A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring.

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  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • Subject Terms:
      Posture*/physiology ; Algorithms* ; Sleep*/physiology; Humans ; Monitoring, Physiologic/instrumentation ; Monitoring, Physiologic/methods ; Accelerometry/instrumentation
    • Abstract:
      This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques. This was achieved based on subject (human) data at standard and adaptive levels using posture-learning processing elements (PEs). Intermittent posture evaluation was performed with respect to static and adaptive PEs. The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. An FPGA-based Hierarchical Binary Classifier (HBC) algorithm was developed to learn and evaluate sleep posture in real time. The IoT and display devices were used to communicate the monitored posture to attendant/support services. Posture learning and analysis were developed using customized, reconfigurable VLSI architectures for sensor fusion, control, and communication modules in static and adaptive scenarios. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed Board-based field-programmable gate array (FPGA) Xilinx board was used for experimental validation.
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    • Contributed Indexing:
      Keywords: FPGA; adaptive posture analysis; sensor fusion; sleep posture recognition
    • Publication Date:
      Date Created: 20241127 Date Completed: 20241127 Latest Revision: 20241130
    • Publication Date:
      20241202
    • Accession Number:
      PMC11598226
    • Accession Number:
      10.3390/s24227104
    • Accession Number:
      39598882