Real-Time In-Sensor Slope Level-Crossing Sampling for Key Sampling Points Selection for Wearable and IoT Devices

This article presents a slope level-crossing sampling analog-to-digital converter (ADC) that selects key sampling points for quantization in real time during sensing. It only performs quantization for the turning points in the input analog waveform and provides quantization results of the selected sampling points and timestamps between the selected sampling points. When the input analog signal is sparse, the proposed method reduces digital output data throughput. The processing unit generates a dynamic prediction of the input signal as well as an upper threshold and a lower threshold to form a tracking window. A comparator compares the input signal with the upper and lower threshold to determine if the prediction is successful. Quantization is performed only on unsuccessful predicted sampling points, which are considered key sampling points. A counter records timestamps between the unsuccessful predictions, which are the selected key sampling points. The processing unit also includes a neighbor amplitude filter and a slope filter to further reduce the number of sampling points and data throughput when the input signal is associated with high-frequency, low-amplitude noise and high-amplitude, low-frequency baseline wandering. Reconstruction of the analog signal can be achieved using linear interpolation or polynomial interpolation. The system has been implemented and tested using off-the-shelf components. The simulation and experimental results show that the proposed system can reduce the data throughput and achieve a data compression ratio of 7.1 compared with a conventional successive approximation register (SAR) ADC with a 10-bit resolution when sampling an electrocardiogram (ECG) signal.

I. Introduction

The development of advanced data acquisition systems and Internet-of-Things (IoT) technologies have been greatly expected for the next generation wearable biomedical devices and sensing systems, especially in modern human health condition monitoring applications [1], [2], [3]. For example, the diagnostics of cardiovascular disease (CVD) and cerebrovascular diseases (CeVDs) require monitoring electrocardiogram (ECG) and electroencephalogram (EEG). However, current hospitalized monitoring of ECG and EEG costs time and already limited medical resources. Moreover, short-time monitoring may not catch the symptom essential to diagnosis. Thus, long-term real-time ECG and EEG home-monitoring devices play increasingly important roles, which rely on low-power wearable sensors to record and process the analog ECG waveform. Similar applications can be found in other biomedical and IoT applications, such as monitoring brain activities, electrical power consumption, and vibration of buildings and bridges. Therefore long-term real-time data acquisition systems are expected in biomedical and IoT applications with the abilities of digitization, processing, and communication while saving data amount and computing overhead for extended battery lifetime.