves 4.41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest network that obtains a MAE 8 BPM, among those generated by our flow, has a memory footprint of 1.9 kB and consumes just 1.79 mJ per inference.

Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

BackgroundHeart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement.MethodsThis paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements.ResultsExperimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG.ConclusionsTo the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.

Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity

BackgroundRapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity.MethodsThis study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity.ResultsAmong the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67–0.78.ConclusionThe DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

Assessment of ROI Selection for Facial Video-Based rPPG.

In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm.

Abstract 13331: Case Report Of Transient Right Bundle Branch Block With S1q3t3 Pattern In Acute Pulmonary Embolism

69 year old woman with hypertension, sleep apnea and who had a recent knee replacement, was brought in to the emergency room (ER) for syncope. She had her physiotherapy session earlier in the day and became symptomatic with dizziness and shortness of breath and passed out. In the ER she had a systolic blood pressure (SBP) of 90mmHg and an oxygen saturation (O2 sat) of 80% on room air. She was given fluid bolus with improvement of SBP to 110mmHg. O2 sat improved to 99% with 10L of oxygen. Treatment was started with full dose of Enoxaparin subcutaneously. A 12-lead electrocardiogram (EKG) showed sinus tachycardia (ST) at 117 beats per minute (BPM), right bundle branch block (RBBB) and S1Q3T3 pattern. A bedside echocardiogram showed right ventricular (RV) distension. A CT-angiogram showed bilateral main stem pulmonary emboli (PE) with signs of RV strain. Patient was admitted and remained hemodynamically stable. A repeat EKG done the following day showed ST at 110 BPM with resolution of RBBB. An echocardiogram done now showed normal RV size and function. Patent was eventually discharged home on full dose apixaban. Q3S1T3 Figure 1 - ST, RBBB, S1Q3T3 pattern Figure 2 - ST Discussion: The patient presented with new RBBB (Figure 1) due to RV strain from acute PE as shown by the CT-angiogram and bedside echocardiogram. The EKG also showed S1Q3T3 pattern. With the resolution of the RV strain the following day, as noted on the subsequent echocardiogram, the 12 lead EKG showed the resolution of the RBBB and the QRS complex became narrow (Figure 2). RBBB is thought to be caused by acute RV overload and dilatation, accompanied by subendocardial ischemia in the right bundle. The S1Q3T3 pattern is historically considered as a “classic” EKG pattern associated with acute PE, even though it is neither sensitive nor specific for acute PE. Conclusion: The RBBB along with the S1Q3T3 pattern and its resolution with improvement of RV hemodynamics, is the first reported case in the literature.

Abstract 13848: Establishing an Optogenetic in vitro Model to Study Atrial Fibrillation Based on Human Engineered Heart Tissue

Introduction: Atrial fibrillation (AF) is a type of sustained cardiac tachyarrhythmia in which the atrial rate can reach up to 300-600 beats per minute (BPM). The elucidation of the mechanisms of AF and the search for new therapeutic methods is hampered by a lack of suitable in vitro models. In previous studies, chronic optical tachypacing induced arrhythmia vulnerability in ventricular-like human engineered heart tissue (EHT). Here, we aimed to optimize an AF in vitro model based on optogenetics, EHT and a novel optogenetic pacing platform. Methods: Human induced pluripotent stem cells (hiPSC) stably expressing Volvox-Channelrhodopsin-1 (VChR1) were differentiated into atrial-like cardiomyocytes (CM) by treatment with 1 μM retinoic acid (RA) at the cardiac induction stage. CMs were used to cast fibrin-based 24-well format EHTs. The expression of atrial/ventricular markers was validated by qPCR, and action potential (AP) recordings with atrial-specific channel inhibitors were performed on at least one-month-old EHTs. A novel custom-designed circuit board with a programmable microcontroller and 24 RGB addressable light-emitting diodes (LED) was used for optical tachypacing. 25-30 days old EHTs were stimulated by 90 s bursts of blue light pulses (5 Hz), separated by 10 s breaks for 35-40 days. Non-paced and paced EHTs were characterized regarding contractility and gene expression. Results: EHTs had a high expression of atrial mRNA markers and displayed atrial-like AP. A flexible and straightforward optogenetic tool was built to make chronic tachypacing feasible. Using this novel platform, EHTs were successfully paced at 5 Hz for up to 40 days. Compared with non-paced EHTs, paced EHTs showed a different spontaneous contractility profile, higher expression of stress genes, and released more troponin I into the medium. Conclusions: We have successfully generated an in vitro model to mimic the status of atria with a high frequency (300 BPM) during AF. Paced EHTs displayed stress signs, partially resembling the changes of atria in AF. In the future, we could utilize this model to study the underlying mechanisms and assess proarrhythmic and antiarrhythmic drug effects.

Invisible ECG for High Throughput Screening in eSports.

eSports is a rapidly growing industry with increasing investment and large-scale international tournaments offering significant prizes. This has led to an increased focus on individual and team performance with factors such as communication, concentration, and team intelligence identified as important to success. Over a similar period of time, personal physiological monitoring technologies have become commonplace with clinical grade assessment available across a range of parameters that have evidenced utility. The use of physiological data to assess concentration is an area of growing interest in eSports. However, body-worn devices, typically used for physiological data collection, may constitute a distraction and/or discomfort for the subjects. To this end, in this work we devise a novel “invisible” sensing approach, exploring new materials, and proposing a proof-of-concept data collection system in the form of a keyboard armrest and mouse. These enable measurements as an extension of the interaction with the computer. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard device, involving 7 healthy subjects. A particularly advantageous characteristic of our setup is the use of conductive nappa leather, as it preserves the standard look and feel of the keyboard and mouse. According to the results obtained, this approach shows 3–15% signal loss, with a mean difference in heart rate between the reference and experimental device of −1.778 ± 4.654 beats per minute (BPM); in terms of ECG waveform morphology, the best cases show a Pearson correlation coefficient above 0.99.

Surface modified hybrid ZnSnO3 nanocubes for enhanced piezoelectric power generation and wireless sensory application

Piezoelectric Nanogenerators (PENGs), which can convert ambient mechanical stimuli into electrical energy, are held in high regard due to their cost-effectiveness, energy harvesting applications, and potential as self-powered sensors. We report an aluminum-doped zinc stannate (ZnSnO3) PENG that can achieve high electrical outputs with respect to the external force. In order to enrich the piezoelectric mechanics, a low-temperature solution method was adopted in our work to synthesize ZnSnO3 nanocubes with an average side length of only 30 – 55 nm. Furthermore, ZnSnO3 was doped with 1–5 wt% of aluminum nanoparticles. We report that 2 wt% of aluminum doped ZnSnO3 showed the highest electrical output in terms of open circuit voltages and short circuit current. The nanogenerator device achieved an average open-circuit voltage of 80–175 V with a frequency range of 60 BPM (Beats Per Minute) to 240 BPM, an unprecedented electrical output in comparison to current ZnSnO3-based PENGs. With the presented high output-to-size ratio taken into consideration, the device was mounted in a helmet and tested as an energy harvester and wireless human motion sensor, which can generate electric charge as well as detect human movements and transmit the corresponding signals wirelessly. Our work is indicative of a promising smart helmet using organic-inorganic hybrid materials.

Multi-modal Framework for Fetal Heart Rate Estimation: Fusion of Low-SNR ECG and Inertial Sensors.

This study presents a novel multi-modal framework for fetal heart rate extraction, which incorporates wearable seismo-cardiography (SCG), gyro-cardiography (GCG), and electrocardiography (ECG) readings from ten pregnant women. Firstly, a signal refinement method based on empirical mode decomposition (EMD) is proposed to extract the desired signal components associated with fetal heart rate (FHR). Afterwards, two techniques are developed to fuse the information from different modalities. The first method, named early fusion, is intended to combine the refined signals of different modalities through intra-modality fusion, intermodality fusion, and FHR estimation. The other fusion approach, i.e., late fusion, includes FHR estimation and intermodality FHR fusion. FHR values are estimated and compared with readings from a simultaneously-recorded cardiotocography (CTG) sensor. It is demonstrated that the best performance belongs to the late-fusion approach with 87.00% of positive percent agreement (PPA), 6.30% of absolute percent error (APE), and 10.55 beats-per-minute (BPM) of root-meansquare-error (RMSE).Clinical Relevance- The proposed framework allows for the continuous monitoring of the health status of the fetus in expectant women. The approach is accurate and cost-effective due to the use of advanced signal processing techniques and lowcost wearable sensors, respectively.

Touchless Compression Using Shallow Kinetics Induced by Metronome (SKIM)

Purpose/Objective(s)As an alternative to conventional compression approaches (e.g., hard plate or belt) amidst the ongoing COVID-19 pandemic, we have developed a touchless motion management strategy for stereotactic body radiation therapy (SBRT). By increasing the patient's breathing rate to induce fast shallow-breathing (FSB) with the aid of a metronome, our hypothesis was that the motion magnitude of the target would be minimized with no physical contact and compression.Materials/MethodsTen SBRT patients who were treated under FSB were selected for this study: 4 lung and 6 liver cases. Our proposed method is called shallow kinetics induced by metronome (SKIM). We have used a metronome by setting the beats-per-minute (BPM) in the range of 50–60 to induce the SB. This corresponded to a patient breathing rate of 25–30 (breathing) cycles-per-minute. Two 4DCT scans, free-breathing (FB) and SKIM, were acquired and exported to commercially available software. Subsequently, the motion magnitude of the target in a superior-to-inferior direction was measured in each data set and compared.ResultsThe respiratory motion was greatly reduced when using SKIM. The mean ± standard deviation (SD) values of motion magnitude of the target in FB and SKIM across patients were 1.65 ± 0.93 cm and 0.76 ± 0.20 cm, respectively. Among these cases, the mean BPM setting of the metronome was 55.2.ConclusionCompared with FB, the SKIM technique could significantly reduce the respiratory motion magnitude of thoracic and abdominal targets. Our proposed method can be an excellent practical alternative to the conventional compression methods due to its flexibility and ease of implementation, especially during a global pandemic when minimal physical contact is crucial for the safety of both patients and care providers. Table 1. The comparison of motion magnitude of the target between free-breathing (FB) and shallow kinetics induced by metronome (SKIM). The mean ± standard deviation values across patients were 1.65 ± 0.93 cm and 0.76 ± 0.20 cm, respectively.

Frequency of hand movements as a possible diagnostic tool for parkinsonian bradykinesia. Proposal of a simple bedside test.

Bradykinesia, dysrhythmia, and decrement in hand movements (HM) are core symptoms of Parkinson's disease (PD). The maximal rate of repetitive rhythm-preserving HM can be a diagnostic tool for PD bradykinesia. To improve the clinical diagnosis of bradykinesia by identifying the frequencies at which rhythmic HM become irregular in PD patients compared to healthy age-matched controls. Forty PD patients and 16 controls were asked to alternately perform left and right hand movements following the rate of a metronome with sound stimulation beginning at 85 beats per minute (BPM) and increasing in increments of 15 BPM up to 355 BPM. The rhythm of the HM for each rate was assessed visually, and the threshold frequency at which the subject could no longer rhythmically continue HM was measured by the metronome. The increasing rates of HM until reaching that threshold were compared between patients with PD and controls. The mean rates of a metronome in PD vs. healthy subjects were 173.3 ± 42.0 vs. 248.8 ± 48.5 BPM (p < 0.001) and 164.8 ± 34.2 vs. 241.2 ± 40.1 BPM (p < 0.001) for the dominant and non-dominant hands, respectively. The areas under the ROC curve were 0.929 [95%CI: (0.86-0.99)] for the dominant hand and 0.947 [95%CI: (0.88-1.0)] for the non-dominant hand. The BMP score cut-off value was 208 (sensitivity 72.7%, specificity 100%) for the dominant hand and 206 (sensitivity 87.5%, specificity 95%) for the non-dominant hand. The proposed test quantified the frequencies of rhythmic HMs in PD patients vs. controls and improved the diagnosis of bradykinesia in PD patients.