An online support system for stroke education &amp;#x2014; Focusing on use of the system by schoolteachers in junior high schools

Tani, S.; Nishikawa, Hiroshi; Ishiguro, Hiroshi; Todo, T.; Iwata, Mamoru; Nakao, Toshihiko; Shigehatake, Yuya; Sakamoto, Yuki; Miyashita, Fumio; Yokota, Chiaki; Minematsu, Kazuo; Nakazawa, Kimitaka

doi:10.1109/ismict.2013.6521737

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Stroke symptoms that are often experienced by sufferers are arm limp and difficult to talk [4] [5], Also, other symptoms that also often arise are problems in one or both eyeballs, loss of balance in the body, dizziness and headaches without knowing the cause in a short period [5] [6]. Symptoms like this indicate that the patient is experiencing TIA (Transient Ischemic Attack) or a mild stroke [6] [7]. Sufferers often ignore mild stroke.…”

Section: Introductionmentioning

confidence: 99%

Movements Monitoring and Falling Detection Systems for Transient Ischemic Attack Patients Using Accelerometer Based on Internet of Things

Ichwana

Arief

Puteri

et al. 2018

2018 International Conference on Information Technology Systems and Innovation (ICITSI)

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One symptom of Transient Ischemic Attack (TTA) or mild stroke is a loss of balance that makes ones easy to fall. The attacks on TIA usually occur in a short time and require immediate medical help. This study aimed to develop movements monitoring system and detector of fall for TIA sufferers using accelerometer and technology of internet of things (IoT) technology. The contribution of this study is to define ten falling movements with a decision tree algorithm. These movements are 1). Not moving, 2). Walking, 3). Standing up, 4). Sitting down, 5). Sitting, falling to the right, 6). Standing, falling towards the right, 7). Sitting-falling to the left, 8). Standing, falling to the left, 9). Sitting, falling to the front, 10). Standing, falling forward. An accelerometer is used to detect patient movement through linear and angular acceleration. The definition of movement and state of falling patients is determined by the decision tree algorithm. When a TIA patient falls, the system will send a notification to the family via the Smartphone application about the location where the patient fell. The IoT concept is applied to build this system. This test uses a test scenario of nine positions and movements of patient. Test results show that the system has detected 81.48% falls in TIA patients and can send notifications to the patient's family with a response time of 2.65 seconds.

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Section: Introductionmentioning

confidence: 99%