Search

The classifier of support vector machine (SVM) learning for assessing quality of arteriovenous fistula (AVF) at hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor are presented in this work. Based on current medical standard, there are two important indices for assessing AVF quality, the blood flow volume (BFV) and the degree of stenosis (DOS). In current clinical practice, BFV and DOS of AVFs are assessed by using an ultrasound Doppler machine, which is bulky, expensive, hard-to-use and time-consuming. Therefore, a new PPG sensor module is designed to provide patients and doctors an inexpensive and small-sized solution to assess AVF quality. The readout of the sensor is successfully optimized to increase the signal to noise ratio (SNR) and reduce the environment interference, the readout circuitries are designed to fit the full dynamic range of analog-digital converter (ADC) and to filter out the noise. To assess quality of AVF, three different machine learning classifiers are developed, where the input features are selected based on optical Beer Lambert’s law and hemodynamic model. Finally, the clinical experiment results show that the proposed PPG sensor successfully achieves an accuracy of 87.838% in assessing AVF quality based on satisfactory DOS and BFV measured.

Mathematical derivation of calculating blood flow volume (BFV) at arteriovenous fistula (AVF) using a newly-developed photoplethysmography (PPG) sensor is presented in this work. Also, the readout circuit of the PPG sensor intended to increase the signal-noise ratio (S/NR) is designed and presented in this work. The designed PPG sensor equipped with derived mathematical equations shows high correlation (R2 = 0.7563) and low error (RMSE = 212 ml/min) as compared to the gold standard.

We design a low energy Bluetooth (BLE) device including reference-tag-based indoor position algorithm to solve different situations including location determining, route guideline and tracking in this study. It can not only help the visitor or patient to understand the position but also provide a system for medical staff to monitor the safety of patients and new born babies in hospital. This device is tiny enough to be embedded into visitor's wristband, baby's foot band and patient's clothes. It is composed of signal control unit, signal sensing unit and wireless connection unit. The signal control unit is responsible to control and memorize the status of the device by an ultra-low power MCU and an EEPROM memory chip. The signal sensing unit is a motion tracking sensor module to capture the accelerometer signal. The wireless connection unit combined of beacon and Bluetooth module to send data package as a BLE sender. The indoor location-based services (LBS) system is combined with smart beacon and Wi-Fi gateway. Beacon plays the role of client and Wi-Fi gateway is server. The RSSI (Received Signal Strength Indicator) of BLE signal to determine whether the beacon owner is in a particular area. However, there may be two or more Wi-Fi gateways to receive the same beacon signal in the same time, we add a reference beacon to each Wi-Fi gateway to reduce the error rate of determining beacon's location. The result of experiment shows that there is only 5% error rate for determining the position of devices.

Falls have been a major health risk that diminishes the quality of life among the elderly. In this paper, we propose a new method using acceleration data and hidden Markov model (HMM) to detect fall events. A wearable device integrating a tri-axial accelerometer was used to collect acceleration data of human chest. Feature sequences (FSs) were extracted from the acceleration data and used as sequence of observations to train an HMM of fall detection. The probability of the input FS generated by the model was calculated as the detection standard. Experimental results showed that the accuracy of the proposed method was 97.2%, the sensitivity was 91.7%, and the specificity was 100%, demonstrating desired performance of our method in detecting fall events.

The purpose of this study is to establish a therapeutic system that provided users to get physiological information and to cure myocardial ischemia. In this paper, we get the physiological signal with a smart cloth which can measure ECG signal in daily life and record the posture signal by a motion tracking sensor modules. There are four key technologies including Intelligent Body Sensor Technologies, Wireless Communication Technologies, Conductive fiber Technologies, EECP (Enhanced External Counterpulsation) Technologies. If the smart cloth detect user's ST-segment is rising or declining, we will send user a message to warn user of happening myocardial ischemia and we will provide non-invasive treatment to help user cure the myocardial ischemia early. In this paper we design a structure to achieve the goal to measure ECG signal to analyze and cure user who has myocardial ischemia.

In order to improve upon existing stimulation strategies for prosthetic vision, it is essential to assess stimulation effectiveness by measuring the responses of stimulated cells in retinal tissue. Calcium imaging has slower dynamics and lower signal-to-noise ratio than electrophysiological techniques. However, it has the ability to record responses from a large number of neurons, and is able to monitor responses from neurites too small for patch electrodes. We extended previous techniques of bulk loading RGC somata with organic calcium indicator to achieve staining of RGC dendritic processes and imaged calcium signals in retinal ganglion cell somata and dendrites simultaneously. The higher signal-to-noise ratio dendritic calcium signals make dendrites more suitable imaging regions for recording retinal ganglion cell responses to electrical stimulation protocols that are relevant to driving visual prosthetic stimulation.

A low cost maximum power point tracking (MPPT) technique is proposed in this paper for high power efficiency in photovoltaic systems to track the maximum power point (MPP). Compared to the various studies of the MPPT topologies, the proposed implementation is achieved by monitoring the power slope conditions of solar panels. The post grid-connected DC-AC inverter helps feedback the transformative power for providing AC power. Experiment results demonstrate the rapid tracking operation of MPP and a high tracking effectiveness of 97.3%.

A low cost analog MPPT technique is proposed in this paper for high power efficiency in photovoltaic systems. A wide-range current multiplier, which tracks the maximum power point (MPP) in the solar system, is implemented to detect the power slope condition of solar panels. Experiment results show the proposed technique can rapidly track the MPP with a high tracking effectiveness of 97.3%. Furthermore, the proposed system can supply a grid-connected inverter for providing AC power.

The proposed LDO regulator with a smooth peak current control (SPCC) circuit can be simultaneously controlled by an error amplifier to regulate the output voltage and the peak current controller to limit the current level. The SPCC circuit detects the information of load current and the dropout voltage to smoothly switch between the error amplifier and the peak current controller. The experimental results show that when the load current exceed the peak current level, the over current protection can make the pass device operate as a current source to prevent the over current damaging the chip. Moreover, the control mechanism can return back to error amplifier control when load current becomes smaller than the limiting current and the output voltage close to the rated value for avoiding the oscillator. The output voltage is stable and varies smaller than 15 mV when a 160 mA load current step or a 2 V supply voltage step is put on this LDO regulator.