Human activity recognition (HAR) based on millimeter wave (mmWave) radar has recently attracted significant interest due to its diverse applications in intelligent robots and human-computer interaction (HCI), including the healthcare monitoring robot. 2-dimensional (2D) histogram features of radar point clouds have demonstrated high accuracy in HAR. But further expansion and refinement of this technique is needed. This paper presents a new precise non-invasive HAR framework based on radar point cloud 2D histograms. Our method enhances conventional 2D histograms by integrating fixed radar sensing boundaries into the histograms, which shows the relative spatial position changes of the target points detected by radar. Additionally, we have concatenated Doppler features (i.e., range-Doppler and angle-Doppler histograms) with the point cloud histograms, resulting in a more comprehensive feature representation than conventional point cloud histograms. We investigated the overfitting issue in stacked hybrid networks and established a multi-layer hybrid network with an optimal number of stacked layers for HAR. In the evaluation, our approach achieves state-of-the-art accuracy, with 99.72% on mmWaveRadarWalking dataset and 98.67% on CI4R-Human-Activity-Recognition dataset, respectively. The proposed method can be applied in the fields of robotics and HCI.

Blood pressure (BP) monitoring has become a daily necessity for well-being management. Camera-based BP monitoring has attracted much attention due to its comfort and convenience. It extracts photoplethysmographic (PPG) signals from the facial skin remotely and calculates morphological features for BP estimation. Although green light with strong pulsatility is commonly used, its penetration depth in skin tissues is shorter than that of the near infrared (NIR) light, indicating that the NIR light may contain more distinct PPG features (i.e., dicrotic wave) that could be more beneficial for BP estimation. In this study, we investigated the wavelength-dependency of the camera-PPG waveform for BP measurement. In particular, two morphological features (K-value and Augmentation Index) from the narrow-band green (550nm), red (660nm), and NIR (850nm) channels were measured and compared for BP calibration. Experimental data were collected from 20 healthy adult subjects using the ice water stimulation protocol. The results show that the camera-PPG waveform features have clear wavelength-dependency. The NIR channel that has deeper skin penetrability (than the green channel) but higher pulsatility (than the red channel) gives more descriptive morphological features related to BP. The conclusions drawn from this study inspire the selection of wavelength for PPG morphologv-based BP measurement.