PostureTag: RFID-Enabled Human Sitting Posture Recognition Using a Tag-Embedded Chair

This paper presents a cost-effective wireless approach for identifying human postures using RFID technology. The system uses UHF RFID tags, some embedded in a chair and others integrated inside footwear, to collect information about sitting postures. The novelty lies in the detection procedure which is a simple and straightforward algorithm that calculates the root mean square error (RMSE) between any test data set and a reference one, encoded by the states of the tags. These states are represented by the normalized counting of tag electronic product code (EPC) by a reader within a specific time duration. The algorithm is made so that of all postures under test, the one having the closest data pattern (i.e. minimum error) to the reference will be selected as the correct one. The required quantity and placement of the tags are optimized by looking at a trade-off between the accuracy and complexity of the system. The proposed method in this stage can reliably differentiate between six primary sitting postures and an empty chair with up to 94-100% accuracy. It also ensures correct identification even if the user shifts position or rotates relative to the reader.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-10-03
DOI
https://doi.org/10.1109/jsen.2025.3615409
Primary Topic
School Choice and Performance
Type
article
Field-Weighted Citation Impact
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article

PostureTag: RFID-Enabled Human Sitting Posture Recognition Using a Tag-Embedded Chair

IEEE Sensors Journal
School Choice and Performance
article

PostureTag: RFID-Enabled Human Sitting Posture Recognition Using a Tag-Embedded Chair

article en

Abstract

This paper presents a cost-effective wireless approach for identifying human postures using RFID technology. The system uses UHF RFID tags, some embedded in a chair and others integrated inside footwear, to collect information about sitting postures. The novelty lies in the detection procedure which is a simple and straightforward algorithm that calculates the root mean square error (RMSE) between any test data set and a reference one, encoded by the states of the tags. These states are represented by the normalized counting of tag electronic product code (EPC) by a reader within a specific time duration. The algorithm is made so that of all postures under test, the one having the closest data pattern (i.e. minimum error) to the reference will be selected as the correct one. The required quantity and placement of the tags are optimized by looking at a trade-off between the accuracy and complexity of the system. The proposed method in this stage can reliably differentiate between six primary sitting postures and an empty chair with up to 94-100% accuracy. It also ensures correct identification even if the user shifts position or rotates relative to the reader.

IEEE Sensors JournalVol. 25(23)
University of Pisa (IT)
Openalex Percentile: Top 35%
School Choice and Performance
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PostureTag: RFID-Enabled Human Sitting Posture Recognition Using a Tag-Embedded Chair · IEEE Sensors Journal (2025) | TGRS Research Map | TGRS