Activity Recognition for Emergency Care using RFID

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

doi:10.4108/icst.bodynets.2011.247213

Cited by 14 publications

(15 citation statements)

References 12 publications

(18 reference statements)

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“…Our early work focused on the components needed to build the overall system for object use detection in the trauma bay [21][22] [23]. In contrast, this paper focuses on the details and evaluation of the complete system for object use detection in a realistic setting.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, this paper focuses on the details and evaluation of the complete system for object use detection in a realistic setting. Specifically, we started with mock resuscitations in a laboratory setting with teams of two experimenters (non-medical students) and nine objects [23]. We built a system for detecting the use of these objects based on their motion and location, which achieved an average precision rate of 63.8% and a recall rate of 90.6% [23].…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, we started with mock resuscitations in a laboratory setting with teams of two experimenters (non-medical students) and nine objects [23]. We built a system for detecting the use of these objects based on their motion and location, which achieved an average precision rate of 63.8% and a recall rate of 90.6% [23]. We then used these data to experiment with various feature sets and classifiers for detecting whether an object is in motion from passive RFID data [22].…”

Section: Related Workmentioning

confidence: 99%

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Passive RFID for Object and Use Detection during Trauma Resuscitation

Parlak

Marsic

Sarcevic

et al. 2016

IEEE Trans. on Mobile Comput.

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We evaluated passive radio-frequency identification (RFID) technology for detecting the use of objects and related activities during trauma resuscitation. Our system consisted of RFID tags and antennas, optimally placed for object detection, as well as algorithms for processing the 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. Our results also offer insights into the limitations of passive RFID and areas in which it needs to be complemented with other sensing technologies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Passive RFID for Object and Use Detection during Trauma Resuscitation

Parlak

Marsic

Sarcevic

et al. 2016

IEEE Trans. on Mobile Comput.

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“…These interactions were used to recognize various tasks in trauma resuscitation [5]. Others have aimed at recognizing activities of nurses such as walking and carrying wheelchairs, based on a combination of an infrared location system, and body movements [2].…”

Section: Related Workmentioning

confidence: 99%

“…However, it is challenging for hospital workers to establish and maintain such an awareness when they are distributed across the hospital and are not co-located [3]. Within the hospital domain, the HAR research has had a particular focus on the clinical workers' activities, including wards [4], emergency departments [5], and surgical operating rooms [6].…”

Section: Introductionmentioning

confidence: 99%

Task phase recognition for highly mobile workers in large building complexes

Stisen

Mathisen

Sørensen

et al. 2016

2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Being aware of activities of co-workers is a basic and vital mechanism for efficient work in highly distributed work settings. Thus, automatic recognition of the task phases the mobile workers are currently (or have been) in has many applications, e.g., efficient coordination of tasks by visualizing coworkers' task progress, automatic notifications based on context awareness, and record filing of task statuses and completions.This paper presents methods to sense and detect highly mobile workers' tasks phases in large building complexes. Large building complexes restrict the technologies available for sensing and recognizing the activities and task phases the workers currently perform as such technologies have to be easily deployable and maintainable at a large scale. The methods presented in this paper consist of features that utilize data from sensing systems which are common in large-scale indoor work environments, namely from a WiFi infrastructure providing coarse grained indoor positioning, from inertial sensors in the workers' mobile phones, and from a task management system yielding information about the scheduled tasks' start and end locations. The methods presented have low requirements on the accuracy of the indoor positioning, and thus come with low deployment and maintenance effort in real-world settings.We evaluated the proposed methods in a large hospital complex, where the highly mobile workers were recruited among the non-clinical workforce. The evaluation is based on manually labelled real-world data collected over 4 days of regular work life of the mobile workforce. The collected data yields 83 tasks in total involving 8 different orderlies from a major university hospital with a building area of 160, 000 m 2 . The results show that the proposed methods can distinguish accurately between the four most common task phases present in the orderlies' work routines, achieving F1-Scores of 89.2%.

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Understanding Naloxone Uptake from an Emergency Department Distribution Program Using a Low-Energy Bluetooth Real-time Location System

et al. 2020

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Introduction Emergency department (ED)-based naloxone distribution programs are a widespread harm reduction strategy. However, data describing the community penetrance of naloxone distributed from these programs are lacking. This study gauges acceptance of naloxone use and monitoring technology among people who use drugs (PWUD), and explores the use of real-time location systems (RTLS) in monitoring naloxone movements. Methods A prospective observational study was conducted on a convenience sample of individuals (N = 30) presenting to a tertiary-care academic medical center ED for an opioid-related complaint. A naloxone kit equipped with a low-energy Bluetooth (BLE) tracking system was employed to detect movement of naloxone off the hospital campus as a proxy for community penetrance, followed by a qualitative interview to gauge participant acceptance of naloxone use and monitoring technology. Results Detection of BLE signals verified transit of 24 distributed naloxone kits off our hospital campus. Three participants whose BLE signals were not captured reported taking their kits with them following discharge, suggesting technological errors occurred; another three participants were lost to follow-up. Qualitative interviews demonstrated that participants accepted ED-based naloxone distribution programs and passive tracking technologies, but revealed concerns regarding hypothetical continuous monitoring systems and problematic interactions with first responders and law enforcement personnel. Conclusions Based on acquired BLE signals, 80% of dispensed naloxone kits left the hospital campus. Use of RTLS to passively geolocate naloxone rescue kits is feasible, but detection can be adversely affected by technological errors. PWUD are amenable to transient monitoring technologies but identified barriers to implementation.

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Activity Recognition for Emergency Care using RFID

Cited by 14 publications

References 12 publications

Passive RFID for Object and Use Detection during Trauma Resuscitation

Passive RFID for Object and Use Detection during Trauma Resuscitation

Task phase recognition for highly mobile workers in large building complexes

Understanding Naloxone Uptake from an Emergency Department Distribution Program Using a Low-Energy Bluetooth Real-time Location System

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