Katsumi Abe

16.2: A Parallel Digital‐Data‐Driver Architecture for Low‐Power Poly‐Si TFT‐LCDs

A parallel digital-data-driver architecture has been developed to reduce power consumption in low-temperature poly-Si TFT-LCDs. It features low-power 3V-interface level shifters and 198 serial/parallel converters driven in parallel at a clock frequency of 62.5 kHz. Total power consumption in a 2.4-in., 41K(176 × 234)-pixel TFT-LCD with integrated 6b DACs is 12 mW at a 30-Hz frame frequency.

An integrated poly-Si TFT current data driver with a data-line pre-charge function

We have developed an integrated poly-Si TFT current data driver with a data-line pre-charge function for active-matrix organic light-emitting diode (AMOLED) displays. The current data driver is capable of outputting highly accurate (±0.8%) current determined by 6-bit digital input data. A novel current-programming approach employing a data-line pre-charge function helps achieve accurate current programming at low brightness. A 1.9-in. 120 x 136-pixel AMOLED display using these circuits was demonstrated.

Wearable and low-stress ambulatory blood pressure monitoring technology for hypertension diagnosis

We propose a highly wearable, upper-arm type, oscillometric-based blood pressure monitoring technology with low-stress. The low-stress is realized by new developments in the hardware and software design. In the hardware design, conventional armband; cuff, is almost halved in volume thanks to a flexible plastic core and a liquid bag which enhances the fitness and pressure uniformity over the arm. Reduced air bag volume enables smaller motor pump size and battery leading to a thinner, more compact and more wearable unified device. In the software design, a new prediction algorithm enabled to apply less stress (and less pain) on arm of the patient. Proof-of-concept experiments on volunteers show a high accuracy on both technologies. This paper mainly introduces hardware developments. The system is promising for less-painful and less-stressful 24-hour blood pressure monitoring in hypertension managements and related healthcare solutions.

Detection of Drug Administration Behavior with Swallowing Sounds

In recent years, chronic diseases have become the main causes of death around the world, and medication non-adherence among patients with chronic diseases is a common problem. A system for detecting drug administration behavior in daily life is strongly required. Currently, there is not a system for detecting this behavior by using wearable sensors. In this paper, we propose a wearable sensing method for detecting drug administration behavior in daily life by using swallowing sound, which is available and suitable for daily monitoring. To recognize the behavior from swallowing activities, a classification methodology using wavelet based features as feature vectors and artificial neural network as classifier is proposed. A high classification accuracy of 85.4% was achieved in classifying two swallowing activities of drinking water and taking a capsule with water. Furthermore, we also propose a compensation method for time-dependent change based on the frequency characteristics of swallowing sound.