Real-Time Fall Detection Using Smartphone Accelerometers and WiFi Channel State Information

In recent years, as the population ages, falls have increasingly posed a significant threat to the health of the elderly. We propose a real-time fall detection system that integrates the inertial measurement unit (IMU) of a smartphone with optimized Wi-Fi channel state information (CSI) for secondary validation. Initially, the IMU distinguishes falls from routine daily activities with minimal computational demand. Subsequently, the CSI is employed for further assessment, which includes evaluating the individual's post-fall mobility. This methodology not only achieves high accuracy but also reduces energy consumption in the smartphone platform. An Android application developed specifically for the purpose issues an emergency alert if the user experiences a fall and is unable to move. Experimental results indicate that the CSI model, based on convolutional neural networks (CNN), achieves a detection accuracy of 99%, \revised{surpassing comparable IMU-only models, and demonstrating significant resilience in distinguishing between falls and non-fall activities.

The Field-based Model: A New Perspective on RF-based Material Sensing

This paper introduces the design and implementation of WiField, a WiFi sensing system deployed on COTS devices that can simultaneously identify multiple wavelength-level targets placed flexibly. Unlike traditional RF sensing schemes that focus on specific targets and RF links, WiField focuses on all media in the sensing area for the entire electric field. In this perspective, WiField provides a unified framework to finely characterize the diffraction, scattering, and other effects of targets at different positions, materials, and numbers on signals. The combination of targets in different positions, numbers, and sizes is just a special case. WiField proposed a scheme that utilizes phaseless data to complete the inverse mapping from electric field to material distribution, thereby achieving the simultaneous identification of multiple wavelength-level targets at any position and having the potential for deployment on a wide range of low-cost COTS devices. Our evaluation results show that it has an average identification accuracy of over 97% for 1-3 targets (5 cm * 10 cm in size) with different materials randomly placed within a 1.05 m * 1.05 m area.

Towards the limits: Sensing Capability Measurement for ISAC Through Channel Encoder

Integrated Sensing and Communication (ISAC) is gradually becoming a reality due to the significant increase in frequency and bandwidth of next-generation wireless communication technologies. Therefore it becomes crucial to evaluate the communication and sensing performance using appropriate channel models to address resource competition from each other. Existing work only models the sensing capability based on the mutual information between the channel response and the received signal, and its theoretical resolution is difficult to support the high-precision requirements of ISAC for sensing tasks, and may even affect its communication optimal. In this paper, we propose a sensing channel encoder model to measure the sensing capacity with higher resolution by discrete task mutual information. For the first time, derive upper and lower bounds on the sensing accuracy for a given channel. This model not only provides the possibility of optimizing the ISAC systems at a finer granularity and balancing communication and sensing resources, but also provides theoretical explanations for classical intuitive feelings (like more modalities more accuracy) in wireless sensing. Furthermore, we validate the effectiveness of the proposed channel model through real-case studies, including person identification, displacement detection, direction estimation, and device recognition. The evaluation results indicate a Pearson correlation coefficient exceeding 0.9 between our task mutual information and conventional experimental metrics (e.g., accuracy).