Towards automated self-tagging in emergency health cases

Abstract

In emergency health cases such as mass casualty incidents, the death ratio is still high due to lack of an automatic and intelligent system which timely observes and reports patient criticality. Indeed, the existing criticality assessment approaches are manual such as the established Simple Triage and Rapid Treatment (START). Accordingly, it is difficult for care givers to provide optimal healthcare, in particular, if the number of casualties outnumbers the responders. A challenge is how to automatically tag a possibly large number of victims with various types of disorders immediately after an incident and before the arrival of the paramedics. Such an automated tagging would provide for more optimized emergency response.We propose an automatic self-tagging methodology using body sensor networks that deliver relevant vital signs, i.e., respiratory rate, heart rate and mental status. We present three approaches to recognize and grade the criticality level of patients. The proposed approaches are generic and can be easily adapted to different scenario such as patients in intensive care units, patients in surgery and elderlies being monitored in their home. Being fully automated, our methodology is able to provide realtime tagging with higher accuracy and fine-granularity than the simplistic manual current systems. We demonstrate the viability of our self-tagging approaches by statistically demonstrating their accuracy compared to that of experts manual tagging.

A framework for crowd-sourced data collection and context-aware services in Hajj and Umrah

“…For example, using HeartRate data several unit context can be inferred such as USER HeartRate HIGH, USER HeartRate Low, USER HeartRate NORMAL, USER HeartRate ALARMING etc. Assuming the existence of such sets of context primitives, e.g., using [16], we can infer a highlevel user context such as "user is stressed" as follows Context = User is stressed The same phenomena apply to the ritual context as well. For example, pilgrims are supposed to throw the pebbles on Jamarat, sacrifice an animal, and have their haircut between 10 th and 13 th days of Zulhijjah and only then they are allowed to remove the special cloths called Ehram 6 .…”

Medium Access Control (MAC) for Wireless Body Area Network (WBAN): Superframe structure, multiple access technique, taxonomy, and challenges

“…As stated in [65] that low threshold of a vital sign is in more life-critical conditions as compared to the high threshold value of a vital sign. Hence to resolve such challenging problem, the MAC Superframe structure needs to add an individual slot for each type of emergency data.…”

“…The table-based, aggregation and fuzzy logic based proposed approaches are used to find the criticality levels of respiratory rate (RR), heartbeat rate (HR), and mental status (MS) [65]. The RR and HR are further classified into low_critical, high_critical and normal values.…”

“…Hence, the emergency data requires a higher attention to giving the first channel access as compared to periodic and non-emergency data [70]. The emergency data are in a life-threatening situation and may suffer the patient's life if it does not allocate the first channel [65]. The researchers ought to re-design MAC and routing protocols with efficient scheduling access schemes in order to transmit emergency data without contention.…”

Improving the Emergency Services for Accident Care in Saudi Arabia

“…An automatic and intelligent system was introduced by Ullah et al [4] to observe and report the patients on time. These systems are using sensory networks to communicate the signs of heart rate, respiratory rate and mental status of patients.…”