Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism

Paweeya Raknim, Kun Chan Lan

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Diagnosing brain disorders, such as Parkinson's disease (PD) or Alzheimer's disease, is often difficult, especially in the early stages. Moreover, it has been estimated that nearly 40% of people with PD may not be diagnosed. Traditionally, the diagnosis of neurological disorders, such as PD, often required a doctor to observe the patient over time to recognize signs of rigidity in movement. Materials and Methods: The pedestrian dead reckoning (PDR) system is a self-contained technique that has been widely used for indoor localization. In this work we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smartphone. Seventeen patients were studied over a period of 1 year. During the year it became apparent that 1 of the patients was actually developing PD. To the best of our knowledge, our work is the first attempt to use sensors in a smartphone to help identify patients in their early stages of neurological disease. Results: On average, the accuracy of our step length estimation was about 98%. Using a binary classification method-namely, support vector machine-we carried out a case study and showed that it was feasible to identify changes in the walking patterns of a PD patient with an accuracy of 94%. Conclusions: Using 1 year of gait trace data obtained from the users' phones, our work provides a first step to experimentally show the possibility of applying smartphone sensor data to provide early warnings to potential PD patients to encourage them to seek medical assistance and thus help doctors diagnose this disease earlier.

Original languageEnglish
Pages (from-to)75-81
Number of pages7
JournalTelemedicine and e-Health
Volume22
Issue number1
DOIs
Publication statusPublished - 2016 Jan

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Health Information Management

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