A Radar-Based System for Detection of Human Fall Utilizing Analog Hardware Architectures of Decision Tree Model

• Published in: IEEE open journal of circuits and systems Vol. 5; pp. 224 - 242
• Main Authors: Alimisis, Vassilis, Arnaoutoglou, Dimitrios G., Serlis, Emmanouil Anastasios, Kamperi, Argyro, Metaxas, Konstantinos, Kyriacou, George A., Sotiriadis, Paul P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog circuits; Analog hardware classifier; Classifiers; Computational modeling; Computer architecture; Continuous wave radar; decision tree; Decision trees; Fall detection; Fourier transforms; Hardware; Operators (mathematics); Portable computers; Radar; Radar antennas; Radar detection; radar-based system; sigmoid-based implementation; sub-threshold region
• ISSN: 2644-1225; 2644-1225
• DOI: 10.1109/OJCAS.2024.3407663

A fall-detection system was implemented utilizing a 2.45 GHz continuous wave radar along with power-efficient and fully-analog integrated classifier architectures. The Power Burst Curve and the effective acceleration were derived from the short time Fourier transform, and then processed by the analog classifier. The proposed classifier architectures are based on different approximations of the Decision tree classification model. The architectures consist of three main building blocks: sigmoid function circuit, analog multiplier and an argmax operator circuit. To assess the hardware design, a thorough analysis is performed, comparing it to commonly used analog classifiers while exploiting the extracted data. The architectures were trained using Python and were compared to software-based classifiers. The circuit designs were executed using TSMC's 90 nm CMOS process technology and the Cadence IC Suite was employed for tasks including design, schematic implementation, and post-layout simulations.