Automatic Acute Stroke Symptom Detection and Emergency Medical Systems Alerting by Mobile Health Technologies: A Review

J Stroke Cerebrovasc Dis. 2021 Jul;30(7):105826. doi: 10.1016/j.jstrokecerebrovasdis.2021.105826. Epub 2021 Apr 28.

Abstract

Objectives: To survey recent advances in acute stroke symptom automatic detection and Emergency Medical Systems (EMS) alerting by mobile health technologies.

Materials and methods: Narrative review RESULTS: Delayed activation of EMS for stroke symptoms by patients and witnesses deprives patients of rapid access to brain-saving therapies and occurs due to public unawareness of stroke features, cognitive and motor deficits produced by the stroke itself, and sleep onset. A promising emerging approach to overcoming the inherent biologic constraints of patient capacity to self-detect and respond to stroke symptoms is continuous monitoring by mobile health technologies with wireless sensors and artificial intelligence recognition systems. This review surveys 11 sensing technologies - accelerometers, gyroscopes, magnetometers, pressure sensors, touch screen and keyboard input detectors, artificial vision, and artificial hearing; and 10 consumer device form factors in which they are increasingly implemented: smartphones, smart speakers, smart watches and fitness bands, smart speakers/voice assistants, home health robots, smart clothing, smart beds, closed circuit television, smart rings, and desktop/laptop/tablet computers.

Conclusions: The increase in computing power, wearable sensors, and mobile connectivity have ushered in an array of mobile health technologies that can transform stroke detection and EMS activation. By continuously monitoring a diverse range of biometric parameters, commercially available devices provide the technologic capability to detect cardinal language, motor, gait, and sensory signs of stroke onset. Intensified translational research to convert the promise of these technologies to validated, accurate real-world deployments are an important next priority for stroke investigation.

Keywords: Artificial intelligence; Biometric; Sensors; Smartphone; Smartwatch; Stroke symptoms; Wireless.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence
  • Clinical Decision-Making
  • Computers, Handheld*
  • Early Diagnosis
  • Emergency Medical Services*
  • Fitness Trackers
  • Humans
  • Mobile Applications*
  • Monitoring, Ambulatory / instrumentation*
  • Predictive Value of Tests
  • Prognosis
  • Smartphone
  • Stroke / diagnosis*
  • Stroke / physiopathology
  • Stroke / therapy
  • Telemedicine / instrumentation*
  • Time Factors
  • Time-to-Treatment
  • Transducers, Pressure*
  • Wearable Electronic Devices*
  • Wireless Technology / instrumentation*