Neural network-supported patient-adaptive fall prevention system

ÖZCANHAN M. H., UTKU S., Unluturk M. S.

NEURAL COMPUTING & APPLICATIONS, cilt.32, sa.13, ss.9369-9382, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 13
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00521-019-04451-y
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.9369-9382
  • Anahtar Kelimeler: Fall prevention, Medical systems, Patient safety, Wearable sensors, PHYSICAL-ACTIVITY, SENSOR, TIME, CLASSIFICATION, ACCELEROMETER, COST
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Patient falls due to unattended bed-exits are costly to patients, healthcare personnel and hospitals. Numerous researches based on up to three predetermined factors have been conducted for preventing falls. The present comprehensive proposal is based on four sub-systems that synthesize six factors. A parameter is assigned to each factor with a coefficient specifically determined for each individual patient and per admittance. The parameters are aggregated in equations that lead to an early warning about a probable bed-exit, or an alarm about an imminent bed-exit. The ultimate aim of our proposal is the generation of the earliest possible warning to grant the longest time for nurse intervention. Thus, the probable fall of high-risk patients can be prevented, by stopping the unattended bed-exits. The proposal is supported by a prototype multi-tier system design and the results of laboratory patient bed-exit scenarios, carried out using the design. Comparison of the obtained results with previous work shows that our proposed solution is unmatched in providing the longest time for nurse intervention (up to 15.7 +/- 1.1 s), because of the comprehensive six-factor synthesis, specific to each individual patient and each admittance.