A pervasive assessment of motor function: A lightweight grip strength tracking system

Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Jack Mortazavi, Majid Sarrafzadeh

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

With the growing cost associated with the diagnosis and treatment of chronic neuro-degenerative diseases, the design and development of portable monitoring systems becomes essential. Such portable systems will allow for early diagnosis of motor function ability and provide new insight into the physical characteristics of ailment condition. This paper introduces a highly mobile and inexpensive monitoring system to quantify upper-limb performance for patients with movement disorders. With respect to the data analysis, we first present an approach to quantify general motor performance using the introduced sensing hardware. Next, we propose an ailment-based analysis which employs a significant-feature identification algorithm to perform cross-patient data analysis and classification. The efficacy of the proposed framework is demonstrated using real data collected through a clinical trial. The results show that the system can be utilized as a preliminary diagnostic tool to inspect the level of hand-movement performance. The ailment-based analysis performs an intergroup comparison of physiological signals for cerebral vascular accident (CVA) patients, chronic inflammatory demyelinating polyneuropathy (CIDP) patients, and healthy individuals. The system can classify each patient group with an accuracy of up to 95.00% and 91.42% for CVA and CIDP, respectively.

Original languageEnglish (US)
Article number6515611
Pages (from-to)1023-1030
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume17
Issue number6
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Ailment classification
  • Grip strength tracking
  • Movement disorders
  • Pervasive medical system
  • Upper limb deficits

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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