Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications.
The experiment results offer potential for promising healthcare applications using Wi-Fi passive sensing in the home to monitor daily activities, to gather health data and detect emergency situations.
no code implementations • 28 Feb 2022 • Bo Tan, Elena Simona Lohan, Bo Sun, Wenbo Wang, Taylan Yesilyurt, Christophe Morlaas, Carlos David Morales Pena, Kanaan Abdo, Fathia Ben Slama, Alexandre Simonin, Mohamed Ellejmi
This paper explores an integrated approach for improved sensing and positioning with applications in air traffic management (ATM) and in the Advanced Surface Movement Guidance and Control System (A-SMGCS).
The idea of exploiting the Wi-Fi bursts as the medium for sensing purposes, particularly for the human targets in the indoor environment, was cultivated in both radar and computer science communities and it has became a noticeable research genre with cross-disciplinary impact in security, healthcare, human-machine interaction etc. This article comparatively introduces passive radar based and channel state information (CSI) based approaches.
Under communications quality-of-service (QoS) constraints, the joint performance region of communications sum rate and radar estimation error variance is studied.
This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams.
We evaluate the proposed method on a state-of-the-art dataset and experimentally confirm that our approach outperforms the baseline method.