Functional near-infrared spectroscopy (fNIRS) is considered as a non-invasive and effective brain-computer interface technology. Wearable high-resolution fNIRS requires a large-scale LED array, which consumes a lot of power, and shorten the battery life. This paper proposes a spatial adaptive sampling (SAS) method that can take advantage of the spatial sparsity of fNIRS devices and greatly reduce the power consumption while maintaining high image quality. To improve the performance of the proposed SAS technique, a low power binary neural network (BNN) is proposed to accurately predict the current brain task. And the optimal dynamic LED pattern for each brain task is investigated. The proposed SAS technique is validate through an off-line experiment, it can reduce the power consumption of the LED array by 62.5% compared to not using SAS technology while maintaining a PSNR (Peak Signal to Noise Ratio) of 33 dB.

Photoplethysmography (PPG) is one of the widely used noninvasive heart rate (HR) monitoring techniques in wearable devices. Lighting of the LED dominates the power consumption of a PPG sensor. Lowering the LED lighting duration is an effectively approach to save power. This paper proposes an adaptive heartbeat locked loop (AHBLL) technique, which can dynamically adjust the dividing ratio N according to the heart rate derivative (HRD). In this way, the LED pulse duty cycle can be significantly reduced to save power. To improve the robustness of the AHBLL system, the relationship between HRD and the dividing ratio N is theoretically analyzed, which provides an optimal design guideline. To verify the proposed technique, an HR sensing circuit including an HRD detector is designed and simulated. The results show that the LED power consumption is reduced by 2.2~3.3× compared with the state-of-the-art heartbeat locked loop (HBLL) technique.