Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

DistancePPG: Robust non-contact vital signs monitoring using a camera

Background 
Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established


Objective 
To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds This systematic review provides a complete summary of methods used in the literature to give a baseline for future works


Data sources 
A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases Additional articles were further obtained by references listed in the articles found Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification


Study selection 
Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated


Data extraction 
Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved


Data synthesis 
A total of 77 reports from the literature were included in this review 55 (7143%) of the studies focused on wheeze, 40 (5195%) on crackle, 9 (1169%) on stridor, 9 (1169%) on rhonchi, and 18 (2338%) on other sounds such as pleural rub, squawk, as well as the pathology Instrumentation used to collect data included microphones, stethoscopes, and accelerometers Several references obtained data from online repositories or book audio CD companions Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques Performance reported in the surveyed works were converted to accuracy measures for data synthesis


Limitations 
Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different A standard validation method has not been established, resulting in different works using different methods and performance measure definitions


Conclusion 
A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis The performance of recent studies showed a high agreement with conventional non-automatic identification This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases

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Automatic adventitious respiratory sound analysis: A systematic review.

IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art

Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook

In this paper we describe a comparative classification of Pneumonia using Convolution Neural Network. The database used was the dataset Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification made available by (Kermany, 2018) with a total of 5863 images, with 2 classes: normal and pneumonia. To evaluate the generalization capacity of the models, cross-validation of k-fold was used. The classification models proved to be efficient compared to the work of (Kermany et al., 2018) which obtained 92.8 % and the present work had an average accuracy of 95.30 %.

Classification of Images of Childhood Pneumonia using Convolutional Neural Networks.

ECG signal occurs due to heart's electrical activity and helps detect and record people's heart health. Many methods have been developed to classify ECG signal automatically. In this research, Hjorth Descriptor is used as a method for feature extraction. K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) are used as classifier in classification stage. Experiment result showed that both K-NN and MLP achieved accuracy up to 100% for 50% of test data. Results of 99.33% accuracy were obtained for 10-fold cross validation. Hence, Hjorth Descriptor generates a good feature related to ECG signal classification process.

ECG signal classification using Hjorth Descriptor

Today, the development of manipulator robot technology is growing fast and giving good impact for human life. Application of the manipulator robot technology can help many human's tasks effectively. However, most publications in this field of interest are based on complex computation, by using mathematical model from the actual robot. This paper proposes a control system for manipulator robot using Microsoft Kinect based on proportional-derivative control algorithm (PD-control). The function of Microsoft Kinect is as a sensor that detects position of Kinect user joints. The data from Microsoft Kinect will be processed by using inverse kinematics for mapping the position of user joints to the manipulator robot. After that, position controlling for manipulator robot will be processed based on PD-control algorithm in order to obtain the position of the manipulator robot in accordance with the movement of the user. The experiments have already been successfully implemented for movement of manipulator robot with some movement patterns directed by user.

Proportional derivative control based robot arm system using Microsoft Kinect

Lung sounds provide important information about the health of the lungs and airways. Lung sounds have a special and distinguishable pattern related to abnormalities that might occur in the lungs or respiration tract. Automatic lung sound recognition is directed to reduce subjectivity in assessing lung sounds. Hjorth descriptor is one method used for observing natural biological signals. Hjorth descriptor was measured to reveal biological signal's complexity. In this paper, lung sound characteristics were measured using Hjorth descriptors. Hjorth descriptor will be used to look at complexity of lung sounds. Hjorth descriptors of lung sounds are measured in the time domain and the frequency domain, and clustered using the K-means clustering method. These parameters were tested as to whether they could function as features in automatic lung sound recognition. The experimental results show that the Hjorth descriptor in the time domain is promising in order to be used as a feature for lung sound classification.

Determining lung sound characterization using Hjorth descriptor

This paper discussed one application of IOT as media data transmission for the electrocardiogram (ECG) signal. We developed ECG monitoring system that can be accessed by several users simultaneously via the internet network. The system consists of ECG hardware, transmission module based on Zigbee and web server for data storage and web application. The ECG signal taken from body's patients is acquired by the ECG machine, and then raw data is sent serially to the computer server using Zigbee. Furthermore, the data can be accessed by other authorized parties via the web pages for the purpose of treatment or consultation. By using this application, patients with heart diseases can interact and consult with a cardiologist anytime and anywhere. The testing result shows that the system can handle up to 20 users without errors. Meanwhile for 50–150 user some errors occurred due to insufficient bandwidth or high data traffic on the server.

A low-cost Internet of Things (IoT) system for multi-patient ECG's monitoring

Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.

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Achmad Rizal

Papers

ECG signal classification using Hjorth Descriptor

Proportional derivative control based robot arm system using Microsoft Kinect

Determining lung sound characterization using Hjorth descriptor

A low-cost Internet of Things (IoT) system for multi-patient ECG's monitoring

Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification