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This paper presents a pixel-level predictive sampling method for image sensing and processing to reduce the computing overhead for power-limited image sensing systems. The predictive sampling method scans through rows and columns to identify the location and value of the critical pixels, which are the turning points in the row and column arrays. The prediction is performed using the value of prior pixels and a pre-defined error threshold. When the prediction is successful, the pixel is marked as a non-critical pixel and is skipped for recording and processing. Only the critical pixels are selected for further processing. We proposed reconstruction methods that recover the raw image from the selected critical pixels using interpolation. The experimental results show that the proposed method can reduce the data throughput by 72% with an error of 1.6% for sparse images. The convolutional neural network model applied with this method can achieve a similar detection accuracy in a standard method while only using 27.1% of data size. 

Sitting is the most common status of modern human beings. Some sitting postures may bring health issues. To prevent the harm from bad sitting postures, a local sitting posture recognition system is desired with low power consumption and low computing overhead. The system should also provide good user experience with accuracy and privacy. This paper reports a novel posture recognition system on an office chair that can categorize seven different health-related sitting postures. The system uses six flex sensors, an Analog to Digital Converter (ADC) board and a Machine Learning algorithm of a two-layer Artificial Neural Network (ANN) implemented on a Spartan-6 Field Programmable Gate Array (FPGA). The system achieves 97.78% accuracy with a floating-point evaluation and 97.43% accuracy with the 9-bit fixed-point implementation. The ADC control logic and the ANN are constructed with a maximum propagation delay of 8.714 ns. The dynamic power consumption is 7.35 mW when the sampling rate is 5 Sample/second with the clock frequency of 5 MHz.