Object detection and activity recognition in dynamic medical settings using RFID

Establishing context-awareness is key to develop automated decision support systems for dynamic and high-risk scenarios, where a critical component of context is the current activity. This thesis addresses the RFID-based detection of used medical objects with the ultimate goal of recognizing medical activities. We set trauma resuscitation, the initial treatment of a severely injured patient in the emergency department, as our target domain. We first describe the process of introducing RFID technology in the trauma bay. We analyzed trauma resuscitation tasks, photographs of medical tools, and videos of simulated resuscitations to gain insight into resuscitation tasks, work practices and procedures, as well as the characteristics of medical tools. Next, we propose and evaluate strategies for placing RFID tags on medical objects and for placing antennas in the environment for optimal tracking and object detection. We also discuss implications for and challenges to introducing RFID technology in other similar settings characterized by dynamic and collocated collaboration. Next we evaluate the use of RFID technology for object detection and activity recognition in a realistic setting. We tagged 81 medical objects and eight providers in a real trauma bay and recorded RFID signal strength during 32 simulated resuscitations. We extracted descriptive features and applied machine-learning techniques to monitor object use. We achieved accuracy rates of >90% when identifying the instance of a particular object type that was used during a resuscitation. Performance for detecting the usage interval of an object depended on various factors specific to the object. Our results also provide insights into the limitations of passive RFID and areas in which RFID needs to be complemented with other sensing technologies. We also investigated the usability of object motion and location cues for activity recognition by conducting motion detection and localization experiments under challenging scenarios. Using statistical methods, we were able to detect object motion with an accuracy of 80%, and predict the zone where the object is located with an accuracy of 86%.Ph. D.Includes bibliographical referencesIncludes vitaby Siddika Parlak Polatka

Activity recognition for emergency care using RFID

Copyright © 2012 ICST. We present a system that recognizes human activities during trauma resuscitation, the fast-paced and team-based initial management of injured patients in the emergency department. Most objects used in trauma resuscitation are uniquely associated with tasks. To detect object use, we employed passive radio frequency identification (RFID) for their size and cost advantages. We designed the system setup to ensure the effectiveness of passive tags in such a complex setting, which includes various objects and significant human motion. Through our studies conducted at a Level 1 trauma center, we learned that objects used in trauma resuscitation need to be tagged differently because of their size, shape, and material composition. Based on this insight, we classified the medical items into groups based on usage and other characteristics. Objects in different groups are tagged differently and their data is processed differently. We applied machine-learning algorithms to identify object-state changes and process the RFID data using algorithms specific to object groups. Our results show that RFID has significant potential for automatic detection of object usage in complex and fast-paced settings

Activity recognition for emergency care using RFID

Copyright © 2012 ICST. We present a system that recognizes human activities during trauma resuscitation, the fast-paced and team-based initial management of injured patients in the emergency department. Most objects used in trauma resuscitation are uniquely associated with tasks. To detect object use, we employed passive radio frequency identification (RFID) for their size and cost advantages. We designed the system setup to ensure the effectiveness of passive tags in such a complex setting, which includes various objects and significant human motion. Through our studies conducted at a Level 1 trauma center, we learned that objects used in trauma resuscitation need to be tagged differently because of their size, shape, and material composition. Based on this insight, we classified the medical items into groups based on usage and other characteristics. Objects in different groups are tagged differently and their data is processed differently. We applied machine-learning algorithms to identify object-state changes and process the RFID data using algorithms specific to object groups. Our results show that RFID has significant potential for automatic detection of object usage in complex and fast-paced settings

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

We describe the process of introducing RFID technology in the trauma bay of a trauma center to support fast-paced and complex teamwork during resuscitation. We analyzed trauma resuscitation tasks, photographs of medical tools, and videos of simulated resuscitations to gain insight into resuscitation tasks, work practices and procedures. Based on these data, we discuss strategies for placing RFID tags on medical tools and for placing antennas in the environment for optimal tracking and activity recognition. Results from our preliminary RFID deployment in the trauma bay show the feasibility of our approach for tracking tools and for recognizing trauma team activities. We conclude by discussing implications for and challenges to introducing RFID technology in other similar settings characterized by dynamic and collocated collaboration. © 2012 Elsevier Inc.

TITB-00456-2012.R2 1 Design and Evaluation of RFID Deployments in a Trauma Resuscitation Bay

Abstract—We examined configuring RFID equipment for best object use detection in a trauma bay. Unlike prior work on RFID, we (1) optimized the accuracy of object use detection rather than just object detection, and (2) quantitatively assessed antenna placement while addressing issues specific to tag placement likely to occur in a trauma bay. Our design started with an analysis of the environment requirements and constraints. We designed several antenna setups with different number of components (RFID tags or antennas) and their orientations. Setups were evaluated under scenarios simulating a dynamic medical setting. We used three metrics with increasing complexity and bias: read rate, RSSI distribution distance, and target application performance. Our experiments showed that antennas above the regions with high object density are most suitable for detecting object use. We explored tagging strategies for challenging objects so that sufficient readout rates are obtained for computing evaluation metrics. Among the metrics, distribution distance was correlated with target application performance, and also less biased and simpler to calculate, which made it an excellent metric for context-aware applications. We present experimental results obtained in the real trauma bay to validate our findings. P Index Terms—object use detection, RFID, setup evaluation

Passive RFID for object and use detection during trauma resuscitation

© 2016 IEEE. We evaluated passive radio-frequency identification (RFID) technology for detecting the use of objects and related activities during trauma resuscitation. Our system consists of RFID tags and antennas, optimally placed for object detection, as well as algorithms for processing RFID data to infer object use. To evaluate our approach, we tagged 81 objects in the resuscitation room and recorded RFID signal strength during 32 simulated resuscitations performed by trauma teams. We then analyzed RFID data to identify cues for recognizing resuscitation activities. Using these cues, we extracted descriptive features and applied machine-learning techniques to monitor interactions with objects. Our results show that an instance of a used object can be detected with accuracy rates greater than 90 percent in a crowded and fast-paced medical setting using off-the-shelf RFID equipment, and the time and duration of use can be identified with up to 83 percent accuracy. We conclude with insights into the limitations of passive RFID and areas in which RFID needs to be complemented with other sensing technologies