Remote Physical Frailty Monitoring– The application of deep learning-based image processing in tele-health

Mohsen Zahiri, Changhong Wang, Manuel Gardea, Hung Nguyen, Mohammad Shahbazi, Amir Sharafkhaneh, Ilse Torres Ruiz, Christina K. Nguyen, Mon S. Bryant, Bijan Najafi

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment under unsupervised conditions, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72 < r < 0.99) were observed between the sensor-based FM and sensor-less FM to extract all frailty phenotypes and FI. After adjusting with age and body mass index(BMI), sensor-less FM enables distinguishing COPD group from controls (p<0.050) with the largest effect sizes observed for weakness (Cohen’s effect size d=2.41), frailty index (d=1.70), and slowness (d=1.70). These pilot findings suggest feasibility and proof of concept validity of this sensor-less FM toward remote assessment of PF in COPD patients.

Original languageEnglish (US)
JournalIEEE Access
StateAccepted/In press - 2020
Externally publishedYes


  • chronic obstructive pulmonary disease
  • deep learning
  • digital health
  • mobile health
  • Physical frailty
  • remote patient monitoring
  • telemedicine

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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