Fingertip forces are important biomarkers for the detection and management of various conditions, including stroke and Parkinson's disease. This paper presents TouchWave, a non-contact sensing system designed to monitor fingertip forces during activities of daily living (ADL). TouchWave leverages under-cabinet millimeter-wave (mmWave) sensors to capture both macroscopic hand movements and subtle biomechanical cues associated with fingertip force production. A novel signal processing scheme is developed to suppress noise while preserving force-related information in the mmWave signals. Additionally, a hybrid deep neural network model is proposed to estimate highfidelity fingertip forces. A comprehensive evaluation involving 21 participants demonstrates the effectiveness of TouchWave in both controlled settings and ADL scenarios.

Spatial-temporal graph data widely appear in real-world applications like traffic flow forecasting and skeleton-based action recognition. To effectively handle such data, spatial-temporal graph learning neural networks (STGNNs) have emerged as a promising solution and outperform traditional deep learning in these tasks. However, existing studies mainly focus on the performance aspect but lack a systematic study on their robustness when spatial-temporal graph structure varies. In this paper, we aim to fill this gap by systematically investigating the structure vulnerabilities of a variety of popular STGNNs in practical tasks. To this end, a spatial-temporal graph attack framework, namely Ants, which consists of gradient-sign guided sensitive time-slice selection followed by quantization-based adversarial edge searching, is proposed to craft minimized adversarial perturbations on discrete spatial-temporal graph data in both spatial and temporal dimensions. Extensive experiments on traffic flow forecasting and human action recognition tasks with six STGNNs and four datasets well demonstrate the effectiveness and stealthiness of our Ants. While prior works do not consider attacks on spatial-temporal graphs, we compare our attack with state-of-the-art attacks on graphical models extended to the STGNN setup, and the performance of Ants demonstrates that, for STGNNs, the structure vulnerability in the context of spatial and temporal dynamics needs to be considered carefully.

CCS CONCEPTS• Applied computing → Health informatics.