Haptic skills are essential for effective Cardiopulmonary Resuscitation (CPR). Existing CPR training simulators provide unrealistic chest conditions. It is assumed that the CPR performance on a real human chest is the same even when the trainees have learnt on unrealistic dummy chests. To test this assumption we have developed an immersive Virtual Reality based CPR simulator in which force feedback is provided using a dummy chest with a linear spring mounted on a force plate. The user applied force is mapped to visual chest compressions modeling various springs, either linear or non-linear. Two groups of subjects were trained either with linear or non-linear chests. Both the groups were tested for their CPR performance on an unknown non-linear chest and their performance score was compared. The % of mean and standard deviation of the score (p value: 0.001) for the group with the non-linear chest is 91.66% and 1.70% and that with linear chest is 86.39% and 3.44% respectively. This suggests that the group that were trained on non-linear chest performed better. The experiments clearly demonstrates that visual feedback could influence haptics perception of chest compliance and the VR-CPR system could simulate various linear and non-linear chest compliance controlling visual feedback alone.

Affordable hi-fidelity VR based CPR simulator with haptics feedback

The hypothesis of this study is to verify the sensory dominance with the combinations of three sensory modalities (Audio-Haptics (AH), Visual-Haptics (VH), Audio-Visual-Haptics (AVH)) using Virtual Reality (VR) based Cardiopulmonary Resuscitation (CPR) simulator. To test this hypothesis three experiments with three different groups of participants were conducted with the above three modes of combinations. Finally, three groups were tested for their CPR performance on an unknown linear chest stiffness of mannequin-based CPR simulator and their performance score was compared. The % mean and standard deviation of the performance score (p-value: 0.00006) in the testing phase for group A-AH, B-VH, and C-AVH is $77.95\%\pm 8.27\%, 89.47\%\pm 6.19\%, \ \text{and} \ 91.73\%\pm 3.14\%$ respectively. The results show that the group who trained with the three sensory modalities have better performance than that of the other two groups. Our future work is to incorporate rescue breathing in the CPR training simulator for better skill training.

The Effect of Audio and Visual Modality Based CPR Skill Training with Haptics Feedback in VR

The semiconductor technology for a certain function, such as instrumentation amplifier ICs is currently developing very fast, commercially available and can be obtained easily on the market. An instrumentation amplifier is a very important part in the process of data acquisition, especially for biopotential signals from the human body because its amplitude is very small and susceptible to noise interference. The selection of the proper amplifier instrumentation will produce an accurate biopotential signal reading. This paper explains the use of 2 types of instrumentation amplifiers, namely AD620 and ADAS1000, which are used in the design and realization of 12-channel ECG (Electrocardiograph). The performance and noise resistance of the two instrumentation amplifiers are compared and analyzed so that an appropriate instrumentation amplifier can be determined especially in the case of 12-channel ECG applications. 12-channel ECG was chosen because of the complexity of the design and can provide more detailed information as well as to detect the abnormalities heart’s functions. The results shows that 12-channel ECG using AD620 instrumentation amplifier has an SNR value below 12.04 dB, while using the ADAS1000 instrumentation amplifier has an SNR value below 35.5 dB and it is more resistant to noise interference.

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Comparative SNR Analysis Between Instrument ADAS1000 and AD620

Cardiovascular diseases (CVD) or abnormalities are the leading causes of mortality in today’s era throughout the world. A human life can be saved on proper time, if they are diagnosed with diseases at the earliest. The major popular modalities in monitoring the electrical activity of the heart using the Electrocardiogram (ECG), the Phonocardiogram (PCG), and the Photoplethysmogram (PPG). Electrocardiogram (ECG) readings with sophisticated signal processing tools hinders because of non-portability. Whereas, PPG has become increasingly common due to its low cost, wireless capabilities, and relatively small size and optical nature. Including normal patient, Cardiac dis-order such as hypertension, ischemic heart disease, myocardial infarction, among others, disrupts the body’s volumetric blood flow rate. The work proposes a revolutionary strategy to utilize the IoT-based platform to download and access the vast amount of bio signals data effectively using data-compression technique to save time and cost without compromising accuracy. The solution is the implementation of Gaussian and Fourier models of the PPG datasets and, after that, compression of each model parameter. The enormous data set of various cardiac patients contains some vital information for which the implementation of such a robust data compression tool or data compression mechanism is necessary. So, the information can be accessed from anywhere in the world with a high-speed internet connection and with the specific settings for (Unique Patient ID) for PPG monitoring. 

Data Compression of Photoplethysmogram Signal for IoT Application

Cardiovascular diseases (CVD) or abnormalities are the leading causes of mortality in today’s era throughout the world. A human life can be saved on proper time, if they are diagnosed with diseases at the earliest. The major popular modalities in monitoring the electrical activity of the heart using the Electrocardiogram (ECG), the Phonocardiogram (PCG), and the Photoplethysmogram (PPG). Electrocardiogram (ECG) readings with sophisticated signal processing tools hinders because of non-portability. Whereas, PPG has become increasingly common due to its low cost, wireless capabilities, and relatively small size and optical nature. Including normal patient, Cardiac dis-order such as hypertension, ischemic heart disease, myocardial infarction, among others, disrupts the body’s volumetric blood flow rate. The work proposes a revolutionary strategy to utilize the IoT-based platform to download and access the vast amount of bio signals data effectively using data-compression technique to save time and cost without compromising accuracy. The solution is the implementation of Gaussian and Fourier models of the PPG datasets and, after that, compression of each model parameter. The enormous data set of various cardiac patients contains some vital information for which the implementation of such a robust data compression tool or data compression mechanism is necessary. So, the information can be accessed from anywhere in the world with a high-speed internet connection and with the specific settings for (Unique Patient ID) for PPG monitoring.

Cardiovascular diseases have reached epidemic proportions in developing countries, and acute Myocardial Infraction is the most common manifestation of this disease. A high fidelity mannequin is being built towards training medical personnel in diagnosing MI. This paper describes the simulation of a 12 lead ECG, the primary tool for recognizing type and stage of MI, in the mannequin and its incorporation into the hemodynamic model, ensuring realistic variations in beat-to-beat timing. The module also verifies correct electrode placement for ECG.

ECG simulation for Myocardial Infarction diagnosis in high fidelity mannequins

Cardio Pulmonary Resuscitation (CPR), especially chest compression, should be performed promptly on victims of cardiac arrest for improved morbidity. CPR is performed in 2 steps, chest compression and chest ventilation. But, commercially available CPR mannequins do not accurately mimic the viscoelastic properties of the human chest, resulting in poor CPR performance. We propose a CPR mannequin which exhibits a chest with increasing stiffness as the depth of compression is increased; the increased bio-fidelity of this mannequin should improve CPR training efficacy. This mannequin also measures depth and rate of compression, the air volume during chest ventilation, and compares the measured values against acceptance criteria that can be tuned to different standards.

K. Kanakapriya

Papers

ECG simulation for Myocardial Infarction diagnosis in high fidelity mannequins

CPR Module with Variable Chest Stiffness in High Fidelity Mannequins