New active diode with bulk regulation transistors and its application to integrated voltage rectifier circuit

This paper describes new active diode with bulk regulation transistors and its application to the integrated voltage rectifier circuit for a biological signal measurement system with smartphone. The conventional active diode with BRT has the dead region which causes leak current, and the output voltages of the application (e.g. voltage rectifier circuit) decrease. In order to overcome these problem, we propose new active diode with BRT which uses the control signal from the comparator of active diode to eliminate the dead region. Next we apply the proposed active diode with BRT to the integrated voltage rectifier circuit. The proposed active diode with BRT and voltage rectifier circuit were fabricated using 0.6 μm standard CMOS process. From experimental results, the proposed active diode with BRT eliminates the dead region perfectly, and the proposed voltage rectifier circuit generates + 2.86 V (positive side) and - 2.70 V (negative side) under the condition that the amplitude and frequency of the input sinusoidal signal are 1.5 V and 10 kHz, respectively, and the load resistance is 10 kΩ

Non-invasive RF sensing for detecting breathing abnormalities using software defined radios

The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data ( 91%) each time the trial was made. DT (17.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting

Cor/log BAN BT a wearable battery powered mHealth data logger and telemetry unit for multiple vital sign monitoring

The wireless data logger system "Cor/log® BAN BT" (CL) allows seamless 24/7 monitoring of relevant vital sign parameters. CL covers the entire period of acute point of care inside the hospital and the recovery period, when first mobility is achieved and when the patient is released into an ambulatory or homecare environment. The CL records the relevant vital signs such as ECG, respiration, pulse oximetry with plethysmogram and movement. The vital data collected with the CL data logger is saved on a memory card for further analysis and is simultaneously transmitted in real-time to a telemedicine server via a smartphone or tablet. The smartphone also provides GPS location information. In addition Cor/log View, an Android™ Application for viewing recorded vital sign data originating from the CL, was developed. CL has also a connector to the generic MedM health cloud. MedM is a generic patient data management system (PDMS) consisting of a cloud portal and a mobile health app. The app runs on Android™, iOS™ and Windows™. The app can connects wirelessly to the CL physiologic monitor and stores the vital signs in the cloud