Device-free sensing is the sensing of human activities or physiological variables (e.g. heart rate) using contactless solutions. In this case, it is the analysis of reflections of radio-waves bouncing off the human body. Current device-free methods that use channel state information (currently WiFi) for monitoring activity recognition are based on specific hardware, which is often outdated. This is a challenge for the future, as it gets incredibly more difficult to find the proper resources.
Recently, a tool became available to modify the firmware of the Raspberry Pi . The question is how this new tool holds up against the already defined soft- and hardware . Therefore, your task will be to collect data from human activities using two types of state-of-the-art tools: a pre-programmed mini-PC that is known to collect excellent data and a (to-be-programmed-by-you) Raspberry Pi using NEXMON . I will supply you with the tools and hardware required, but it is up to you what data to collect (activities, physiological , animals). If necessary, you can also get access to a mmWave sensor and/or a fully-enabled sensor chair. This is a great assignment if you like tinkering with a Raspberry Pi, state-of-the-art software and human-centric research.
 Schäfer, J.; Barrsiwal, B.R.; Kokhkharova, M.; Adil, H.; Liebehenschel, J. Human Activity Recognition Using CSI Information with Nexmon. Appl. Sci.2021, 11, 8860. https://doi.org/10.3390/app11198860
 Jeroen Klein Brinke and Nirvana Meratnia. 2019. Dataset: Channel state information for different activities, participants and days. In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA'19). Association for Computing Machinery, New York, NY, USA, 61–64. DOI:https://doi.org/10.1145/3359427.3361913
20% Implementation (hardware/software), 20% Data gathering, 40% Data analysis, 20% Writing
Jeroen Klein Brinke (email@example.com)
Allow multiple students: YES