Advancing profiling sensors with a wireless approach.

Sensors (Basel, Switzerland) · 2012 · Unclear · Insufficient evidence

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UnclearInsufficient evidencePaper 3156

Study snapshot

PublicationSensors (Basel, Switzerland) · 2012

Study typeengineering

Reported directionUnclear

Evidence ratingInsufficient

Abstract

The notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeengineering

Effect directionunclear

PopulationNot supplied

Sample sizeNot supplied

ExposureNot supplied

Evidence strengthInsufficientConfidence: 74% · Peer reviewed: yes

Main findings

The paper describes a wireless version of a near-infrared (N-IR) profiling sensor using distributed sensor nodes, base-station preprocessing/data realignment, and a back-propagation neural network classifier. The classifier reportedly achieved approximately 94% accuracy in categorizing objects as human, animal, or vehicle, and the wireless approach is stated to improve deployment options compared with a wired prototype.

Outcomes measured

Object classification accuracy (human/animal/vehicle)
Wireless profiling sensor system performance/deployment options

View raw extracted JSON

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  "outcomes": [
    "Object classification accuracy (human/animal/vehicle)",
    "Wireless profiling sensor system performance/deployment options"
  ],
  "main_findings": "The paper describes a wireless version of a near-infrared (N-IR) profiling sensor using distributed sensor nodes, base-station preprocessing/data realignment, and a back-propagation neural network classifier. The classifier reportedly achieved approximately 94% accuracy in categorizing objects as human, animal, or vehicle, and the wireless approach is stated to improve deployment options compared with a wired prototype.",
  "effect_direction": "unclear",
  "limitations": [],
  "evidence_strength": "insufficient",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "profiling sensor",
    "wireless sensor nodes",
    "near-infrared",
    "distributed data collection",
    "data aggregation",
    "base station preprocessing",
    "back-propagation neural network",
    "classification accuracy",
    "intelligent fence"
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  "suggested_hubs": []
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.3390/s121216144
PubMed: 23443371

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