HLS based hardware acceleration on the zynq SoC: A case study for fall detection system

Ait Si Ali, Amine, Siupik, Marek, Amira, Abbes, Bensaali, Faycal and Casaseca-de-la-Higuera, Pablo (2014) HLS based hardware acceleration on the zynq SoC: A case study for fall detection system. In: AICCSA 2014 - 11th IEEE/ACS International Conference on Computer Systems and Applications, 10th - 13th November 2014, Doha, Qatar.

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Official URL: http://dx.doi.org/10.1109/AICCSA.2014.7073266


Fall detection is a major problem in healthcare systems, especially for elderly people who are the most vulnerable. It is important to design and implement not only an accurate fall detection system (FDS) but also a system with a real-time response. The achievement of high accuracy and fast response time together allows the development of a system that helps saving lives, time and money in healthcare industry. This paper presents the design, simulation and implementation of a novel FDS using the Shimmer wearable sensor. The discrete wavelet transform (DWT) is applied for preprocessing the data coming from the Shimmer platform, principal component analysis (PCA) is used for dimensionality reduction and feature extraction and finally, a binary decision tree (DT) is utilized for classification purpose. The system is simulated in MATLAB prior to the implementation on the Zynq system-on-chip (SoC) for hardware acceleration. DWT is executed on the processing system (PS) of the Zynq platform in a software manner while PCA and DT are both implemented on the programmable logic (PL) for hardware acceleration. PCA and DT are developed in C and synthesized in Vivado high level synthesis (HLS) tool to transform the C based designed into a register transfer level (RTL) implementation. Various optimization techniques are explored in Vivado HLS. The performance of the FDS in terms of accuracy of the classifier is 88.4% while the overall resources used in PL of the Zynq vary between 2% and 23% depending on the running frequency and optimization technique used.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: decision tree classifier, fall detection system, high level synthesis, principal component analysis, principal component analysis
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 20 Jul 2018 15:54
Last Modified: 11 Oct 2019 19:45
URI: http://nrl.northumbria.ac.uk/id/eprint/35079

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