Introducing RFID technology in dynamic and time-critical medical settings: Requirements and challenges

Parlak, Siddika; Sarcevic, Aleksandra; Marsic, Ivan; Burd, Randall S.

doi:10.1016/j.jbi.2012.04.003

Cited by 47 publications

(44 citation statements)

References 23 publications

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“…Although the act of unwrapping can be interpreted as usage, it is swift and may be confused with noise. The use of wrapped objects can be detected by other cues, such as location (e.g., objects on patient bed are likely to be in use) or relationship information (e.g., if laryngoscope is in use, then CO 2 detector is likely in use) [13]. This paper focuses on detecting motion of nonwrapped objects, which are used for longer times, thus providing reliable motion detection.…”

Section: Application Domain: Trauma Resuscitationmentioning

confidence: 99%

“…We focused on this area because most interactions with objects occur around the patient (Table I). We assumed complete antenna coverage (see [13]) and focused on evaluating our motion detection algorithms.…”

Section: A Environmental Setting and Rfid Equipmentmentioning

confidence: 99%

“…These features must be considered both when placing the tags [13] and when developing the algorithms (this paper). Use duration for medical objects varied from very short to relatively long, with an average about 20 s ( Table II).…”

Section: B Effects Of Multiple Objects (Tags)mentioning

confidence: 99%

“…At least one tag must be attached to each object, and most objects required multiple tags [13]. The high degree of tagging led to many tags in the antenna view at any time, reduced the read rate from any single tag.…”

Section: B Effects Of Multiple Objects (Tags)mentioning

confidence: 99%

“…It is a challenging setting due to human presence and a large number and variety of objects. It requires careful placement of RFID antennas and tags [13], and the use of statistical methods for data processing (our focus here). We extract features from RSSI and classify them as moving or still using binary classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

Parlak

Marsic

2013

IEEE Trans. Instrum. Meas.

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Abstract-We studied object motion detection in an indoor environment using RFID technology. Unlike prior work, we focus on dynamic scenarios, such as emergency medical situations, subject to signal interference by people and many RFID tags. We build a realistic trauma resuscitation setting and record a dataset of around 14 000 detection instances. We find that factors affecting radio signal, such as tag motion, have different statistical fingerprints, making them discernible using statistical methods. Our method for object motion detection extracts descriptive features of the received signal strength and classifies them using machine-learning techniques. We report experimental results obtained with several statistical features and classifiers, and provide guidelines for feature and classifier selection in different environments. Experimental results show that object motion could be detected with an accuracy of 80% in complex scenarios and 90% on average. The motion type, on the other hand, could not be identified with such high accuracy using currently available passive RFID technology.

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Section: Application Domain: Trauma Resuscitationmentioning

confidence: 99%

Section: A Environmental Setting and Rfid Equipmentmentioning

confidence: 99%

Section: B Effects Of Multiple Objects (Tags)mentioning

confidence: 99%

Section: B Effects Of Multiple Objects (Tags)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

Parlak

Marsic

2013

IEEE Trans. Instrum. Meas.

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Research on the Classification Method of Refrigerator Ingredients Based on Inception Structure

Cui

Wang

et al. 2023

IEEJ Transactions Elec Engng

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With the continuous development of intelligent refrigerators, users are having higher and higher requirements for smart management of refrigerator contents. When using traditional machine learning algorithms to deal with refrigerator ingredients classification, problems such as slow recognition speed and low accuracy would often arise. At the same time, the classification accuracy is easily affected by the location of the content. Therefore, this paper uses continuous asymmetric convolutions and depthwise separable convolutions to improve the original Inception model and builds a classification prediction network based on the improved Inception model. The steps to applying the said model is as follows: First, we put the Radio Frequency Identification (RFID) tag on the food in the refrigerator, and then we collect the antenna data RSSI received by each tag through the reader. Finally, the RSSI is preprocessed and entered into the classification network to determine the location of the label. The proposed classification network has the advantages of possessing a simple structure, easy expansion of network depth, and fewer training parameters. The experimental results show that compared with other machine learning algorithms and pure convolution networks, the lightweight network model based on the improved Inception structure demonstrates great superiority. Compared with the original Inception structure, the enhanced network model is more lightweight. The number of parameters is reduced by 50%, and the amount of float-point operations is reduced by 34%. Meanwhile, the accuracy of the lightweight model has been improved by 0.6%. Thus the improved model has better prediction performance in the classification of refrigerator content.

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