| IP개요 |
Recent healthcare systems based on human body communication (HBC) require human interaction sensors. Due to the conductive properties of the human body, capacitive sensors are most widely known and are applied to many electronic gadgets for communication. Capacitance fluctuations due to human interaction are typically converted to voltage levels using some analog circuits, and then analog-to-digital converters (ADCs) are used to convert analog voltages into digital codes for further processing. However, signals detected by human touch naturally contain large noise, an active analog filter that consumes a lot of power is required. In addition, the inclusion of ADCs causes the system to use a huge area and power. The proposed structure adopts a digital-based moving average filter (MAF) that can effectively operate as a low-pass filter (LPF) instead of the large-area and high-power consumption analog filter. In addition, the proposed ∆C detection algorithm can distinguish between the human interaction and object interaction. As a result, two individual digital signals of touch/release and movement can be generated, and the type and strength of the touch can be effectively expressed without the help of ADC. The prototype core occupies an active area of 0.0067 mm2, consumes 7.5 uW of power, and has a conversion time of 105 ms.. |
